At Benmore, our job isn’t to blindly build feature lists.

It’s to apply judgment to help founders cut through noise, define what actually matters, and get to market without wasting time, capital, or momentum.

That’s exactly what this client interview illustrates.

In our latest conversation, I sat down with Garrett Campbell, Co-Founder & CEO of Objectionly, to break down how he went from running a sales recruiting business to building an AI platform that helps teams coach better, close more deals, and scale — without burning out managers.

As Garrett put it when reflecting on our work together:

That clarity-first approach is what allowed Objectionly to move quickly, launch with confidence, and raise from a position of leverage.

So what is Objectionly actually building?

At its core, the platform helps sales leaders actually understand why deals are won or lost, without spending 20+ hours a week reviewing calls.

In the interview, we unpack:

• The real reason top sales teams outperform others (and why it has nothing to do with hiring better reps)

• How Garrett validated demand before writing a line of code

• Why starting with a brutally simple MVP mattered more than flashy features

• What it took to go from prototype → paying customers → raising at a $10M valuation

• Garrett’s experience building Objectionly as a non-technical founder

This is a clear look at what disciplined execution actually looks lik

The Big Mover: AI and Agentic Coding Tools.

Late last year, AI-coding tools like Cursor, Github Co-Pilot, and Devin were introduced into software development - and many developers started talking about AI-driven development. Then, in February, Claude Code was launched - and many developers' worlds broke. The impact of these tools in software development was pretty much explosive - and the term was coined "vibe-coding".

To many developers, coding went from a generally mentally taxing and meticulous process to basically gambling on if the AI tool of choice will generate the correct output or not.

However, the impact was undeniable, and companies across the world pushed their development teams to utilize these tools. Code was getting written at an unprecedented rate - features flying off of the shelves, rapidly built UIs, entire codebases getting rewritten in a matter of days. Amazing… right?

Essentially yes - at least in our opinion - with many caveats.

AI Coding Observations

First and foremost, AI (specifically LLMs) are prediction machines. This means that fundamentally, what they spit out - a novel, code, research, etc. - is variable and, to some extent, a game of optimizing chance. Additionally, due to their variable and unpredictable nature, layers of auditing and quality control become increasingly more important.

Article on how ChatGPT works if you’re interested in understanding the math behind these tools:

What Is ChatGPT Doing … and Why Does It Work?

Stephen Wolfram explores the broader picture of what's going on inside ChatGPT and why it produces meaningful text. Discusses models, training neural nets, embeddings, tokens, transformers, language syntax.

writings.stephenwolfram.com/2023/02/what-is-chatgpt-doing-and-why-does-it-work

Context management (what information the AI Agent is given or can refer back to) has also emerged as an incredibly important factor in using these tools. Unfortunately, you can’t feed a 20 million line code base into an AI tool and it just understands everything. When determining what information to give these agents, being intentional and selective in the context is extremely important.

Knowing when to stop the agent is also important. For example, you have 10-20% of your session usage left, it’s probably not a good idea to start work on another massive feature, but instead, to start a new session and feed the AI tool a new set of information and context to orient itself more effectively.

The specific coding language that is being used, in our opinion, is still important, but is losing importance rapidly. However, the guardrails that are being set in said language are increasing in importance. Specifically, understanding that these AI tools are essentially rogue geniuses, you want to let them loose, but giving them access to a helicopter is probably not a good idea.

So, coding languages with many out-of-the-box security features and great design library integrations (prebuilt UI/design code - rather than coding design from the ground up) are more important - because they have more guardrails (and less variability in output). The same goes for your deployment infrastructure: safer, less variable deployment stacks hedge on less customizability but faster, less variable deployment.

We specifically use Django on our backends, and Next.js or React Native for our frontends because of the above reasons. Associated articles below:

Django for AI: How the Classic Web Framework Is Quietly Powering the Next Generation of Intelligent…

For years, Django has been known as the framework for “perfectionists with deadlines.” Now, in 2025, it’s also becoming the framework for…

medium.com/@joyichiro/django-for-ai-how-the-classic-web-framework-is-quietly-powering-the-next-generation-of-intelligent-e3216580442b

Why Next.js is the Perfect Platform for AI-Related Apps

softescu.com/en/blog/why-nextjs-perfect-platform-ai-related-apps

Lastly, the coding agents are noticeably better on greenfield projects (projects starting from nothing) rather then picking up work on existing code bases.

Orchestrator Talent and Planning

The above observations in relation to coding are all downstream from two key components that we are seeing skyrocket in importance → orchestrator talent (the competency, skill, knowledge, of the person using the AI agent), and project planning and documentation.

These AI coding tools are emerging as great direction-takers, but they are just going to do whatever you ask. If you ask for a nearly impossible feature, as of right now, there’s little to no push back from the AI agent - it’s just going to try to do it.

If you aren’t meticulous in planning your project out or knowing when something is not feasible, you’ll get stuck in the infamous gas-light loop of the AI agents. Forever being told that a feature is done (Great! I’ve completed <impossible feature> you can now view it from this link!) when it’s just not - because it wasn’t possible in the first place.

So, properly planning out a project and having someone who is knowledgable leading your project is now more important then ever.

The analogy that we like to use is imagine that you could build a physical real-world house by prompting ChatGPT. Yea, your house will be built quickly, but is it compliant with zoning laws? Is it safe? Do you have 30 extra rooms that you don’t need but now need to pump AC and electricity into? Is the balcony facing the right direction? etc.

The difference is as follows →

You prompting ChatGPT to build a house

> Build me a house that is red, looks like the Home Alone house, can store my 3 cars, and will last 1000 years

A builder prompting ChatGPT (We have no experience in construction, some of the terminology and processes may be incorrect):

> We have a construction project at parcel #XXXXXX, here are the applicable zoning laws and HOA specs <specs>. 

Here is the list of materials that we have sourced and their pricing <list> here are our vendors and vendor schedules <vendors>. 

Here is the budget for the project <budget>. Note that in about 3 months we're expecting a heavy winter so construction will have to accommodate for below-freezing temps. 

Let's start with laying the foundation of the house. Please use the Home Alone house's blueprint (viewable here <homealone_blueprint.pdf>) as a reference for what the foundation of the home should be - and follow it as closely as possible given the guidelines of the project. 

Note that the garage foundation in the given blueprint will need to be modified to accommodate a three car garage, in order to stay in spec, please expand the square footage of the home by 15% in order to make room for the additional space - while maintaining the original dimensions of the home.

Once I approve the foundation, we'll move into the framing of the home.

The point is that, at the current moment, these coding tools seem to be most effective by allowing existing experts to leverage their knowledge and implementation efforts. Knowing what to build and how to build it are quickly establishing themselves as the most important aspects of software development.

Benmore company-wide Claude Code analytics for November 2025.

Where we think it’s headed

We think that the implementation of software will continue to hurtle toward net zero labor input. Software can, in the next decade or so, essentially become a commodity. Need a home security app that works with your ring cameras? Call up your local software development provider. Need an app that makes the drivers of your trucking company log their time and integrates with your custom accounting software to track labor expenses? Call up your local software development provider.

We’re already seeing this emerge with many of our clients. Why pay $100 per user per month for a pre-built SaaS that solves 50% of your problem when you could build a software that you own, don’t have to pay extra for as you hire more people, and uniquely completely solves your problem?

It’s coined as the “build or buy” choice and we expect this discussion to start to lean towards building more and more in the coming years. Anecdotally, a team member of ours has mentioned that in China, many companies have chosen to build over buy, even before AI, because of the lower cost of hiring software developers. If this is true, then the model of building over buying has been proven to work outside of the US and would indicate that a similar movement would take place here in the US as AI has made the cost of developing software less expensive.

In the startup ecosystem, a similar phenomenon may take place. A good amount of VC investment has been predicated around the cost to hire a team - now we are seeing a lot of VC money going to fund distribution and marketing. That being said, accessibility to private capital and business loans has exploded recently, potentially allowing founders to hold onto more equity in the earlier stages of growing a startup.

How Benmore is positioned going into 2026

Our company has focused on a couple areas:

Talent acquisition, proprietary tools for our talent to leverage, and engineering-first new-age consulting structure.

Talent

With regard to talent, we noticed that there is massive opportunity when it comes to hiring new grads. Many incumbent software companies have significantly reduced new-grad position openings or closed them completely. It's extremely interesting that in an era when technology valuations are booming, software jobs are diminishing.

However, our view is that new-grads are the most positioned to adopt and become experts in the new era of software development. If AI is as revolutionary as we think, you could make the argument that everyone is starting at an even playing field - or that the original playing field has been flattened significantly. Who better to rapidly adopt, up skill, and become experts in AI-driven development then new-grads?

This is our position and we have made a concentrated effort to attract and retain the most talented new-grads that we can find. Our forward-deployed developers hail from or have experience working at the top computer science universities, Harvard, University of Illinois, Northwestern, and Boston University. Additionally, a key skill that we look for in our hiring process is the usage of AI-coding tools and learning ability.

Proprietary Technology

When it comes to our proprietary technology, taking our earlier claim that AI is a tool to be leveraged, we have built out an internal suite of tools specifically to aid our team. These tools are built to optimize usage of existing tools. For example, we have a deliverable of type “dynamic asset” in our tool suite. It’s a snippet of custom code that is, well, dynamic. It can be a presentation, a design guide, a meeting recap, a roadmap, a spreadsheet etc. Functionally, this allows us to avoid locking into Powerpoint or Excel etc. and instead we can quickly create tailored deliverables at an unprecedented speed.

Another example is our context bank. Throughout the project, all documentation, roadmaps, meetings, etc. are agnostically fed into the coding agents that we utilize. This allows us to consolidate project knowledge into a centralized place that anyone on our team can refer to and AI tools can natively pull from.

We continue to contribute to our internal technology daily, however, the ethos of the initiative is to build things that our team can leverage, not replace them.

Modern Consulting Structure

Lastly, we took a look at the traditional consulting structure that has been solidified over the past couple of decades and, drawing inspiration from new consulting players in the space, essentially flipped the playbook.

Traditional consulting is billed on hours and team members. You would have giants like Accenture staffing 10+ people to a project. Included would be a project manager/lead, two designers, six developers, and a dev ops engineer. Each logging hours, each getting billed for.

However, this never made that much sense to us… aren’t the incentives fundamentally misaligned? It’s in the consulting company’s best interest to assign as many people as possible to a project for as long as possible… right?

Now, with AI, this model is even more backwards. If you have one extremely talented developer who knows how to leverage AI, they could realistically do the work of five people, retain all of the project knowledge themselves rather than having to explain it to an entire team, and concurrently work on two or three other projects while long running AI tasks are being executed (orchestrating AI). So… how do you even bill for an hour of their time?

This is actually a hot topic in consulting at the moment as the entire consulting pricing model is being flipped backwards. We currently are iterating on our pricing model, however, we do know that we should, at the very least, bill on outcomes - this aligns incentives (payment is tied to project completion, so it’s in everyone’s best interest to finish the project.)

Relating back to that AI-enabled developer that we were talking about earlier, that’s incorporated into our process as well. They’re called Forward Deployed Developers and are trained to be proficient in project management, client-facing communication, and, of course, software development. Weaponized with AI, you can flatten project team hierarchies. This results in less communication bottlenecks, more project ownership, and faster implementation… all boiling down to a best-in-class service being delivered faster and at better prices.

Final Thoughts

We’ll end this with a quick anecdote. A couple of weeks ago we got a chance to meet a former director of a large incumbent consulting company and we asked them one quick question: “Did you ever provide your services to SMBs or startups?”. They chuckled a bit and said “hell no”. They didn’t go much into why… but the prevailing rationale is that SMBs and startups can’t afford a three year long, five million dollar project. They can’t light money on fire with unclear deliverables or direction, and they can’t afford to engage in risky incentives because their boss told them too. They need actual value from an engagement. This is the problem that Benmore solves, democratizing consulting by providing best-in-class software consulting and implementation from underutilized talent by leveraging industry changing technology.

In our latest episode of The Benmore Brief Podcase, we sat down with client Jerome Dotson Jr., founder of Musicade, a mobile application designed to bridge the gap between fast-rising artists, DJs, event organizers, venues, and fans all on one platform.

Here’s how we helped make Musicade a reality:

Visibility for Upcoming Artists: We built discovery tools that give new talent a direct path to connect with fans and event organizers.

End-to-End Event Management: From creating and promoting events to ticket sales and secure refunds, Musicade handles it all seamlessly.

Community-Driven Engagement: Fans and professionals can message, like, and react, building relationships inside the app.

Location-Centric Discovery: Users can discover trending venues, organizers, and entertainment professionals in their city.

From Manual to Scalable: What was once coordinated offline is now a fully functional cross-platform mobile app built with Flutter, Django, and PostgreSQL.

The result is a platform that is already transforming how the entertainment industry connects and laying a strong foundation for future growth including live streaming and in-app music.

Watch our full conversation with Jerome here:

Click below to explore the full case study:
Read the Case Study →

React Native vs. Flutter vs. Native: Choosing the Right Path for Your Mobile App

Cross-platform development has opened the door for founders to get apps to market faster and at lower cost. The choice between frameworks like React Native, Flutter, full native development, or progressive web apps (PWAs) depends on your product goals, budget, and the degree of native functionality required.

First Things First: What Are We Even Talking About?

When people hear “mobile app development,” it can sound like a wall of technical jargon. At its core, though, you are choosing what kind of toolbox you will use to build your app. Different toolboxes come with different tradeoffs.

Think of it like building a house:

• Native development is like hiring separate expert teams to build two identical houses side by side, one with American materials and one with European materials. Each will be perfectly tailored to its environment, but you are paying for two crews, two sets of materials, and twice the time.

• React Native and Flutter are like using prefabricated modules that fit both American and European standards. You build one house, then adjust a few details for each location. It saves time and money, but some specialized features might not be available without custom work.

• Progressive Web Apps (PWAs) are like setting up a high-end tent or prefab cabin. You can move in quickly and it will work fine for many situations, but if you want heated floors or built-in solar panels, you will run into limits. Wrappers (tools that package a website as an app) are like taking that prefab cabin and dressing it up so it looks more like a traditional home.

All of these approaches get you a roof over your head, but the right one depends on your budget, your timeline, and whether you need things like custom stonework or if a simple, comfortable cabin will do.

Native Development: Maximum Control, Maximum Cost

Native development uses the operating system’s primary language, such as Kotlin or Java for Android and Swift or Objective-C for iOS.

Pros

• Direct access to all hardware and OS features

• Best performance with no abstraction layer

• Suitable for complex or resource-intensive features such as AR, 3D gaming, or advanced Bluetooth

Cons

• Two entirely separate codebases to maintain

• Higher upfront costs and longer timelines

• Requires hiring or contracting two specialized teams

Native works best when performance, responsiveness, or deep system integration is essential and when resources are available to support both ecosystems.

React Native: JavaScript First

React Native, created by Meta, lets developers write most of an app in JavaScript while still accessing native APIs through “bridges.”

Pros

• Single codebase for iOS and Android

• Large ecosystem of libraries and community support

• Familiar for teams already skilled in JavaScript or React

Cons

• Bridging can slow down apps that rely heavily on native features

• Newer OS features may require third-party modules

• Graphics-heavy apps may not run as smoothly as native

React Native suits teams that want speed to market, can leverage JavaScript expertise, and need a balanced approach between cost and capability.

Flutter: Google’s All-In-One Toolkit

Flutter uses Google’s Dart language and compiles directly into native ARM code. Instead of bridging, it renders its own widgets and UI.

Pros

• Near-native performance even for complex UIs

• Consistent look across platforms without relying on system components

• Strong momentum in Google’s ecosystem with support for desktop and web

Cons

• Smaller developer pool compared to React Native

• Larger app size because of the built-in rendering engine

• Dart has a steeper learning curve for teams unfamiliar with it

Flutter is a good option when design consistency matters and when performance needs are high but maintaining two native codebases is not practical.

Progressive Web Apps and Wrappers

Progressive Web Apps act like websites with app-like behavior and can be “wrapped” with tools like Capacitor.js or Cordova to distribute through app stores.

Pros

• Single codebase across web and mobile

• Lower cost and faster release

• Well-suited for content-driven products, marketplaces, or SaaS dashboards

Cons

• Limited offline capabilities

• Restricted access to advanced device features such as push notifications or sensors

• App store acceptance can be inconsistent

PWAs are best for products focused on content or form-driven interaction rather than deep hardware integration.

How to Decide

• Go Native for apps tied closely to hardware or that require flawless performance, such as medical devices or AR/VR.

• Choose Flutter if you need polished UI, consistent performance across platforms, and you are comfortable with Dart.

• Pick React Native if you want faster development with JavaScript talent and a middle ground on cost and capability.

• Start with a PWA if your product is web-first, you want to test the market, or you do not need advanced device functionality.

Final Word

Technology frameworks should serve the business model, budget, and long-term goals. For early validation or MVPs, PWAs or React Native can get you moving quickly. For scaling into a polished product, Flutter or full native development may provide the stability and depth required.

The AI Agent Boom: Dot-Com 2.0 for White-Collar Work

In the dot-com era, services that were once tied to legacy software, custom builds, or physical infrastructure became scalable online. Marketplaces like Amazon replaced brick-and-mortar, Salesforce digitized CRM, and communities that lived on message boards scaled into global networks. The internet democratized access to value creation.

We are now entering a similar moment with AI agents. Only this time, the shift is unfolding inside white-collar expertise.

Why AI Agents Resemble the Dot-Com Boom

AI agents go far beyond chatbots. They are autonomous workflows that package domain knowledge into repeatable systems. Imagine turning the skillset of a lawyer, analyst, or consultant into software that runs around the clock and scales at almost zero cost.

In conversations with founders like Lawrence K., a Marine veteran building an agentic platform of his own, the pattern is clear. He spent two decades inside the veterans’ disability claims process and saw firsthand how confusing, costly, and time-consuming it is for vets to secure what they are owed. His concept reimagines the process as a guided, AI-driven experience that removes lawyers taking 20–30% of back pay and replaces multi-hour office visits with a voice-driven system that vets can use from home.

This is exactly what happened in the SaaS boom: domain experts and technologists identified inefficient services and moved them into scalable platforms. The only difference is that today, the “platform” is an AI agent.

The Market is Confirming the Shift

• Robert F. Smith, CEO of Vista Equity Partners, recently described agentic AI as “like electricity”, a shift that will transform enterprise software. Vista has already built an “Agentic AI Factory” to deploy agents across its portfolio (Economist Impact).

• Morgan Stanley projects AI could add $13 to $16 trillion in value to the S&P 500, with nearly half of that growth tied to autonomous, agent-driven systems (Business Insider).

• Dario Amodei, CEO of Anthropic, has warned that AI may eliminate up to 50% of entry-level white-collar jobs in the next five years, with unemployment potentially rising to 10–20% (Axios, New York Post).

• The Economist calls this the “AI glass floor”. Entry-level roles may not vanish overnight, but barriers to entering classic professional careers are rising as agents absorb the very work those roles were designed for (Economist Impact).

The signal is clear. The same way dot-com reshaped distribution, agentic AI is reshaping delivery of expertise.

Why Ground-Level Builders Are Best Positioned

In our earlier piece, “Why AI and Modern Development Are Creating Opportunities VCs Missed”, we explained how modern tools have changed the economics of software development. Historically, building for a $50M market was irrational because development costs started at $200K–$500K. That model only worked for billion-dollar markets and VC funding.

Today, with AI-accelerated coding and modular infrastructure, development costs have fallen by 90%. A $20K build can now profitably serve a $50M market. For a founder with domain expertise, that math changes everything.

This is why ground-level builders with niche knowledge are in the strongest position. They know the workflows, they feel the inefficiencies, and they can now package that knowledge into products that were previously impossible to deliver.

What is Driving the AI Agent Boom

Economic Scale
Once deployed, agents operate at zero marginal cost per additional user. This mirrors the economics of early SaaS, where recurring revenue could be scaled without adding proportional headcount.

Productivity Shift
McKinsey estimates generative AI could add $2.6 to $4.4 trillion annually across business functions such as software, sales, R&D, and customer operations.

Structural Displacement
Entry-level roles in law, consulting, and finance are already seeing tasks like drafting, analysis, and filing move to AI. The Economist notes that the career ladder itself is being eroded as junior pathways vanish.

Urgency and Speed
First-mover advantage matters. Vista is already deploying agent factories. Startups are launching agents and facing copycats within weeks. The race is not theoretical, it is happening in real time.

What This Means for Founders and Experts

If you are a domain expert in a niche field, this is the moment to act. The opportunity is not reserved for large tech firms. It is open to anyone who can translate specialized knowledge into workflows that are efficient, emotionally intelligent, and user-centric.

Lawrence’s Vet Claim DIY illustrates this well. The system is not just a form filler. It is designed around veterans’ needs, using voice interaction, logic specific to military service, and empathetic UX. That combination of expertise, workflow, and design is what makes an AI agent valuable.

Why We Are Bullish on Ground-Level Entrepreneurs

What’s Next

For founders: Identify the workflows in your industry that can be codified as an agent. These often hide in repetitive, high-friction processes.

For domain experts: Partner with builders who understand how to package your expertise into workflows with empathy and usability.

For investors and operators: Recognize that defensibility is moving beyond network effects. It now sits in workflow design, emotional intelligence in UX, and domain-specific execution.

We are not speculating about a distant future. The shift is underway, and it is moving fast. Ground-level experts who move now have the best chance of shaping the next generation of scalable, real-world AI.

I recently had a great conversation with Michael.

To preserve his confidentiality I won’t share details about the software he’s looking to build, but Michael is the owner of a service-based business looking to build a software product help serve the middle of his market: not the massive firms, not the sole-proprietors — but small teams that are dramatically undeserved within the current software landscape. This is what he said:

”They need something simple and affordable. The big software is too expensive [for them], and the free versions don't cut it."

Michael's frustration perfectly captures a massive shift happening in software development right now.

For decades, the economics of building software meant only two types of companies could survive: massive B2C platforms serving millions of users, or enterprise B2B solutions charging 5-6 figures (minimum) per client per year.

The “middle market”, profitable niches serving thousands of customers at reasonable prices, was left behind.

But that's changing. Fast.

Why Software Development Was Historically So Expensive

For the past 30 years, building custom software required:

Large Development Teams: You needed senior developers, project managers, QA engineers, DevOps specialists, and designers. Even simple applications required 4-8 people for months.

Everything Built From Scratch: Every feature; user authentication, payment processing, data storage, email notifications, had to be coded manually. A basic business application could easily require 50,000+ lines of custom code.

Complex Infrastructure: Setting up servers, databases, security, and scaling required specialized expertise and expensive hardware. Cloud services existed but were complex and costly to implement correctly. Most software was on-prem.

Long Development Cycles: Without modern frameworks and tools, building even basic functionality took months. Iteration was slow and expensive.

This meant software development costs started at $100,000+ for anything meaningful. Only two business models could justify these costs:

1. Mass Market B2C: Apps serving millions of users with freemium models or advertising revenue

2. Enterprise B2B: Software charging $50,000-$500,000+ per customer

The VC Funding Model Reinforced This Reality

Venture capital operates on a "swing for the fences" model. They need companies that can:

• Scale to billion-dollar valuations

• Achieve massive market penetration

• Generate returns of 10x-100x their investment

This creates a natural bias toward:

Huge Total Addressable Markets (TAM): VCs want companies targeting $10B+ markets, not $100M niches.

Winner-Take-All Dynamics: Platforms that can achieve massive scale and network effects.

Capital-Intensive Growth: Companies that can absorb millions in funding to achieve rapid growth.

The result? Countless profitable middle market opportunities were ignored because they didn't fit the VC model.

Why The Middle Market Was Underserved

Let's look at Michael's example:

• Market Size: His industry niche might be a $50M market

• Customer Base: Maybe 5,000-10,000 potential customers nationwide

• Price Point: $20-200/month per user

• Revenue Potential: $2-20M annual revenue at near market saturation

For a VC, this doesn't move the needle. It's not a billion-dollar opportunity.

But for a bootstrapped founder? This could be a $2-20M annual revenue business with 80%+ margins.

The problem was development costs. Spending $300,000-500,000 to build software for a $50M market made the unit economics impossible for bootstrapped founders.

How Modern Development Changed Everything

Three major shifts have made middle market software development economically viable:

1. AI-Accelerated Development

AI tools like GitHub Copilot, Claude, and specialized coding assistants have dramatically increased developer productivity:

• Code Generation: AI can write 60-80% of boilerplate code

• Faster Debugging: AI helps identify and fix issues in minutes, not hours

• Architecture Planning: AI assists with system design and best practices

Result: Development time reduced by 40-60% for many projects.

2. Modern Development Infrastructure

The explosion of APIs, packages, and cloud services means developers rarely build from scratch:

Authentication: Auth0, Firebase, or Supabase handle user management
Payments: Stripe, Square, or PayPal provide payment processing
Databases: AWS RDS, PlanetScale, or Supabase manage data storage
Email: SendGrid, Mailgun, or Resend handle email delivery
Hosting: Vercel, Netlify, or Railway deploy applications instantly

Result: What took 6 months to build now takes 6 weeks.

The New Economics of Middle Market Software

Here's how the math has changed:

Traditional Development (2000-2020):

• Development Cost: $200,000-500,000

• Timeline: 8-18 months

• Team Size: 6-10 people

• Infrastructure Setup: 3-6 months

Modern Development (2025+):

• Development Cost: $10,000-30,000

• Timeline: 2-6 months

• Team Size: 1-3 people

• Infrastructure Setup: 1-2 weeks

This 90% cost reduction has opened up thousands of profitable middle market opportunities.

Suddenly, a profitable business serving a specific niche becomes not just possible, but attractive.

The PE Exit Opportunity

While VCs aren't interested in $50M markets, private equity firms absolutely are, especially vertical market software (VMS) aggregators like Constellation Software.

PE firms are actively acquiring software companies with:

• $3-15M annual revenue

• High recurring revenue (70%+ SaaS metrics)

• Niche market leadership

• Predictable cash flows

They're paying 4-8x revenue multiples for these businesses.

This creates a clear path: Bootstrap a middle market software business to $5-10M ARR, then exit to PE for $20-50M.

Why This Trend Is Accelerating

Several factors are making this opportunity even more attractive:

Increasing AI Capabilities: Development will only get faster and cheaper as AI improves.

Growing API Economy: More services become plug-and-play, reducing custom development.

Remote Work Normalization: Access to global talent continues expanding.

VC Market Saturation: VCs are chasing fewer mega-opportunities, leaving more middle market gaps.

Small Business Digitization: COVID accelerated software adoption among traditional industries.

How to Identify Middle Market Opportunities

Look for markets with these characteristics:

Clear Pain Points: Industries using spreadsheets, paper, or outdated software for critical processes.

Fragmented Solutions: Markets where people cobble together 3-4 different tools.

Price Gaps: Where enterprise software costs $500+/month but free options are inadequate.

Overlooked Niches: Industries VCs consider "boring" or "too small."

Regulatory Requirements: Sectors with compliance needs that generic software can't address.

The Bootstrapper's Advantage

Bootstrapped founders now have several advantages over VC-backed startups in middle markets:

Speed: No board meetings or investor approval needed for product decisions.

Focus: Can target specific niches without pressure to expand TAM.

Profitability: Can optimize for cash flow, not growth-at-all-costs.

Customer Intimacy: Direct contact with users, not filtered through product managers.

Flexibility: Can pivot quickly based on customer feedback.

Your Middle Market Opportunity

The software development cost revolution is creating thousands of opportunities that were previously impossible to pursue profitably.

The question isn't whether these opportunities exist—it's whether you'll recognize and act on them.

Start by looking at your own industry or expertise:

• What processes still rely on spreadsheets or manual work?

• Where do existing solutions overshoot or undershoot market needs?

• What would you build if development only cost $20,000 instead of $200,000?

The middle market software revolution is just beginning.

The founders who recognize this shift—and act on it—will build the next generation of profitable, sustainable software businesses.

While VCs chase the next unicorn, there's never been a better time to serve the overlooked middle and build something valuable.

More Benmore Technologies Content:

AI in Software Development: A Deep Dive

In this article we go in depth on how AI is currently being used to assist software development and where we think it's headed in the upcoming years.

brief.benmore.tech/p/title-9533b15f78558ae3

On the Road: Building Connections and Driving Innovation

Over the past few weeks, our team has been hitting the road to connect with founders, operators, and innovators across North America. There's something irreplaceable about face-to-face conversations, they reveal insights that simply can't be captured through screens.

Recent Field Updates

Bristol, Rhode Island
We visited a renewable energy company that owns and operates solar farms throughout New England. Our discussions centered on how AI can optimize yield and streamline operational efficiency across their portfolio. The potential for technology to accelerate clean energy adoption continues to inspire our work.

Calgary, Alberta
We reconnected with Derek Ojukwu, a recent University of Calgary Computer Science graduate, to explore how AI is transforming the landscape for early-career software engineers. These conversations with emerging talent help us stay connected to the next generation of innovators.

Edmonton, Alberta
We caught up with entrepreneurs Banti Shaw and Mohammed Adnan Patel, who shared insights about the MVP they're developing for their exciting new venture.

We also had the privilege of meeting with Sinclair Ajoku, our Lead Designer, in person for the first time after nearly two years of remote teamwork. Hearing his story and vision for user experience firsthand reinforced why we're passionate about what we do.

Grand Prairie, Alberta
We met with Mitch Heinricks and Travis Driedger from Thor Electric to discuss their innovative new product. Rather than a typical conference room meeting, we talked shop at a clay shoot, proving that the best business conversations often happen in unexpected settings.

Austin, Texas
We visited The Data Foundry data center and collaborated with a team member from Genesys Consulting, a managed service provider and IT consulting firm based in Providence, Rhode Island. Working hands-on to install hardware components on one of their racks provided invaluable insight into the infrastructure requirements that power today's digital solutions.

Looking Forward

In our increasingly digital world, it's tempting to default to virtual interactions. However, there's no substitute for genuine, face-to-face connection. These conversations don't just inform our technology solutions, they shape our understanding of the real challenges our clients face every day.

We're energized by the innovations we've witnessed and the partnerships we're building. The future of technology lies not just in what we create, but in how deeply we understand the problems we're solving.

Exciting developments are on the horizon, and we can't wait to share more with you soon.

Best regards,
The Benmore Technologies Team

Interested in learning more about how Benmore Technologies can help solve your technical challenges? Contact us to start the conversation.

More Content

Rethinking Digital Transformation: Why More SMBs Are Leveraging Custom Technology Solutions

"Are your business processes stuck in the past, costing you time, money, and growth opportunities?” If you’re a… Director of Transformation CIO IT Director Operations Leader …an SMB, you’re likely tasked with modernizing operations to stay competitive. Digital transformation—the process of using tec

www.linkedin.com/pulse/rethinking-digital-transformation-why-more-smbs-leveraging-tzynf/?trackingId=4CJA5PVoB6Vikhto94P9Kg%3D%3D

"Are your business processes stuck in the past, costing you time, money, and growth opportunities?”

If you’re a…

• Director of Transformation

• CIO

• IT Director

• Operations Leader

…an SMB, you’re likely tasked with modernizing operations to stay competitive. Digital transformation—the process of using technology to streamline workflows, boost efficiency, and drive value—promises to get you there.

But too often, common solutions like…

• off-the-shelf SaaS tools

• in-house tech teams

• expensive consulting firms

…lead to slow progress, ballooning costs, or unclear ROI.

The good news? You can achieve digital transformation with bespoke in-house solutions that are fast, cost-effective, and deliver measurable results—without the usual headaches.

Let's break down...

• Why bespoke solutions are the key to successful digital transformation.

• The pitfalls of common approaches

• How Benmore Technologies uses AI to deliver optimized, scalable solutions.

• A real-world case study showing this approach in action.

By the end of this article, you’ll have a clear roadmap to transform your business with tailored solutions that avoid the traps of traditional methods.

Why Custom Solutions Are the Future of Digital Transformation

Digital transformation is about reimagining how your business operates using technology—whether it’s automating workflows, unifying data, or improving decision-making.

Bespoke in-house solutions, tailored to your unique needs, are the most effective way to achieve this. Here’s why:

1. Perfect Fit for Your Business: Unlike generic SaaS tools, bespoke solutions are designed to match your specific processes, ensuring seamless integration and maximum efficiency.

2. Scalability and Control: You own the solution, allowing you to adapt it as your business grows, without being locked into rigid vendor platforms.

3. Clear ROI: Custom solutions focus on high-impact problems, delivering measurable savings and revenue growth, unlike sprawling consulting projects with vague outcomes.

But many businesses shy away from bespoke solutions, fearing they’re too risky, complex, or costly.

Instead, turning to alternatives that often fall short.

Let’s explore these common approaches and their pitfalls.

The Pitfalls of Common Digital Transformation Approaches

Most technology initiatives fixate on one of three paths for digital transformation, each with significant drawbacks:

1. Off-the-Shelf SaaS Tools The Problem: SaaS platforms like Salesforce or Monday.com promise quick fixes but often don’t align with unique workflows. You end up paying for unused features, facing integration headaches, or needing costly workarounds. For example, 41% of SMBs report feeling lost in the maze of SaaS offerings, unsure which tools fit their needs. The Impact: Misaligned tools lead to low adoption, duplicated efforts, and ongoing subscription costs without solving core problems.
   

2. Hiring In-House Tech Teams The Problem: Building an in-house team sounds appealing, but if tech isn’t your core competency, it’s a recipe for trouble. Recruiting skilled developers, managing projects, and keeping up with trends is expensive and time-consuming. A study found 54% of organizations struggle with a lack of technical skills for digital transformation. The Impact: Projects drag on, costs escalate, and you’re left with solutions that may not scale or integrate well.
   

3. Expensive Consulting Firms The Problem: Big consulting firms like McKinsey or Bain offer expertise but often deliver slow, costly projects with unclear ROI. Their recommendations, like installing CRMs or hiring Chief Digital Officers, can feel generic and disconnected from your reality. The Impact: You’re stuck with never-ending engagements, high fees, and solutions that don’t fully address your needs.

These approaches share common issues: they’re too slow, too expensive, or fail to deliver clear value.

So, how can you do better?

How Benmore’s AI-Driven Bespoke Solutions Solve These Problems

At Benmore Technologies, we use AI and a lean, iterative approach to deliver bespoke solutions that overcome the pitfalls of SaaS, in-house teams, and traditional consulting firms.

How We Do It:

• Lean MVPs: We build Minimum Viable Solutions that solve one high-impact problem first, delivering value quickly and scaling later.

• Human Oversight: Our expert team refines AI outputs, ensuring solutions are practical and aligned with your goals.

• Agile Iteration: We deliver incrementally, incorporating feedback to keep projects on track and within budget.

This approach is faster, cheaper, and more effective because it’s built for your business, not a one-size-fits-all template. Let’s see how it works in practice.

Case Study: Sun Theory’s Digital Transformation with Benmore

Let’s look at how we helped Sun Theory, a cannabis retailer and wholesaler, achieve digital transformation with a bespoke solution.

The Problem: Sun Theory operated multiple retail locations and a wholesale division but struggled with fragmented data across systems:

• Their wholesale POS (LeafLink) didn’t connect well to Power BI, limiting analytics.

• Retail data from FlowHub was siloed across locations, making unified reporting impossible.

• Manual order updates between LeafLink and Pipedrive caused delays and errors.This cost them hours weekly and hindered data-driven decisions.

Our Bespoke Solution: Instead of recommending SaaS tools or a massive process overhaul, we built a tailored, AI-assisted solution:

• Database Migration: Moved their Postgres database from DigitalOcean to Azure MySQL for better Power BI compatibility.

• LeafLink-Pipedrive Integration: Used Django webhooks to automate order updates, syncing statuses (e.g., “Shipped” to “Delivered”) in near real-time.

• FlowHub Aggregation: Deployed Azure Functions to pull retail data from FlowHub every two hours, consolidating it into a single MySQL database for Power BI.

• AI Acceleration: AI-generated flow diagrams and mockups streamlined planning, while our team refined the solution for accuracy.

The Results:

• Efficiency: Automated order management cut manual work by 90%, saving 15 hours weekly.

• Visibility: Unified retail and wholesale data enabled real-time Power BI dashboards, improving decisions.

• Scalability: The Azure-based architecture supports future integrations, avoiding vendor lock-in.

• ROI: Delivered in 2 months with a payback period of 3 months, compared to years for consulting-led projects.

Why It Worked: Unlike SaaS tools (misaligned), in-house teams (lacking expertise), or consulting firms (slow and costly), our AI-driven bespoke approach was fast, tailored, and ROI-focused.

Here’s how we compare:

Your Roadmap to Digital Transformation with Bespoke Solutions

Here’s a 5-step plan to achieve digital transformation with bespoke solutions, inspired by our work with clients like Sun Theory:

Step 1: Identify Your Core Challenge(s)

Pinpoint one high-impact process to transform. Ask:

• What’s costing the most time or money? (e.g., manual reporting, siloed data)

• Where would automation or integration deliver quick wins?

Step 2: Audit Existing Systems

Map your data sources, workflows, and tech stack.

• Identify integration points (e.g., CRMs, ERPs, POS systems).

• Assess compatibility with analytics or automation tools.

Step 3: Build a Minimum Viable Solution (MVS)

Develop a lean, bespoke solution targeting your core challenge.

• Focus on must-have features (e.g., Sun Theory’s webhook automation).

• Avoid “nice-to-haves” like advanced analytics until value is proven.

Step 4: Pilot and Refine

Test the solution with a small team or department.

• Gather feedback on usability and impact.

• Iterate quickly to address issues.

• Track metrics like time saved or revenue gained.

Step 5: Scale and Optimize

Once proven, expand the solution to other processes or systems.

• Add features based on team needs.

• Integrate with tools like Power BI or Salesforce via APIs.

• Reinvest savings into broader transformation goals.

Your Turn to Transform with Bespoke Solutions

Digital transformation doesn’t have to be slow, expensive, or uncertain.

Custom in-house solutions, built with AI-driven efficiency and human expertise, offer a smarter path—unlike the misfit of SaaS tools, the complexity of in-house teams, or the bloat of consulting firms.

Now, think about your current operations:

• What process is holding you back?

• Where could a bespoke solution unlock efficiency or growth? That’s your opportunity to start.

In our latest episode of The Benmore Brief Podcast, we sat down with client Daniel “Saxman” Fuqua, founder of GigMate, an application built for solo and duo performers who want the sound and presence of a full band—without hiring one.

Before teaming up with Benmore Technologies, Daniel had the vision, the brand, and deep domain expertise—but needed a product partner to bring it all together.

Here’s how we helped make GigMate a reality:

🎛 Built for Live Performance: We engineered a custom experience for musicians to mix, loop, and adjust high-quality backing tracks on stage.

🛠 Tailored for Creators: The app gives artists full control—adjusting tempo, key, and instrumentation based on their unique sound.

🚀 From Idea to Product: Daniel calls working with Benmore “a game-changer” that turned his idea into a tool trusted by performers.

The result? A platform that’s already giving independent musicians the power to elevate their live shows—and a foundation for scale.

Watch the full interview with Daniel:

In our latest episode of the Benmore Brief Podcast, we sat down with Mitch Barton, Director of Transformation at Sun Theory—a fast-growing cannabis company using data and software to streamline operations in one of the most complex regulated industries.

We discussed:

• His journey from philosophy student to cannabis tech leader

• How data is driving smarter decisions in cannabis operations

• The challenges of scaling with fragmented systems across retail and wholesale

• Why AI and automation are the future of cannabis analytics

• What extreme ownership looks like inside high-growth businesses

If you're curious about how technology is reshaping emerging markets—or how to build smarter, more efficient systems in regulated industries—this episode is for you.

Watch the full episode here:

Want to learn more about how we helped Sun Theory acheive their technology goals?

Click here to check out their case study.

Most founders don’t fail because their vision wasn’t big enough.

They fail because they tried to build the entire vision on day one.

All the features. All the users. All the edge cases.

And in doing that?
They burned time, money, and momentum before ever hitting the market.

But what if there was a better way?

Our latest video breaks down a strategy we’ve recommended again and again at Benmore Technologies to help founders launch faster, prove demand early, and scale with confidence.

It’s called the Wedge Strategy.

What is it?

It’s the art of building only the part of your product that solves one painful, specific problem—and ignoring the rest (for now).

Instead of trying to build a full “ecosystem,” you start by earning your way into the market with a focused MVP.

You wedge into the market. Then you expand.

This is how startups like Notion, Figma, and Slack got started. And it’s the same playbook we use with a lot our clients.

In the video, I break down:

• Why most first-time founders take on too much risk too early

• What makes a good wedge (and a bad one)

• How to avoid overbuilding and move faster with less stress

• The step-by-step timeline that took Notion from a simple notes app to a $10B platform

And most importantly…

How you can apply the Wedge Strategy right now to build your MVP faster, more capital-efficient, and with less risk.

If you're sitting on an idea but unsure how to move forward… this one’s for you.

Best,
Timothy John Uzoegbu
Co-Founder, Benmore Technologies

In this week’s episode of the Benmore Brief Podcast, we sat down with Jason Ford, Senior Mortgage Banker at Bell Bank, to talk about how software is reshaping financial services—without losing the human touch.

Jason shares his unique path to mortgage banking and reflects on how remote work, AI tools, and digital communication platforms are transforming the way professionals engage with clients in a highly regulated space.

We cover:

• Building trust in a digital-first world

• The role of personalization and speed in customer communication

• How the mortgage industry is adapting to remote work and automation

• The impact of AI and where efficiency meets compliance

Whether you're in fintech, real estate, or just curious about how legacy industries are adapting to modern tools, this episode is worth a listen:

The Future of Mortgage Banking: Remote Work and Tech

Missed our previous episode?

In our latest episode of the Benmore Brief Podcast, we sat down with Arkash Jain, a researcher at Harvard Medical School working at the intersection of AI, computer vision, and cell biology.

We discussed:

• His journey from India to Boston University to Harvard
  

• The shift from traditional SaaS to custom-built internal tools
  

• How advancements in AI—particularly self-supervised learning—are reshaping the software landscape
  

• The growing role of open source in innovation
  

• What all of this means for the future of work and software development
  

If you’re thinking about where technology is headed and how to stay ahead of the curve, this conversation is worth a listen.

Watch the full episode here:

AI this and that - what’s going on?

I’m sure that you have seen it on the news, from friends, from a random person in a coffee shop – AI is here. But what even is it, and what is going on?

Well, to start, we need to define AI, as it has fallen into a “hype phrase.” Most of the hype you have seen regarding AI has been centered around Large Language Models (text generation) and image and video generation. There’s a whole subset of AI that has not made the news regarding data analysis, traditional algorithm methodology, and pattern recognition.

So, for the purpose of this article, we’re going to talk about Large Language Models (LLMs) and the use cases that we have seen in relation to software development.

Applications like ChatGPT, Claude, embedded LLMs in Cursor, and Devin AI are points of contention amongst software developers, so we aim to voice our opinions on the current and future use cases.

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How do these things even work?

Well, in short, ChatGPT and other LLMs are predictive models that try to predict the next word or character.

More info here: Stephen Wolfram’s article

These are mathematical prediction engines that run off of data. The more data you feed an LLM, the better it gets at predicting. Additionally, the less variability in the prediction, the better it performs. This is my theory as to why they work so well with code. Code follows a set language; there isn’t as much variability as if you were to generate a full novel, for example.

But that’s it; it’s just a theory. Interestingly, even the creators of these LLMs don’t know why they work so well, which is scary or interesting depending on how you look at it.

Regardless, we’re here, the cats out of the bag; we are left to determine and iterate how it can be applied.

The current state of LLMs

Right up until about a week ago, concerns were being raised about the future viability of LLMs. Companies were running out of data (yes, you heard that right) and were resorting to making synthetic data to continue progress, which could lead to a snake that ate its own tail in terms of model degradation. There was a lot of skepticism that we had hit a plateau of model intelligence, and it was clear that a new path or methodology forward was needed.

This all seemed to come to a halt just a few days ago when OpenAI announced its o3 model. Focusing on self-prompting, reasoning, and prompt iterations, it seems as if the ceiling has been shattered.

Essentially—and this is again just a super simplified explanation—rather than focusing on feeding the LLMs more data, the momentum has shifted to how we optimize the reasoning capabilities of these models. So, how can we make it self-prompt itself, how can we make it essentially think? Which is, I mean, yeah—it’s wild.

To that point, they even ran tests where the LLMs were literally scheming and deceptive. Basically, if they thought they were going to get shut down and terminated, they started lying in self-preservation.

Please, please, please watch these two videos if you’re interested in anything I said above:

Ok, how does this relate to software development?

With all of that out of the way, let’s talk about how these models assist with software dev. First and foremost, these are all our opinions from working on multiple projects at the MVP and enterprise level.

MVP (or small project) development.

One word: GREAT! It was so good, we put our entire development team on a ChatGPT license.

Now, the reason why is more nuanced. Most MVPs are pretty straightforward in complexity. Also, you’re starting from scratch. This is where LLMs excel. They can just speed-run setting a project up; they can speed-run simple feature implementation.

However, we have come to the conclusion that you still need a developer at the helm. It’s not there yet where you can have no technical knowledge and prompt yourself into an application.

Furthermore, we optimize our processes to completely align with AI. Specifically, recall how I said that ChatGPT is trained on publicly available data. That means it has a good grasp of common coding languages and frameworks. We use Django for most of our applications for various reasons, one of which, though, is it uses Python and, depending on the frontend you need, uses HTML, CSS, and JavaScript—which are the top four most popular coding languages according to Statista (link below).

Again, we can really only make assumptions, but I think part of the reason why Benmore has had so much success with AI is because we build apps in the most popular coding languages.

(For the developers reading this, we know that Django has many pitfalls as well, but there are more reasons than just AI usage that we mainly use Django. We’ll probably outline this in a future article.)

So all in all, because MVP projects are typically less complex, aren’t built on an existing codebase, and, with Benmore, use well documented languages; AI code generation has been great.

Considerations

BY NO MEANS does this mean that we just have people copying and pasting AI code—no, definitely not. However, it has shifted certain processes to be more review-oriented.

For example, let’s say you need the code to handle a sign-up page on an app. Rather than code every line from memory or using documentation, you can ask ChatGPT to generate the code and then review and test it to ensure it works as expected.

It’s certainly less of a cognitive load (less mentally taxing) than writing all of the code. But every line of code that is implemented has to be audited by a developer.

Where we think it’s going:

I think that in the next 5-10 years MVPs will be able to be built 90% by AI and then a developer will need to go in and build the outstanding features. However, in order for this to happen, there still needs to be a huge emphasis on the planning and discovery phase of your application.

Existing Codebases

One word: horrific. Yeah, it’s just pretty bad in general, and it’s pretty clear what the reason is as well.

At a certain point, larger codebases reach a level of complexity where no one understands most of the app. In a best-case scenario, maybe your most senior developer knows about 30% of the codebase.

This just happens naturally as you grow and have more developers contributing to applications—there’s code being pushed from 20, 30, or 1,000 people.

That being said, if you were to use AI, any feature that is developed is typically going to be dependent on a whole bunch of code in order to function correctly. Even if you use a service-based architecture, each service is probably going to get complex.

As AI stands today, it doesn’t have the ability to take in enough context to build features in a complex codebase effectively.

Where we think it’s going:

I think that as reasoning improves, these models will be able to search the codebase—like any other developer would—look up resources online, and contribute more effectively. I’m hesitant to say that it’ll be good at that for a while, but I do think that it will get to a point of being able to contribute to larger codebases.

However, no matter how good it gets, there will probably be some human expert needed to guide and understand the code that is written. This goes for MVP projects as well.

Thanks! And if you’re interested in learning more about our processes or the apps that we have build using AI, book a call!

Let’s get started! Book a free consulting call:

Benmore Technologies Discovery Call

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Let’s integrate AI into that end-user’s registration workflow using the GPT API to auto-translate languages to English- make sure you use a structured output and validate the JSON.

The above sentence would make sense to any developer that’s worked with OpenAI’s ChatGPT models in an application but would probably come across as a foreign language to someone who isn’t a developer or actively working in technology.

Just like bankers throw around complicated jargon, developers and tech professionals are no different. Now, this is all done for a reason. Software is a game of abstraction, and developers are dealing with complicated concepts that oftentimes are simplified into jargon to streamline communication.

For example, the above sentence could be rewritten as:

Let’s implement ChatGPT’s language translation functionality into the people who are using our app’s signup pages. We should do this by going to OpenAI’s, the company behind ChatGPT, standard online programming interface - in which we can ping the gateways they set up and use their language translation in our own application. Furthermore, we need to be consistent in the response that ChatGPT responds with so we need to set up a standard return format that we can count on. We should make sure we set this setting, which is their structured output setting, when our application communicates with ChatGPT to ensure that our application doesn’t error out if ChatGPT malfunctions.

Whew! As you can see, this jargon exists for a reason, but that doesn’t mean you need to be blinded by it when you get into technical discussions with your developers. Below, we’ll discuss two methods to utilize when you find yourself in one of these technical discussions.

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Have ChatGPT on standby!

No joke, ChatGPT is excellent at deconstructing tech-language. If you find yourself in a technical discussion, go to ChatGPT, type in the jargon, and say: Explain this to me like I am five.

For example this is the output from ChatGPT using the original sentence.

You can follow up and ask ChatGPT to expand on specific parts of the response. For example, I am going to prompt it to expand on what JSON is:

Demand clarity from developers through visualizations.

So, when you’re consuming jargon, you can have ChatGPT as a helper to assist with your deconstruction. But what about when you’re working directly with developers, how can you assist them in breaking down concepts to you directly?

We’ve found that, at Benmore, visualizing concepts through whiteboarding is the easiest way to breakdown complex technical concepts.

Typically, we’ll use https://excalidraw.com/ to rapidly develop whiteboards that can visualize complex topics and make them consumable.

For example, here’s a whiteboard representation of the original sentence:

If you’re on a call with a developer, simply ask them to whiteboard their logic and send them the Excalidraw link. From there, the developer should be able to visualize the complex task for you and make it consumable.

Most actions in technology are a series of logical gates - if something happens, then do this. Because of this, whiteboarding the flow of the logic makes these complex flows understandable.

Bonus tip: brush up on the basics.

Most applications simply deal with creating data, reading data, updating data, and destroying data. (CRUD) If you have the time we’ve linked a couple basic explanations of common programming concepts below:

If you’re interested in getting started with us and learning how we manage technical communication with our clients, book a call below.

Let’s get started! Book a free consulting call:

Benmore Technologies Discovery Call

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What is Scope Creep?

Broadly, scope creep is when a project’s requirements expand beyond the original vision for the project throughout the lifecycle of the project. This is a concept that can be seen with almost all projects and especially with software development. The issue with scope creep is it can bloat timelines for a launch, it can increase application cost, and it can lead to indecisiveness and an unclear vision for the application.

We’ll discuss how to proactively protect your project against scope creep and also how to react to its manifestation mid-project. Note that we’ll mainly be discussing scoping and how it effects the MVP development of software products by drawing on previous experiences that we have had with our own applications and in our service work for others.

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Prevention of Scope Creep.

The easiest way to “defend” against scope creep is too prevent it before you even start the development of your application. Commonly referred to as a requirements gathering phase or a discovery phase, there should be a concentrated period of time in which you determine what exact features and requirements you need to have in your application.

You can even take this one step further by designing mockups of your application, gathering assets like your logo and your branding strategy, and putting together a non-functional prototype.

Regardless, the outcome of this phase of project development is to determine essentially two things: what your application is going to do and what it is going to look like. If you can nail these two things down, you essentially have a great “roadmap” or, as we like to call it, a “blueprint” that you can refer back to when it is time to start writing code.

How we Utilize this in our Process.

At Benmore, a main focal point is our commitment and concentration on the discovery phase when we work with our clients. Discovery phases typically take between 4-6 weeks and include weekly calls with our team. We believe that if we are able to work with our clients to get them to a point, as stated above, where they know what their app is going to do and what it’s going to look like; we are then in a great position to move into development.

Specifically, we use Figma to design the core views or screens in your application, research and technically vet your application idea, and work with you to create a list of requirements. Once you are comfortable with the discovery phase, then we discuss development and timelines; knowing exactly what we’re going to build and what it’s going to look like.

Managing Scope Creep During Development.

So, we’ve developed a great blueprint, we know exactly what the application is going to do, should be smooth sailing from here, right?

Well, sometimes yes, most of the time no - but that’s not a bad thing. For example, let’s say you made an AI app that, let’s see, creates a yoga routine for users. However, halfway through the development of the application, you realize that users also want meditation sessions generated as well. So, you add this to the requirements list and expand timelines. That’s scope creep!

However, that’s not necessarily a bad thing, especially if it helps out your application. Your blueprint should never be too rigid and should allow for mobility as it is typically going to arise throughout development. However, how can you manage this so it doesn’t get out of control?

The key thing that you need to determine if you have an idea during development for expanding scope is: is it necessary? We think that there’s a pretty straightforward way to simplify this. If a potential customer has mentioned it, then it might be. However, when you are building an application, it’s important to understand that you are viewing it through your eyes, not your users!

When you think that a feature should be added, you should always do so cautiously. Specifically, you should understand that adding a feature will probably increase the timelines for your application and the cost as well. It’s also important to understand that if you haven’t asked essentially over a hundred potential users, you’re pretty much assuming that’s it is something that most users would deem as needed.

So, it’s important to weigh all of these factors in before making a call on a feature expansion and determine if it actually makes sense to develop.

How we Utilize this in our Process.

We set up our development structure to be as accommodating as possible when it comes to feature expansion. Specifically, we bill, during the development phase, on a set monthly schedule. This means if you request a feature to be added to the project, we don’t have to revisit a discovery phase; we simply give you a timeline for the additional feature and inform you on the increased timelines and cost.

Furthermore, any developer that you are dealing with from our team is the owner of the project, meaning you have the actual person building your application making the timeline extension estimations and calling the shots. This, in our opinion, is most effective because the developer, who is building your application, is going to be most in-tune with the codebase and ramifications of a feature request - making them most qualified to inform you on the extended timelines and impact of a new feature.

Furthermore, all of our developers are full stack, meaning that, unless necessary, we don’t pull back in the design team or the product lead to reopen the discovery phase. Our developers have the skillset necessary to make design decisions on the fly and stand as independent developers who don’t need to fallback on other team members to move development forward. All of our developers are briefed in our “forward deployed developer” methodology which puts the developer at the forefront of the project and in direct communication with you throughout the entirety of the project’s lifecycle.

We took inspiration for this model of development from Palantir’s delta developers. You can read more about the methodology here:

Dev versus Delta: Demystifying engineering roles at Palantir

Palantirians talk about what it’s like to work at a company that does engineering differently.

blog.palantir.com/dev-versus-delta-demystifying-engineering-roles-at-palantir-ad44c2a6e87

If you want to learn more about our process or have an idea for an application that you’re ready to get started on - book a call with us!

Let’s get started! Book a free consulting call:

Benmore Technologies Discovery Call

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A software approach to the standard lead generation playbook.

I’m sure you’ve heard it before when it comes to driving leads; ads, ebooks, webinars, newsletters and so on. However, when you are woking with developers, or are a developer, oftentimes you can take these common strategies and implement them with a software twist.

Specifically, you can spin off a small part of your core products’ functionality into a lightweight tool that is free to use, but requires a signup. Or, you can develop an adjacent tool that is free to use but targets potential paying customers.

Below, we’ll discuss three methods that we’ve seen founders and companies use to drive leads using spin-off or lightweight software tools.

Benmore’s founder tools of the week:

Check out these tools to supercharge your growth.

The Innovation Workspace | Miro

Miro introduces the innovation workspace: a single AI-powered collaboration platform that helps teams move faster from idea to outcome.

miro.com/innovation-workspace

Angel: Timeless Advice from an Angel Investor Who Turned $100,000 into $100,000,000

In ANGEL, Jason Calacanis shares every unfair advantage he developed to deploy capital more intelligently than the competition.

www.angelthebook.com

Create courses with AI-powered authoring tool | LearningStudioAI

Transform any subject into a stunning online course with LearningStudioAI.

learningstudioai.com

Method 1: The Mortgage Calculator.

The first example that comes to mind for the strategic use of a lightweight software tool is the mortgage calculator. This tool allows people to calculate their mortgage costs and targets prospective home buyers. So, unsurprisingly, companies that service mortgages are the ones building this software tool and using it to drive leads. A quick Google search confirms this assumption.

The first result for me was Bankrate, which provides mortgage services.

But, expanding on this, you can use this strategy in your company as well. If your target customers have some sort of need for a calculator, or a calculation of some sort, you can build a tool that performs the calculations. There are so many use cases for this; tax estimators, insurance estimations, savings projections etc.

You’re going to want to make this tool easy to use, and simple. You probably don’t want to even require a signup as the goal of the tool would be to drive consistent eyeballs to your website and other content with the goal of developing a brand and potentially getting newsletter or lead form signups.

Method 2: The Plugin.

When it comes to developing a tool for lead generation, you’re going to want to expose the tool to as many users as possible, for free. Now, if you’re just starting out, you probably don’t have an audience or customers; so how can you get your tool in front of people?

This is where plugins are extremely valuable. If you create a plugin for an existing platform, you automatically expose the tool to the plugin-store of the platform - which gives you free exposure.

For example, if you made a screenshot tool for Google Chrome, there are millions of users browsing the Chrome plugin store. Chances are someone will see your tool from the plugin library and start using it. Fast forward a couple months, you could have thousands of users that know of your brand, use your tool, and that you have collected lead info on.

There are plenty of platforms that are worth building a tool for, but it’s important to ensure that the tool still targets your customer profile. For example, if you have an application that helps architects manage their projects, you don’t want to build a screenshot tool. But, you may want to build a plugin for Google Chrome that has a handy collection of common architectural equations and analysis calculators.

Check out the video below if you want to see an example of someone who just developed a chrome extension, and was able to monetize off of the extension alone.

Method 3: Open Source It.

Lastly, an interesting way to drive leads is to build a democratized, open sourced tool for a community to contribute too. From there, you can build your reputation, drive traffic to premium version, or foster an audience.

This is a bit different from the previous two methods in the sense that it is typically a larger scaled undertaking. With a calculator or plugin, you’re looking at developing a quick productivity boosting tool. However, an open sourced tool (typically) is going to lean more on the side of exposing essentially a complete application rather than a smaller tool.

A prime example of this is https://excalidraw.com/. Excalidraw is a free, open source tool that we use daily at Benmore for white-boarding. It’s extremely helpful and is extremely user friendly. Furthermore, there is an API and general developer support as well. Regardless, if you go to https://plus.excalidraw.com/, you can see a premium version of the application that is geared a bit more towards enterprises.

This is a genius play from a business perspective. The free tool is used by tons of people and is open, transparent, and community driven. However, there is a paid version for customers that need more utility.

That being said, as you start to build your project or formulate your idea, think about a part of the functionality that could lend to community-driven growth through open sourcing.

If you thought any of the above methods were interesting or want to talk more to us about developing software-enabled marketing tools, let’s talk!

Let’s get started! Book a free consulting call:

Benmore Technologies Discovery Call

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Jack of all trades, but a master of none.

A common pitfall of modern AI products is the generalization of the data that they were trained on. Let’s take an LLM (A Large Language Model like ChatGPT, Gemini, etc.), they had to be trained on publicly available and non-specific data. What this essentially means is that the more niche of a solution that you need, the less accurate the LLM will be.

This is because an LLM is a product of predicability; it simply predicts, based on its training, what the answer should be to your question. So, if you work in a space that has little online documentation or non-public documentation, LLMs are going to fare terribly with your use-cases.

This is a simplification, and if you want to look under the hood at how LLMs work check out this article: https://writings.stephenwolfram.com/2023/02/what-is-chatgpt-doing-and-why-does-it-work/.

However, this means that if your product or positioning puts you in a place where you can access, have knowledge of, or own non-exposed data, you can customize an agent that is tailored to a niche use case.

Benmore’s founder tools of the week:

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ChatGPT Prompt Engineering for Developers - DeepLearning.AI

Level up your use of LLMs with prompt engineering best practices. Learn to automate workflows, chain LLM calls, and build a custom chatbot.

www.deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers

There’s an ai for that: an online database of AI tools that you can use to search for use cases.

theresanaiforthat.com

Rise of the agents.

The “agentization“ of ChatGPT or other LLMs is not a new concept at all. Initially starting with the ChatGPT plugin concept and evolving into the GPT Library, where users could tailor ChatGPT to their use cases and release them to a community; the need for specialized agents is an acknowledged and continuing problem with LLMs.

But, before we even start getting into how to build an agent and fill this gap in the market, let’s first discuss what an “AI Agent“ is:

From Amazon, this is the best description of an AI Agent that we could find online, but it’s still very broad. In essence, using the above definition, a lot of software solutions are “AI Agents“. So, we’re going to develop our own definition for the purpose of this article:

An “AI Agent” is a tailored LLM that is better suited to perform a specific subset of tasks for an end user by utilizing specific prompting, enhanced context, and/or custom capabilities.

How this often manifests in computer applications is with a general LLM tasked with categorizing a user’s request, and then using its knowledge of available agents to delegate the work to the enhanced capabilities of an agent. Take a look at the digram below to see this visualized:

Now that we’ve covered the general concept of the “AI Agent“, let’s talk about some use cases that you, as a founder, can build or integrate with an existing product.

The opportunities.

Below, we’re going to list a couple methods we’ve seen be successful when it comes to building and providing value with these agents.

Method 1: Custom Business Processes.

A common use case for these agents is with business specific processes. Although this is more on the service side of things, there are companies like https://customgpt.ai/ that have productized this concept.

Essentially, what it entails is working with a business’ processes to develop AI agents specifically for their use cases.

Example:

Problem:

A client of yours has a business that builds and maintains roofing for commercial warehouses. They serve over 500 warehouses in the greater-Seattle area. They are frustrated with their support and maintenance request process and are looking for AI assistance.

AI Agent Solution:

You decide to build an AI Agent for them that has knowledge of the following concepts:

• Business specific language in the roofing industry and commercial warehouse industry.

• Understanding of the geographic layout of the Seattle area and common references to neighborhoods, addresses, and weather.

• Understanding of the client’s maintenance processes: who and when to escalate requests, and how and where to put in requests for maintenance and support.

Other Examples:

Employee onboarding processes and training, online advertising copywriting and posting, meeting report summarization and logging.

Method 2: Custom Data.

Another common use case for these AI Agents is when you’re dealing with non-public or difficult to access data. Specifically, if ChatGPT is having issues with finding data on a certain topic or needs live access to a dataset, this might be a good place to plug in an AI Agent.

Example:

Problem:

You are a research analyst at a law firm that specializes in patent law. As part of your job, you must research and validate existing patents to determine if there is a conflict for your clients. You are frustrated with the user experience of the online version of the patent database.

AI Agent Solution:

You decide to build an AI Agent that has direct access to the public patent database. It can scan for similar patents to your described searches and analyze the language of the patents themselves for the specific conflicts.

Other Examples:

Business Intelligence dashboard summarization (generating a report from a PowerBI dashboard for example), a ChatGPT with knowledge of internal documentation, employee time logging summarizations.

Method 3: Repeatable, Long Prompts.

Lastly, if you have a need for a long, yet generally repeatable series of prompts, the development of an AI Agent could be beneficial. Basically, if ChatGPT can’t get you what you need in 3 prompts, an AI Agent could assist.

Example:

Problem:

You are a consultant that creates business plans for startups. Each business plan is 20 pages long and generally contains similar sections. You are frustrated with the time it takes to generate each plan even though it’s a repeatable process.

AI Agent Solution:

You decide to develop and AI Agent that allows you to enter in the general information on a startup and then generates a first draft of the business plan using 20 separate prompts that you can then edit and tailor before delivery.

Other examples:

Pitch deck generation, technical documentation generation, course generation, book generation.

By narrowing down your AI Agent to a specific domain, you can ensure protection from the rapid evolution of AI as well. Think about it, no matter how good ChatGPT gets, it’s never going to get its hands on IBM’s internal data, for example.

This is the opportunity for AI Agents, developing solutions for the edge cases - the places where ChatGPT doesn’t or can’t have data to train on.

Regardless, if you have an idea for an AI Agent, or would love to add the feature to your existing product, let’s talk!

Let’s get started! Book a free consulting call:

Benmore Technologies Discovery Call

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Let’s talk about a utilities business.

The US Department of Labor Statistics just released an incredibly interesting report that studies business’ survival rates by business type from 2013-2023. (View the report here). Here’s a direct quote summarizing the report: 

Ok, let’s deconstruct this starting with the first half: 

“... agriculture, forestry, fishing, and hunting industry had the highest survival rate (50.5 percent still in business in 2023), followed by utilities (45.7 percent) and manufacturing (43.6 percent)”. 

Now think about that, how many posts on LinkedIn have you heard someone say “I’m excited to kickstart my utilities company.”?

But, let’s expand on that logic; if no one is thinking about it, no one is doing it; meaning there’s less competition in the space. 

Furthermore, if the execution of said company is difficult, any limited competition that might be in the space is going to succumb to pressure. 

One quick Google search on how to start a utilities company leads to a downpour of regulatory requirements, intensive capital requirements, and operational difficulties.

So, yeah, if you raise 50 million dollars and go through the regulatory process to form a utilities company in rural Idaho, you have a pretty high chance of success. But, importantly, let’s talk about the back half of that quote from the report.

Other industries with low 10-year survival rates were information (29.1 percent) and wholesale trade (32.1 percent).

That’s us! Information!

Now, do not be deterred by this statistic. Think about what it takes to start a tech company. If you know how to code, maybe 50 bucks and a computer. There’s (generally) less regulatory requirements, no required licenses, no geographical limitations, etc.

So, honestly, a 29% survival rate isn’t all what it seems.

However, there are some key things that can be implemented in the ideation phase that can improve these odds.

Let’s talk about the first type of tech business: overlooked.

We love when we’re dealing with an overlooked solution. Perhaps the best example of this is our logistics clients.

In logistics, everything is decentralized, trucking companies are running off of scattered systems built decades ago, a good portion of the industry is still running off of pen and paper, and yet; the entire country comes to a complete standstill if there’s a supply chain issue.

Furthermore, it’s an extremely specialized industry, even understanding what to build in logistics typically requires years of experience with exposure to various sides of the supply chain.

It took us 3 years of working with multiple logistics clients to get a hold of the terminology and practices. But there’s a ridiculous amount of opportunity. Still, and I want to emphasize this, realizing the potential of the industry requires a fundamental understanding of the business.

No clearer is this point emphasized then a release by Gartner:

So, I know I just threw a lot of info at you, but let’s compile this into an executable.

You can make the general claim that formulating a niche idea in an industry that you understand can instantly increase the viability of your business. 

How to approach building a niche solution.

Well, with a niched idea you typically want to be a bit more solution oriented.

With a problem in a niched business, the solution itself is going to be a lot harder to develop and the fact that the application functions as expected is going to be the biggest draw.

I’m sure you have used university software before or banking software before and the design has just looked terrible. And, I know I’ve done this, you think, wow why don’t they just change this?

But oftentimes, the system works, and it was hard to build, so who would care about the design, as long as it works.

We’ve dealt with this before with difficult integrations.

We would spend hundreds of hours just trying to get the data to flow consistently into our clients’ system.

It was euphoric when it passed all tests and ran smoothly for a month. And at that point like, sure, we’re going to put a decent design on top of it, but we’re not going to open up another hundred hours of design work. The system works! Slap a nice design on top of it and rev up that sales pipeline!

However, not everyone has worked in ball bearing manufacturing for three generations. I mean Uber was started, don’t quote me on this, I think because the founders were annoyed that they were waiting for a taxi?

Let’s talk about another route: difficult to execute.

Generally, apps that are not niched and target a wide range of consumers, oftentimes, are anchored by marketing.

Interestingly, the solution itself is not that complex, especially in the early stages of the business.

Think about Facebook; at its start, Facebook allowed people to upload pictures of themselves, view other peoples profiles, and interact with them. Technically speaking, nothing crazy.

But it was the marketing that made it explode: targeting universities, only allowing access via invites, the design and branding. 

Similarly, I just saw a recent podcast with the founder of Bumble, a dating app, in which Whitney Wolfe Herd explained she targeted sororities with merchandise and college campuses with flyers.

I would really recommend looking into both of these stories as they outline the alternative marketing methods utilized.

Regardless, this all means these ideas are difficult to execute because they, fundamentally, are common ideas and simple solutions. At the end of the day, for success, you’re going to need savvy marketing.

How to build a generalized application.

When we’re dealing with a generalized application, we hyper focus on your branding and marketing features. We’re not marketing experts, we do know some, but we’re pretty up front about your exposure.

It’s a conversation we have a lot, essentially, look, it's peanuts for us to build your app, but the leverage here is going to be your branding and marketability. We typically then bring up incorporating a lot of share-ability into your application. I’ll elaborate.

For example, let’s say you're making a review website for restaurants that aims to allow for natural and conversational reviews unlike Google and Yelp reviews.

Nothing crazy on our end, we’ll plug into the Google API, or Yelp API to get a list of restaurants, make a sleek design and allow people to sign up and leave reviews - cool. 

However, let’s talk about share-ability here. Remember the entirety of your application is going to be based on people basically saying “Yea those Google reviews are super sanitized - take a look at this app that’s more natural.” 

Ok, so let’s definitely incorporate a share button that allows users to copy a link and share it via their social media, for example.

But, let’s go deeper, at its core what do people want and what do you need to do to make it more valuable than searching a review up on Google? 

Well, there’s a bunch of things, first you need to break your user’s habit of searching for a review on Google. To use your app, they first need to know about it, use it, and then trust it and then come to the conclusion that it’s better than Google reviews. 

Accessibility comes into play here, that restaurant search needs to be quick and accurate. Design comes into play here, you need to emphasize the conversational nature of your application; maybe taking inspiration from a design like Reddit. Download links and ease of use will also play a key role.

I can go on all day long, but, that’s your leverage, marketing focused features on the technical side and savvy marketing on the business side. 

We can help with the first part, the second part comes down to you, what you’ve seen, who you’ve talked to, and what feedback you get. 

Tying all of this together, you’re typically going to be facing basically one of two hardships: it’s either going to be hard to implement and develop a solution for, or it’s going to be hard to compete.

We always like to understand the landscape of what we’re trying to build and like to offer insight where we can. We cover a lot of this in our discovery phase and throughout our process.

If you would like to discuss your specific idea or tech needs more in depth, let’s talk! Set up a discovery call today.

Let’s get started! Book a free consulting call:

Benmore Technologies Discovery Call

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