Services · AI solutions development
Traditional software projects are slow, expensive and crowded with meetings. We build the AI-first way: a small senior team directing AI agents that write, test and review the code. You get a clickable prototype in days, a launchable MVP in weeks, and a bill that won’t make your accountant sit down.
Sound familiar?
You had an idea. Six months, nine people and a budget that rose like sourdough later, you’re still in “phase two”. Nobody did anything wrong. The model is the problem: every line typed by hand, every decision passed through a chain of hand-offs.
AI didn’t just make developers type faster. It changed who does the typing. That changes how big the team needs to be, how fast it moves and what it costs.
The usual suspects
Founder to product manager to designer to tech lead to developer. By the time it reaches the code, “simple sign-up” has become a fourteen-step wizard.
AI-first: the person who hears your idea is the person who builds it, and the spec the agents work from is the one you approved.
Months of building behind a curtain, then the unveiling. The market has moved, and so have your customers.
AI-first: you click a working prototype in the first week and change course while it is still cheap.
Billed by the hour, staffed by the head. Every new person adds cost, and every hand-off adds hours.
AI-first: a small team and fixed-scope stages. You know the price of each step before it starts.
Nine people means 36 pairs who need to stay in sync. Stand-ups, syncs, retros, and the meeting to plan the retro.
AI-first: three people, three conversations. The agents take their instructions from the spec, not from a calendar invite.
The deploy lives in someone’s head. They go on holiday; your release goes with them.
AI-first: everything the team knows is written down where people and agents both read it: decisions, runbooks and tests.
QA waits at the end of the line, so bugs are found when they’re most expensive to fix.
AI-first: agents write the tests alongside the code and run them on every change. The rules of your product are checked automatically, every time.
How we got here
Each shift made software faster to ship. This one makes it faster and cheaper to build, because the people stop being the bottleneck on typing.
Write the whole plan up front, build for a year, unveil it, hope. Like ordering a wedding cake by mail.
Two-week sprints, stand-ups and a wall of sticky notes. Faster, but every line of code is still typed by hand.
Servers by API, deploys every day. Shipping got cheap; building stayed expensive.
Autocomplete for code. The same nine-person team, typing a little faster.
AI agents write, test and review the code. A few senior people decide what to build, design how, and check the agents’ work.
Speed
Typical timelines, traditional team vs AI-first team
| Milestone | The traditional way | The AI-first way |
|---|---|---|
| A clickable prototype | 6 weeks | 1 week |
| A launchable MVP | 5 months | 8 weeks |
| Version 1.0 in production | 12 months | 5 months |
Illustrative, typical timelines for a small web or mobile product. Your real plan comes after a discovery call, and it is written down before we start.
Most of a traditional schedule is waiting: for the next hand-off, the next sprint, the next review. AI agents don’t wait. They draft the code, run the tests and fix what failed while the team reviews the last change.
The team
The traditional digital team is an org chart. The AI-native team is two or three senior builders, each directing a swarm of AI agents that write, test and review the code.
{ hand-offs, stand-ups, tickets }
{ senior builders + an agent swarm }
Cost
Fewer people, for fewer months. Cost falls with both, so it falls a lot.
8 people for 5 months: product, design, a tech lead, front end, back end, data, DevOps and QA.
3 senior people for 2 months, directing AI agents, plus AI tools and compute.
Illustrative MVP. Assumes a blended $12,500 per person-month for both teams. Traditional: 8 people for 5 months. AI-first: 3 senior people for 2 months, plus about $5,000 of AI tools and compute.
What we build
Strategy, design, engineering, AI and deployment from one small team. Start wherever you are.
From napkin sketch to a clickable prototype you can put in front of customers this week.
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Two or three senior builders and a swarm of AI agents, instead of a ten-person org chart.
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The process: spec first, prototype early, test everything, and ship in any language.
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Context, skills, loop and graph engineering: how we make AI agents reliable.
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Assistants, agents and search over your own data, built on a stack that holds up in production.
Read moreWho it’s for
Get an investor-ready prototype and a launchable MVP without hiring a whole engineering team first.
Turn the spreadsheet everyone depends on into a real app, or add AI to the product your customers already use.
Move from “we should do something with AI” to something shipped, without a twelve-month transformation programme.
Add an AI-native pod beside your team to clear the backlog, and leave your people the skills and the playbook.
Give portfolio companies a fast, senior build partner and a repeatable way to put AI to work.
Run the pilot that actually ships: a working product with real users, not another slide deck.
Why VeraGen
VeraGen’s own twenty apps are designed, written, tested and shipped by a small team working with AI agents. The process we sell is the one we use on ourselves.
Ways to work with us
Fixed price, about a week
Fixed scope, fixed price
A flat monthly fee
For your own team
Why now
McKinsey estimates generative AI could add $2.6 to $4.4 trillion a year to the global economy, and that activities taking up to 30% of hours worked today could be automated by 2030.
The companies that win are rarely the ones with the biggest teams. They are the ones that rebuilt how they work first. You don’t need a transformation programme to start: you need one product shipped the new way.
Sources: McKinsey & Company, “The economic potential of generative AI” (June 2023); McKinsey Global Institute, “Generative AI and the future of work in America” (July 2023).
Share of a traditional build’s total hours, by phase
| Phase | The traditional way | The AI-first way |
|---|---|---|
| Product | 15% | 10% |
| Design | 20% | 15% |
| Build & test | 60% | 20% |
| Deploy & run | 5% | 5% |
| Total | 100% | 50% |
Illustrative. Hours per phase as a share of a traditional build’s total (100%). The big saving is in build and test, where agents write, run and fix the code and the tests while people review.
Tell us what you want built. You’ll hear back within one business day with questions, a rough plan and an honest view of whether AI-first is the right fit.
