Why we stay small and senior on purpose

We cap client load, not capability

We work with three clients at once. Not because we can't scale — we turn down inbound every month — but because dilution kills the thing people hire us for. When you add junior engineers, project managers, and account leads to smooth growth, you also add translation layers between the person who understands the system and the person who ships it.

That overhead is expensive in any engineering shop. In AI work it's fatal. The gap between "this should work" and "this works in production with your data" is where most projects die. You can't delegate that learning across a chain of standups.

Skill density beats headcount

A 30-person agency can take on more projects. A three-person team of senior engineers can take on harder ones. We choose the latter because our clients already tried the former.

Most AI consultancies grow by hiring middleweight generalists and wrapping them in process. That works for standard web builds. It fails when you're wiring together models that hallucinate, datasets that lie, and product surfaces where 92% accuracy feels broken to users.

We stay small because the engineers writing the initial prototype are the same ones hardening it for production, debugging the edge cases, and sitting in your Slack when something breaks at 9pm. No handoffs. No knowledge loss. No game of telephone between the person who pitched the idea and the person who has to make PyTorch stop eating your GPU budget.

You talk to the engineer, not a PM layer

When you message us, you're talking to someone who writes code daily. Not a producer. Not a stakeholder wrangler. The person reading your question about why the model is drifting on weekends is the same person who instrumented logging, tuned the inference pipeline, and can push a fix before lunch.

This matters more than it sounds. AI projects succeed or fail on a hundred micro-decisions about data shape, prompt design, fallback behaviour, cost-accuracy tradeoffs. If those decisions bounce through a PM → engineer → PM cycle, you lose a week per iteration and the fidelity degrades every hop.

Direct access means we learn your domain faster, catch misalignments earlier, and don't waste your time explaining things twice.

Few clients is a quality signal

If someone tells you they're running twelve AI projects in parallel, they're either not doing the hard parts or they've parallelised themselves into mediocrity. Real systems work takes focus. It takes being able to read your codebase cold on a Sunday because a data pipeline fell over. It takes remembering why you made a specific tradeoff three weeks ago when it starts mattering again.

We take on three clients at a time because that's the number where we can still do that. Where we can still read your Notion, understand your internal vocabulary, remember which stakeholder needs what, and keep enough system state in our heads to make good calls fast.

This isn't artificial scarcity. It's scope discipline. The alternative is we grow the team, add coordination tax, and become the kind of shop where "let me check with the team" means a two-day lag.

What you actually get

You get engineers who've seen this failure mode before. Who know that the demo is 20% of the work and productionising it is the other 80%. Who can tell you on day two that your data isn't clean enough, your accuracy bar is too low, or your timeline assumes the happy path.

You get continuity. The person who scoped the project is the person deploying it. The Slack thread from week one is still in their head in week eight.

You get no bullshit. We don't have a BD team that promises things we can't build, or a delivery team that inherits the gap. If we say it's possible, it's because we've built it or something close enough to know the shape of the problem.

Why this model survives

The consultancy market has spent fifteen years rewarding growth: more heads, more offices, more case studies. AI is breaking that. The teams that win are the ones who can move fast, go deep, and not lose context in handoffs.

Staying small and senior isn't a phase we'll grow out of. It's the model. If we wanted to be a 30-person agency, we'd be optimising for different things — more proposals, more process, more people who've never used a debugger.

We're optimising for the thing clients actually need: engineers who can ship AI systems that work, and who'll still be around when they don't.