For CTOs
The “add AI” mandate, answered with architecture
Last verified: June 2026· CTO engagement
The board wants AI in the product. Your costs are unpredictable, your data may be sensitive, and your codebase wasn’t built for agents. We help you make the calls that hold up — and ship them.
You’ll get architecture, tradeoffs, and concrete numbers — not a transformation narrative. We help you ship now and build the in-house team later, then transition out cleanly.
What’s on your desk
The CEO wants AI in the product
And you’re being asked to commit to a roadmap before you know which bets are real. We help you sequence it so early wins fund the rest.
Costs are unpredictable
AI spend scales with usage in ways finance hates. We make it observable per route, then cut it behind evals, so you can forecast instead of flinch.
Your data can’t leave the building
Healthcare, finance, defense, or EU residency rules close the hosted-API path. We stand up private inference, RAG, and fine-tuning on infra you control.
Build vs. buy is a coin flip
Every vendor says they’re the answer. We give you an honest read — including when the answer is to hire, not to outsource.
How we’d help
The engagements that fit a CTO best — each ships working software and a measurable result.
LLM Cost & Performance Optimization
Production LLM spend usually grows faster than anyone’s ability to see inside it. We baseline it per route, audit prompts, model routing, caching, batching, and fallback chains, then ship cost-aware routing and observability.
A measured cost reduction against a recorded baseline, quality held
Self-Hosted LLM Infrastructure
For data-sensitive teams (healthcare, finance, defense, EU): local inference, RAG pipelines, and fine-tuning workflows on infrastructure you control.
A working private AI stack on your cloud or on-prem
Agentic Development Platform Build
A brat-style multi-agent harness tuned to your stack. Your devs delegate tasks — refactor a module, add tests, fix a class of bugs across the codebase — with safety rails and human-in-the-loop.
A multi-agent harness running against your repo
Migration: Legacy Codebase to AI-Ready
We restructure a messy monorepo so agents work well in it — module boundaries, type coverage, doc generation, test scaffolding, and spec generation from existing code.
A phased restructure that agents can work in
The proof is open source
We deploy the same infrastructure we build in the open.
Let’s pressure-test your AI plan
Bring the mandate and the constraints. We’ll give you a straight architectural read and a sequence that ships value early.
Turning a mandate into a sequence you can defend
An 'add AI' mandate arrives without a definition, which is the real difficulty. It can mean a feature in the product, a productivity programme for engineering, a cost position, or a signal to the market, and these require almost entirely different work. The first useful move is to make the board's actual objective explicit, because committing engineering capacity to the wrong reading of the mandate is the failure mode that costs a year rather than a quarter.
Build versus buy deserves a sharper frame than it usually gets. Buy the things that are undifferentiated and well served — model access, observability platforms, coding tools themselves. Build the things that encode something specific about your company: how agents reach your internal systems, what your quality bar actually is, how your codebase is structured. The error in both directions is expensive. Building an evaluation platform from scratch is a distraction; buying a generic integration layer for systems only you have produces something nobody uses.
The commitment question underneath all of this is how much you are locking in. Model providers, frameworks and tooling in this space are all moving, and a design that assumes any particular one is permanent will need reworking. The practical hedges are cheap and worth taking: keep model calls behind an interface you control, keep prompts and evaluation data in your own repository rather than in a vendor's console, and avoid building on a capability that only one provider offers unless the advantage is genuinely decisive. None of that is expensive to do up front and all of it is expensive to retrofit.
Finally, be careful what you put in front of the board. A confident productivity percentage is the most requested artefact and the one most likely to damage you in two quarters, when someone asks whether the roadmap moved by the same amount. A directional read with its caveats stated survives contact with the follow-up question, and it preserves the room you will need to keep investing.