For CTOs
The “add AI” mandate, answered with architecture
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 and cut it 30–60%, 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
→Most teams running production AI pay 3–10x what they should. We audit prompts, model routing, caching, batching, and fallback chains, then ship cost-aware routing and observability.
30–60% LLM cost reduction in 4 weeks, documented
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.