We force AI models to deliver their best results.
A language model on its own is just one building block. It is the harness around it — structured context, precise control, validation and feedback loops — that turns it into software that delivers reliably. We build exactly that framework: for SMEs and startups, inside their product, all the way to production.
The model is not the product. The harness is.
The same three principles govern every integration, every agent and every project we deliver.
Better answers
Consistent, verifiable results instead of luck.
Structured context, clear rules and automatic validation instead of prompt guessing. The model gets exactly what it needs — and nothing else.
Fewer tokens, lower costs
Curate the context and you send less.
Deterministic routing, caching and lean prompts measurably reduce tokens and latency — with no loss in quality.
Independent of the vendor
The harness logic runs on your side — without an LLM call.
Rules, routing, evaluation and tooling run deterministically in your environment. The model behind it is interchangeable: Claude today, a local model on your own hardware tomorrow.
What we do for you
Three paths to the goal — from the first sparring session to a harness in production. Clearly separated, seamlessly combinable.
AI Harnessing
We build the framework that keeps your AI model under control: prompts, tools, rules, tests, monitoring. So AI runs reliably inside your software — not next to it.
AI Agents & RAG
Agents that make decisions, call tools and take on tasks autonomously — grounded in your knowledge and guided by your harness.
AI Consulting & MVP
We show you where AI has the biggest leverage in your product and prove it with a POC — together with your team, in weeks rather than months.
Your repository grows. Your AI agents lose the plot.
Sherpa turns a codebase that has grown over years into a knowledge architecture your AI assistants can actually use.
Every project that has grown over years carries knowledge that now lives only in people's heads: who knows which module, what changes together, where it keeps breaking. That is exactly the tribal knowledge your AI assistants are missing — and so are you, when someone leaves.
Sherpa reads your git history and your repository deterministically, without a single LLM call, and plans the knowledge architecture for Claude Code, Codex, Cursor, Copilot and friends from it: owner docs, specialised agents, skills, evals. You review the plan, Sherpa sets it up — and gives a reason, backed by numbers, for every proposal and every no.
What sets Sherpa apart
- Any repository — .NET, Python, Node, Go, Rust, Java; in seconds, no setup
- Tribal knowledge becomes structure — hotspots, change coupling and ownership land where your AI tools read them
- Plan → approve → apply, like Terraform — you see every file before it exists; Sherpa owns only its marked blocks
- Measures itself — every run carries a label with the harness version; every change has a number to answer to
$ sherpa plan .harness-plan.yaml — 6 proposals, 4 reasoned no's + owner-doc pay 24 commits/90d ✓ · 40 files ✓ + agent pay rank 1/4 churn ✓ · 2 authors ✓ + skill regenerate-migrations 6 generated ✓ - owner-doc old 0 commits/90d ✗ - librarian core 1 commits/30d ✗→ .sherpa/harness-plan.yamlProjects that make a difference
We are currently working with our first clients on concrete AI integrations. Detailed case studies will follow once the results are in.
Learn moreReady to get more out of your AI model?
No pitch, no sales talk. Tell us where your model is too expensive or too unreliable today — and we will show you in a first conversation what a harness actually changes.