AI-native products, and teams that can build them
We design and build products where AI does part of the work, with evals, cost limits and human review planned from the start. And we set up your engineering team to build with AI tools under standards everyone agrees on.
From a working demo to a product you can run
An AI feature that works in a demo still needs answers to harder questions: how good is it, what does each request cost, how fast does it respond, and what happens when the model changes.
On the team side, AI coding tools tend to arrive one developer at a time, without shared rules for what goes into them or how their output is reviewed.
Signs this is you
- An AI prototype works, but nobody can say how well
- The cost per request is unknown, or rising
- Developers use AI tools with no shared rules
- AI-written code is merged without a clear review standard
Two tracks: the product and the team
AI-native products
Products and features designed with AI as part of how they work.
- LLM architecture: which model, where it runs, and how the parts fit
- Retrieval-augmented generation (RAG) over your own data
- Agents with limited, clearly defined permissions
- Evals built from real examples, run on every release
- Cost and latency budgets, set before building
AI-native engineering practices
Your team building with AI tools, to standards everyone agrees on.
- AI coding tools set up for your codebase and standards
- Review and testing workflows for AI-written code
- Guardrails: what may go into an AI tool, and what needs human review
- Hands-on training for your engineers
Four ways to work with us
Advisory
We review your AI architecture, or how your team uses AI tools today, and hand you a roadmap: what to build or change, and in what order.
Build
We build the AI feature: architecture, retrieval, evals and cost limits, against a scope and price agreed up front.
Run
We stay on after launch to watch quality, cost and latency, and re-run the evals when the model or your data changes.
Embed
One of our senior engineers works inside your team while it adopts the practices: pairing, reviewing and setting up the tooling, for an agreed period.
Deliverables, not just a plan
- An architecture decision record for the AI feature
- An eval set and harness, run on every release
- Cost and latency budgets, with monitoring
- Guardrail and AI usage guidelines for your team
- Hands-on training sessions for your engineers
- A handover runbook
Is this the right fit?
Companies building a product or feature where AI does part of the work, and engineering teams who want to use AI tools to an agreed standard. If you need one existing workflow automated rather than a product built, start with AI workflows & automation.
Common questions about AI-native teams
What does "AI-native" mean in practice?
For a product, AI is part of how it works from the start, not added afterwards: its data, evaluations, cost limits and human review are planned in the design. For a team, it means AI tools are used with shared standards for what goes into them and how their output is checked.
Should we use RAG or fine-tuning?
Usually retrieval-augmented generation (RAG) first: the model answers from your own documents, which are easy to update and to check. Fine-tuning makes sense when the evaluations show the model needs behaviour that instructions and retrieval can't deliver.
How do you control cost and latency?
We set a budget for both before building, measure them on every release, and choose the smallest model that passes the evals. Caching, shorter prompts and sending simple requests to cheaper models keep them inside the budget.
Will AI coding tools lower our code quality?
They can, if their output is merged without review. We hold AI-written code to the same review and test standards as any other code, add checks to CI, and train the team on where the tools help and where they don't.
Does our code or data leave our control?
We agree which tools and providers are allowed, what data may be sent to them and what stays out, and check each provider's terms on storage and training. Where that isn't acceptable, we look at options that run in your own cloud account.
Start with an honest look at what you run
Tell us what you're running today and where it hurts. We'll come back with the two or three changes worth making first — and if the answer is that your setup is fine, we'll say that instead.