Real AI features, built into your product. On Claude.
AI is easy to demo and hard to ship. We build production-grade AI into business-critical web apps on top of Claude, with the evals, guardrails, and fallbacks that make it safe for real users, and we prove it works on your data before you commit.
Book a discovery call →AI where it earns its place, not bolted on.
AI in your product
Assistants, semantic search and retrieval over your own data, extraction, classification, summarization, and drafting. Real features your users touch, not a chatbot stuck in the corner.
Agents & MCP servers
We build MCP servers so Claude and AI agents can work with your own systems and data safely, under rules you set. Anthropic's open protocol, applied to the integration work we already do.
Private & self-hosted
When data cannot leave your walls, we run open models on your own infrastructure. Claude for capability, local for sovereignty. You choose the balance that fits.
Why we build on Claude
We build on Anthropic's Claude models by default because they are strong, reliable, and safety-focused, which matters when the AI is doing real work inside a product people trust.
We are not locked to it. Where a client needs open or self-hosted models for data sovereignty or cost, we build that too. Most of the value, and most of the risk, lives in the engineering around the model: retrieval, tools, guardrails, and evaluation. That is what we do well.
AI is easy to demo, hard to ship
- We prove the feature on your real data in a fixed-price Validation Sprint. Measured accuracy, not a slick demo.
- We build evals so you can see quality objectively, guardrails so it behaves, and fallbacks for when the model is unsure.
- We treat cost, latency, and privacy as first-class concerns, not afterthoughts bolted on at the end.
Do we have to use Claude?
No. We build on Claude by default because it is strong and safety-focused, but where you need open or self-hosted models, for data sovereignty or cost, we do that too. The engineering around the model matters more than the badge on it.
What is an MCP server, and why would we want one?
MCP is an open standard for connecting AI assistants to real systems and data. An MCP server lets Claude or an agent safely read from and act in your systems, under rules you set, instead of being a disconnected chatbot.
How do you keep AI features reliable?
With evals that measure quality on your own data, guardrails that constrain behavior, and fallbacks for low-confidence cases. We ship AI that is production-safe, not a demo that breaks the first time a real user surprises it.
Can the AI use our private data without it leaving our infrastructure?
Yes. We can run open models self-hosted so data stays in-house, or use Claude with strict data handling. We recommend the right balance of capability and sovereignty for your case.
How do we start?
With a Validation Sprint that proves the AI feature works on your real data, at fixed scope and price, before any big commitment.
Have an AI feature in mind?
Book a discovery call and we will tell you honestly whether it is ready to ship, then prove it on your data before you commit.
Book a discovery call →