LLM-powered features your customers actually use.
Production-grade LLM applications with evals, guardrails, and observability — not demoware.
When the demo works but production breaks
The prototype was magic. In production, it hallucinates, latency spikes, costs balloon, and customer-support tickets pile up about wrong answers.
You shipped something that is now harder to maintain than the deterministic alternative.
We've built and operated LLM apps in production — including high-stakes domains where wrong answers have real cost.
How we build LLM apps
- 01
Scope narrowly
A bounded task an LLM can do reliably — not an open-ended chatbot.
- 02
Ground in your data
Retrieval over your domain content, with citations. Less hallucination, more trust.
- 03
Evaluate continuously
Eval suites catch regressions before deployment. Cost, latency, and quality monitored in production.
What good looks like
A feature your customers use repeatedly because it consistently works — and that you can update without breaking.
Reliable in production
99%+ uptime, predictable latency, controlled cost.
Trusted by users
Citations and refusals where appropriate. No silent hallucinations.
Maintainable
Eval suites, version control, and rollback paths.
Proof
< 2% hallucination rate
Domain-grounded LLM feature with full eval suite and citations. Hallucination rate measured below 2% across production traffic.
— B2B SaaS document tool
Frequently asked questions
Defaults to Anthropic for reasoning-heavy tasks, OpenAI for breadth, with self-hosted options for data residency. We pick by use case, not by loyalty.
Ready to ship a real LLM feature?
Bring us your idea and a sample of the data it would work on. We will scope what is realistic.