How do you stop the model from making things up?
We ground responses in your own content with retrieval, add evaluation and guardrails, and keep a human in the loop for anything high-stakes.
Services
LLM features, retrieval, and autonomous agents built into your product — grounded in your data, evaluated for accuracy, and safe to put in front of users.
Use this when you want AI to do real work in the product — answer from your own content, take actions through tools, or automate a workflow — without shipping something that hallucinates in front of customers.
See related workRetrieval and evaluation keep responses tied to your real content, so the model helps instead of guessing.
Tool use, guardrails, and human-in-the-loop checkpoints let an agent do work without going off the rails.
Evals, tracing, and cost controls make model behavior observable, testable, and affordable to run.
Your content indexed and retrieved so answers stay grounded in real sources.
Tool-using agents that take actions behind guardrails and approvals.
Evals, logging, and traces that make model quality visible and testable.
Rate limits, fallbacks, and budgets that keep AI safe and affordable in production.
AI work pays off when a model removes real effort or unlocks a feature — and when accuracy and safety are treated as requirements, not hopes.
You have proprietary content or a workflow where retrieval, automation, or an assistant creates clear value.
You want AI added for the label, with no data to ground it and no way to measure whether it helps.
A grounded, evaluated feature, prompt and retrieval infrastructure, guardrails, and cost and quality dashboards.
We map the real problem, the constraints, and what success looks like — before a line of code.
Flows, interface, and architecture decided together, so the build arrives without surprises.
Tight iterations you can see and use every week. No black boxes, no big reveal at the end.
We launch, watch the graphs, and stay on to harden, measure, and keep improving.
The short answers buyers usually need before a scoping call.
We ground responses in your own content with retrieval, add evaluation and guardrails, and keep a human in the loop for anything high-stakes.
We stay model-agnostic — typically the latest Claude, GPT, or open models — and choose per task based on accuracy, latency, cost, and privacy.
We scope access carefully, avoid training on your data, and can keep sensitive workloads inside your own infrastructure where needed.
Tell us what you're making. We'll tell you — honestly — whether we're the right team for it, and how we'd approach it.