AI that actually fits the way you work.
We help teams ship LLM-powered features and copilots that solve real, measurable problems — not demos. Grounded in your data, evaluated against your workflows.
What we typically build
- Internal copilots
Chat-style assistants over your docs, tickets, code or playbooks — with proper access control.
- RAG & semantic search
Retrieval over your knowledge base, with citations, freshness checks and relevance evaluation.
- Document intelligence
Extract structured data from PDFs, scans, emails and forms — with human-in-the-loop where it matters.
- Agentic workflows
Multi-step LLM agents that act in your systems via tools, with guardrails and audit logs.
- Evaluations & guardrails
Test sets, regression checks, content safety, jailbreak resistance — built in, not bolted on.
- Model choice
Claude, GPT, Gemini, open-weights — picked per task and per cost target. We're not married to any vendor.
Grounded, evaluated, governed.
Most AI projects fail on trust, not capability. Here's how we ship features your team will actually rely on.
We start with retrieval over what you already have — docs, tickets, code or a database — instead of hoping a general-purpose model already knows your business. If it can't point to a source, we don't ship it.
Before writing a prompt, we build a set of real questions and correct answers with your team. Every model or prompt change gets re-run against it, so regressions get caught before your users see them.
Content filtering, PII handling, rate limits and confidence thresholds that route uncertain or high-stakes cases to a person instead of a guess. Every action gets logged.
We launch against one workflow with real users, and only widen the rollout once it holds up against the eval set. From there, access control, retention and model choice stay under your control.
Questions, answered.
No. We don't use your data to train or fine-tune third-party models. If we fine-tune anything, it's scoped to your environment, and we'll tell you exactly what happens to the data before we touch it.
Whichever fits the task, the budget and your data-residency requirements — Claude, GPT, Gemini or open-weights models. We build so the underlying model can be swapped without rebuilding the feature around it.
We build an evaluation set of real questions and correct answers with your team before launch, and run it against every change. If accuracy drops, we see it before your users do.
Confidence thresholds and guardrails route uncertain or high-stakes cases to a person instead of a guess. Every action is logged, so you can see what happened and why.
Yes. We can deploy inside your cloud account or VPC, tie access to your existing SSO and roles, and keep data inside the boundary you already operate within.
Most pilots run six to ten weeks against a single, well-defined workflow — enough time to get a real read on accuracy and adoption before committing to a wider rollout.
Have a thorny AI use case?
Tell us what you're trying to do. We'll come back with a short, honest read on whether it's a fit for AI, and what the smallest useful version looks like.