Generative AI, wired into the business
Language models put to work inside your product, grounded in your data, guarded by evaluation suites, and priced for production rather than demos.
The practice.
The gap between a generative AI demo and a generative AI feature that earns its place is engineering: retrieval that cites sources, prompts versioned like code, evaluations that catch regressions, and inference bills that stay explicable. That gap is where we work.
We integrate language models into real products, copilots beside your users' workflows, knowledge systems over your documents, content pipelines with human approval gates, on whichever model earns the job, abstracted so the frontier can move without breaking your product.
What we take on.
Typical engagement
RAG knowledge systems
Retrieval over your documents with sources attached, answers users can verify and trust.
How the work runs.
Ground
Retrieval and context built over your real data, with permissions scoped correctly.
Guard
Guardrails, approval gates and evaluation suites that measure quality continuously.
Integrate
The model wired into your product surfaces, not a chat box bolted to the side.
Improve
Traces, feedback loops and a tuning cadence that compounds instead of drifting.
Asked about genai.
The questions buyers raise, answered plainly.
The one that earns it on your tasks. We evaluate options for your workloads and wire the abstraction so models swap as the frontier moves, your product never marries a vendor.
Grounding first: answers come from your data with sources attached, confidence gates route uncertainty to humans, and evaluation suites catch regressions before users do. No system is perfect; this one is accountable.
It is engineered, not discovered: caching, model tiering and prompt budgets keep costs explicable. We report inference spend as a product metric from the first release.
Yes, most of our work lands inside existing products. The integration layer respects your architecture instead of demanding a rewrite.
Generative AI & LLM Integration, frequently asked questions
Put AI to work where it pays.
Tell us the workflow and the constraint. We will say honestly whether AI earns its place, and map the first build if it does.