Your model is not the product. Everything around it is.

Yes, we build retrieval-augmented chatbots on Claude or GPT that answer from your documents, product data, and policies, with chunking, embeddings, a vector store, retrieval you can inspect, source citations on every answer, and a refusal path so the bot says it does not know instead of inventing something. Model choice stays behind an abstraction layer, so you are not locked to one provider.

We ground every answer in retrieval, require inline citations, and tune the system prompt so the model refuses when retrieval returns nothing useful, then we prove it with an eval suite of real questions and known correct answers that runs against every prompt change and every model upgrade. Failures are logged with the retrieved context attached, so you can see why the model answered the way it did. Confidence thresholds route the weak cases to a human.

We meter what actually costs you money, usually tokens, generations, or seats, write those events to your database as they happen, and reconcile them against Stripe so plan limits, credit balances, overages, and upgrades all behave correctly, with a usage dashboard your customers can check before the invoice arrives. Metering runs server side, never in the browser. Hard limits and soft warnings are configurable per plan.

Claude and GPT cover most production work, with open models on your own infrastructure when data residency, cost at volume, or latency makes that the better call, and we keep the provider behind an abstraction layer so switching models is a config change rather than a rewrite. Model choice follows eval results, not launch announcements. We benchmark candidates against your own test set before committing.

Yes, and it is usually the same engagement, because an AI product nobody can find or understand does not sell, so we build the site, the docs, and the structure that lets AI search read and cite you, then wire the demo, waitlist, or trial signup straight into your CRM. We run this on our own site. Our free AI Visibility Checker scores how well AI search can read a site, and every result routes into our CRM.

Yes, that is a common starting point: we audit what exists, keep the parts that work, and rebuild the parts that will not survive real traffic, which usually means auth, rate limiting, queueing for long-running jobs, cost controls, observability, and the eval harness the prototype never had. The architecture and deploy flow get documented so your own engineers can take over. Repos and provider accounts are yours from day one.