AI and LLM Integration

That is a per-task question, not a per-company one. Cheap fast models handle classification and extraction; larger ones earn their price on reasoning and long-context work. We benchmark on your data, and because the answer changes as models ship, we build the switch in.

Not under the API terms we build against — Anthropic and OpenAI both exclude API traffic from training by default. If you need data to stay in a region or never leave your own infrastructure, that constrains the model choice, and we scope it that way from the start.

Almost always retrieval rather than the model. If the right passage never reaches the prompt, no model recovers from that. We fix it with better chunking, hybrid keyword-plus-vector search, and reranking, before touching the prompt.

Yes. Most of the time this is additive: a widget that calls a retrieval endpoint over your existing content, with no changes to the site itself beyond embedding it. The scoping question is what it should be allowed to answer, and what it should hand off to a person.

A focused feature — a single assistant, a retrieval layer over one data source — is typically three to six weeks including an evaluation set. Multiple features, or integration into an existing complex product, runs longer, and we scope each feature as its own phase so you see one working before committing to the next.