LLM
Services

We start from the job to be done — the questions your team answers repeatedly, the documents somebody reads and summarises, the re-typing nobody enjoys — and score each candidate by volume, tolerance for error and how you would measure success. Where a database lookup, a rule or a report would do the job better and cheaper, we say so before you spend money on a model.

We map what the model must know: product and price data, policies, past support tickets, PDFs, internal wikis, and how often each source changes. That tells us what can be retrieved at query time, what needs cleaning first, and which fields must never be sent to a model provider at all.

The first working version uses careful prompting plus retrieval over your own content with pgvector embeddings — deliberately no fine-tuning, because it is rarely the cheapest way to fix a wrong answer. You get a prototype you can interrogate with your own awkward questions in weeks, not quarters, and a clear read on whether the idea holds.

We write down what counts as a correct answer — a graded set of real questions with expected behaviour — and run it against every prompt, retrieval or model change. Guardrails land at the same time: citation of sources, refusal rules when evidence is missing, limits on what the assistant may reveal about its own workings, and hard blocks on returning another customer's data.

Tool calling connects the assistant to live systems so it reads real orders, stock and records instead of guessing, and it ships where your users already are: a web widget, an internal dashboard, WhatsApp or Instagram. Irreversible actions stay behind human approval — the connection layer itself is covered in detail on our GPT & Claude API Integration page.

Every request is logged with tokens in and out, so cost per conversation and per feature is visible from the first day rather than at the first invoice. When a provider deprecates a model or releases a better one, we re-run the evaluation set on the candidate, compare quality and cost, and switch behind a configuration change with a rollback path.