What it is
Language models are good at reading, writing and following instructions. On their own, they know nothing about your company. Generative AI work is mostly the engineering around the model: connecting it to your documents and systems, giving it the right permissions, checking its answers, and measuring whether it is actually helping.
We build assistants, agents and extraction pipelines that sit inside your workflows. The visible part is a chat window or a button. The part that matters is retrieval that finds the right paragraph, guardrails that keep the assistant in its lane, and an evaluation suite that tells you the answer quality went up, not down, after a change.
How we approach it
We pick use cases by one test: can we write down what a good answer looks like and check it automatically? If yes, we can build it, measure it and improve it. If not, we help you reframe the problem until we can.
Every project starts with a set of real questions or documents and the answers your best people would give. That set becomes the evaluation suite. The first version of the assistant runs against it within two weeks. From there we iterate on retrieval, prompts and tools until the numbers are where they need to be, then wire it into your channels, whether that is your website, WhatsApp, Slack or an internal tool.
What you get
A working assistant or pipeline in your infrastructure, with source citations, permission-aware retrieval and a human handover path. An evaluation suite and a dashboard showing answer quality, cost per conversation and latency. Documentation and a runbook so your team can add documents, adjust behaviour and ship changes safely without calling us first.