AI
Solutions

We look at where your team repeats itself — the same questions, the same re-typing, the same document being summarised — and score candidates by volume, tolerance for error, and how measurable the outcome is. You leave this step with a shortlist and an honest note on anything AI is the wrong tool for.

We gather the material the system must answer from: product data, policies, past tickets, manuals, contracts. It is cleaned, chunked, embedded into a vector index, and given an update path so the knowledge base does not silently go stale.

Within weeks you get a working prototype answering real questions from your real content, not a slide deck. Testing against your own material is the only reliable way to see where the model is strong and where retrieval needs work.

We build an evaluation set of questions with known correct answers, including ones the system should refuse, and measure every prompt change against it. Guardrails constrain scope, block disclosure of internals, and force the model to say it does not know rather than invent.

The assistant is connected to the channels and systems where the work actually happens — website chat, WhatsApp or Instagram, an internal dashboard, or your ERP and helpdesk through their APIs. We launch on a narrow scope first and widen it once the logs look clean.

Conversations, escalations, refusals and token costs are logged and reviewed, and the gaps feed straight back into the knowledge base and prompts. Models change quickly, so we keep the system portable and re-test when a better or cheaper one appears.