Applied artificial intelligence

We build AI systems that run in production, wired to your data and your rules. A proof of concept is easy; what survives the first month of real use is not.

Almost every company has tried a chatbot and stopped there. The pilot impresses in the demo and fails on the real case: data spread across three systems, rules nobody ever wrote down, a cost per conversation nobody measures, and no way to know why the model answered what it did.

AI inside the process: agents that carry out tasks through controlled tools, triage and extraction automation, search over your document base, and models integrated into the software you already have. With a record of every run, a cost ceiling, and a person in the loop wherever there has to be one.

It makes sense when repetitive work already occupies people full time, when the information exists but nobody can find it, or when you tried AI in a pilot and could not get it to production.

Agents with tools

Agents that act, not just answer: they query the database, call your API, open a ticket, update a record. Every tool is declared, bounded and audited — the agent does nothing you did not authorise.

WhatsApp chatbot with AI

Support on the WhatsApp Business API, wired to your systems. It answers what it can, looks up what it needs, records what was agreed, and hands over to a person at the right moment.

Search and questions over your own base

Contracts, manuals and history become something you can ask in plain language, with a source cited in every answer. An answer without a source cannot be checked, and what cannot be checked does not get used.

Process automation

Classifying, extracting from and routing documents, email and messages. What is now someone reading and forwarding becomes a flow, with exceptions escalating to a person instead of disappearing.

AI inside your product

Bringing generation, summarisation and suggestion into the software you already sell, with cost control per user and defined behaviour for when the model provider goes down or slows to a crawl.

Evaluation and cost

A set of test cases to measure whether a prompt or model change made things better or worse, and cost measurement per run. Without that, changing anything in AI is an expensive guess.