Absorbing repetitive work without losing control.

We start from a task someone redoes every week, not from a demo. The assistant is bounded to a precise scope, wired into your data, and a human always approves anything binding.

A man in a suit works at a laptop, flanked by transparent floating screens displaying data — Starting pointAutomation & applied AI

Talk about your project See how it unfolds

A clear entry point Shared decisions Deliverables listed

Useful when a repetitive task takes up time that is needed elsewhere.

A team retyping the same data A volume of documents to read or sort Repetitive customer requests

A readable sequence so you stay in control.

Each stage ends in a shared decision. You know what has been settled, what is still open and what that means for the rest.

Choose

One measurable case only: document search, extraction, quoting or support. The rest waits.

Connect

Read-only connection to your documents and tools, sourced answers, a bounded scope.

Set the limits

Human approval on sensitive actions, a log of exchanges and retention rules.

Measure

Answer quality, capped costs, time actually saved — or we stop if the gain is not there.

What you actually walk away with.

Everything produced belongs to you: the code, the credentials, the documentation and the written decisions. Nothing is held back as leverage.

Usually built with

Python PostgreSQL Docker OpenAI Mistral pgvector

A scoped use case

A single task, chosen and described in writing with its data, its limits and how the gain will be measured, or the conclusion that simpler automation will do.

An assistant connected to your data

An assistant with read access to your documents and tools, restricted to the chosen task and citing its sources, so its answers can be checked.

Guardrails and a log

Mandatory human sign-off on anything that commits you, and a log of exchanges showing what the assistant answered or proposed and who approved it.

A GDPR file

A written record of the data processed, the processors involved, including the model provider, and the retention rules, so you can account for the processing under the GDPR.

A cost dashboard

The assistant’s running costs tracked against a cap set in advance and weighed against the time actually saved, so the decision to continue or stop rests on evidence.

Training

A hands-on session with the people who will use the assistant: what it can do, where its limits lie, when to double-check and how to report a wrong answer.