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Area · AI, agents and automation

Hand a repetitive task to a governed AI agent.

Your teams copy information, sort requests or summarize documents every week. We determine what an agent can take over, which data it may use, which actions it may trigger alone and what must still be approved by a person.

Quebec · Canada · Sovereign AI

  1. 01The questionAsked in plain language
  2. 02Your documentsThe assistant searches yours first
  3. 03The answerWith the source shown beside it
  4. 04Your approvalBefore any action that counts
A question travelling through four stages: the question, the search inside your own documents, the answer with its source, then human approval before any action.
The assistant does not answer from memory: it searches your documents, cites its source, and leaves the decision to a human.

What we often hear

These sentences come up often about AI.

  • “We tried it, but we dare not use it for real”

    The demo went well. Nobody knows who would answer for it if the tool got a customer file wrong.

  • “It makes answers up”

    The assistant answers confidently to questions it has no answer for, and nothing signals when to be careful.

  • “It does not just answer: it acts”

    An agent connected to your tools can write, send or change things. Nobody knows what it may trigger alone, which documents it touches along the way, or where they end up.

What we do

Five concrete AI workstreams.

An assistant answers; an agent acts in your tools, under permissions. We start with five concrete needs: use your documents, give the agent its tools, bound what it may do, automate without AI when that works better, and measure the results.

  • Answering from your own documents

    RAG · retrieval-augmented generation

    The agent searches your own content first, then answers while showing its sources. When it finds nothing, it says so instead of inventing.

  • Giving the agent its tools

    MCP · Model Context Protocol

    An open protocol lets an agent query your business applications within bounds, without rebuilding a bespoke integration every time.

  • Setting what the agent may do alone

    Guardrails and permissions

    Read, suggest, write, send: each action is explicitly allowed, and the sensitive ones go through a human approval.

  • Automating without AI when that is better

    The right tool for the case

    Many repetitive tasks are solved by plain automation: more reliable, cheaper, and easier to explain than an agent.

  • Measuring once in service

    Tracking after rollout

    Cost per request, error rate, share of cases where a human had to correct. Without measurement, nobody knows whether it helps.

Agent or plain automation: how to choose?

Use cases

What an AI engagement looks like.

  • An assistant on your internal procedures

    Gain: hours of searching

    Your HR, technical or quality documents become searchable in plain language, with the source shown next to every answer.

  • Triage of incoming requests

    Gain: response time

    An agent classifies, summarizes and routes every email or form to the right person. It drafts the reply; the final decision stays human.

  • Document extraction

    Gain: no more re-keying

    Invoices, purchase orders or contracts: the useful fields are extracted and checked, and doubtful cases are set aside for review.

Next step

Let us talk about your first use case.

Describe a task that costs time every week; we will work out whether an agent is the right answer, or whether something else is.

Discuss your needs