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Cyber · AI · Digital intelligence

Applied AI & agents

Turn a business use case into a useful, controllable AI system compatible with your confidentiality requirements.

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We start from real work: available information, decisions to support, repetitive tasks, acceptable error and sensitive data. Prototyping follows framing, with explicit verification and human accountability.

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What the assignment should give you

  1. 01Use cases ranked by value and feasibility
  2. 02A measurable prototype rather than a vague demo
  3. 03Clear rules for data, control and limitations

How the assignment works

How the assignment works

A short, explicit process adapted to the sensitivity of the situation.

  1. 01

    Scoping

    We define the decision to support, relevant assets, constraints, authorised sources and required confidentiality.

  2. 02

    Investigation

    Collection and analysis follow the agreed scope. Facts, hypotheses and areas of uncertainty remain clearly separated.

  3. 03

    Prioritisation

    Each finding is assessed by reliability, likely impact and the effort required to act.

  4. 04

    Delivery

    You receive a readable synthesis, useful evidence and a roadmap that can be put into practice.

Possible deliverables

Possible deliverables

  • AI opportunity map
  • Assistant, RAG or agent prototype
  • Test set and quality protocol
  • Usage, privacy and governance framework

Common situations

Common situations

  • Search across an internal knowledge base
  • Request qualification or case synthesis
  • Controlled automation of repetitive work

Before the assignment

Frame a useful decision

The starting point is a task, its volume and the cost of a wrong answer or action. Scope defines accessible data, tool permissions and moments when a person must approve or take over.

Information to gather

  • Real examples of successful and failed tasks
  • Documents, access rights and retention constraints
  • Quality criteria and acceptable error costs

Decisions to prepare

  • Is a simple assistant, document search or agent needed?
  • Which actions require approval?
  • Which tests block release?
  • Can the previous configuration be restored?
  • How can confidential documents be avoided in testing?
  • Does Stop undo every action?
  • Can valid JSON contain an error?
  • Does synthetic mean anonymous?
  • Is a high score enough?
  • Does a citation found in the new version confirm the answer?

Illustrative assignment

Situation
A service wants to automate request triage while retaining approval for replies and account changes.
Work
Compare a human-labelled baseline with the assistant on representative, ambiguous and adversarial cases; restrict tools and trace refusals.
Deliverable
A versioned test protocol, permission matrix, human approval thresholds and production stop criteria.

Example acceptance criteria to agree during scoping

  • Useful and forbidden tasks have distinct test cases.
  • An untrusted source cannot broaden tool permissions.
  • Shutdown criteria and human handoff are exercised.

FAQ

Frequently asked questions

Practical answers to help frame the need before an initial conversation.

Do we need to choose an AI model or platform first?

No. The technical choice follows analysis of the need, available data, acceptable error, confidentiality constraints and operating cost.

How do you measure the reliability of an assistant or agent?

Representative test cases measure accuracy, traceability, expected refusals and critical errors. Results define human controls and appropriate limits of use.

Services

01Strategic intelligence & OSINT 02Cyber exposure review 04Due diligence & digital trust 05Secure, high-performance web 06SEO, AEO & GEO visibility

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Cyber Intelligence Embassy presents its expertise and publishes analyses. Requests for services or quotations go through Française du Numérique.

Discuss your needs with Française du NumériqueSpecify the expertise you need and the relevant Cyber Intelligence Embassy page.