Experiments
Pilots that never reach production
Trials started across several functions, with no common criterion for deciding which to pursue and which to stop. The cost is visible; the return is not.
For executive management and boards of directors
Artificial intelligence enters the company on the company's terms.
We design, build and govern artificial intelligence systems for established companies: from use cases with a measurable return to agents in production, with control, traceability and compliance with the European Regulation.
The problem
The challenge is not harnessing artificial intelligence. It is using it without losing control.
Ungoverned use
Tools used outside the rules
Public assistants used with internal documents, contracts and customer data. The risk stays invisible until it becomes an incident.
Accountability
Obligations that fall on those who use the systems
The European Regulation requires companies to know which systems they use, which risk category they fall into and who is accountable for them.
A system nobody can explain does not go into production.
Solutions
Four areas, one design.
Strategy, systems and rules are designed together. An agent without governance is a risk; governance without systems is a document.
- 01Strategy and use casesWhere artificial intelligence creates measurable value, and in which order to take it into production.
- 02AI agents in operationsOperational work carried out inside existing systems, with a person who approves.
- 03Enterprise knowledgeProcedures, contracts and archives you can query, with the source stated in every answer.
- 04Governance and the AI ActInventory, risk classification, internal rules and accountability.
Commitments
Six commitments that apply to every project.
For every system we build, regardless of the model or provider chosen.
01
A person approves
Every output that leaves the company goes through an accountable person. No exceptions.
02
Declared sources
Every answer states the document it comes from. An answer without a source is treated as a hypothesis.
03
Measured before use
Every system is tested on real cases with expected outcomes, and goes into service only at the agreed level.
04
Every action logged
Requests, sources, actions and corrections are logged. It is always possible to reconstruct why the system acted as it did.
05
Data within the perimeter
Documents and archives stay in environments chosen by the company. No data is used to train third-party models.
06
Owned by the company
Code, configurations, instructions and test cases belong to the company, by contract.
Method
From assessment to operation, with a verifiable outcome at every stage.
01
Assessment
Processes, data and existing systems, mapped with the people who manage them. Use cases are identified, and those that do not hold up are discarded.
OutcomeUse case map and priorities
02
Design
Architecture, approval workflows, success indicators and running costs, defined before any code is written.
OutcomeSpecification approved by management
03
Delivery
Short releases, each tested on real cases against an acceptance criterion agreed in advance.
OutcomeSystem accepted on the company's data
04
Operation
Quality monitoring, action logs, updates as models, processes and regulations change.
OutcomeSystem governed over time
First meeting
A confidential conversation with senior management.
To understand where artificial intelligence can improve your operations, and where it is not worth it. A non-disclosure agreement can be signed before the meeting on request.