Building Adopted Agent

Move from pilots to AI agents used daily.

Together with you, we build and deploy reliable and flexible AI agents that are rooted in your existing workflows. By combining user research, robust agency architecture and the selection of the right tools, we secure their transition to production and manage their adoption through usage and impact metrics.

Your main challenges

The pilot's wall: promising agents that remain stuck in experiments.

In many organizations, AI agent projects do not advance beyond the pilot. Unintuitive interfaces, excessive prompt effort, lack of reliability in the face of the variety of inputs: users drop out quickly. The result: investments are diluted and the expected business value never materializes.

Users who are powerless in the face of uses that they do not control.

Even when an AI agent works technically, users don't get value from it. They don't know what they can ask for, they're hesitant about limits, they're worried about making a mistake or “misusing” the agent. Without support, adoption remains marginal, and the expected gains disappear.

Technological complexity that leads to fragile and expensive agents.

The multiplication of frameworks, models and tools makes technological choices difficult. Without dedicated expertise, architectures quickly become unstable or oversized: high inference costs, irregular performances, impossibility of evolving the agent. This lack of robustness blocks scalability and puts teams at risk.

Our approach

Anchoring the agent in real uses.

We identify concrete situations where an AI agent can help: existing workflows, irritants, user expectations. This step avoids above-ground projects and ensures that the agent meets a real need.

Quickly test a first version on real cases.

Based on real situations (customer requests, tickets, files, code examples, etc.), we gather concrete cases to test the agent. A first simple version is evaluated on these cases, and makes it possible to immediately identify where he is wrong, what he does not understand and what needs to be improved.

Help the agent grow by relying on the errors observed.

We enrich the agent step by step: adding capabilities, improving responses, integrating business tools. Each iteration is based on real cases where the agent is wrong, until a sufficient level of reliability is obtained for production.

Deploy in production and manage adoption over time.

We put the agent in a real situation quickly, monitor usage through impact metrics and collect feedback from teams. We clarify the guidelines for use and adjust the agent to promote sustainable and measurable adoption.