Make your AI useful.
Then make it dependable.

When the system runs, but the result is wrong.

An AI workflow can return a response every time and still fail its purpose. The references may be irrelevant, the answer may invent capabilities, or the agent may fail when a task crosses into another system.

Recovery starts by making that failure specific. We look at the expected result, a representative failing example and the context in which the system is operating.

What we can investigate

RAG chatbot consulting and answer quality

Retrieval relevance, the material supplied to the model, citation alignment, unsupported claims and the boundaries of what the assistant should answer. A response with references still needs those references to support its claims. Explore our RAG consulting and chatbot recovery or read the guide to AI hallucinations and practical investigation.

Agents and connected workflows

Task completion, tool access, state across steps, failed handoffs, retries and the point at which human review is required. The practical question is whether the full workflow reaches an acceptable outcome.

Production reliability and cost

Failures under actual usage, latency, model and tool costs, integration dependencies and whether the use case is viable at the volume you expect.

What a recovery engagement should establish

• The failures that matter to users and the business.
• Which parts of the system contribute to those failures.
• What should be changed, retained or investigated further.
• How the repaired behaviour will be checked against agreed examples.
• What your team needs to operate and maintain the result.

Start with an example, not a complete specification.

A question with a bad answer, a workflow that stops halfway or a recurring cost problem is enough for the initial discussion. We can then agree what access and investigation are proportionate.