Use case and quality criteria
A clear scope, representative examples and an evaluation method covering accuracy, coverage, latency, cost and situations where the system should abstain.
Baleoh / Expertise
Useful AI, integrated into your products.
Agents, business assistants, document search and automation: we start with your workflows to build AI your teams can actually operate.
Scope your AI projectYour needs
From a promising demo to a reliable workflow.
A successful demo does not show how a system will handle incomplete documents, an ambiguous request or a sensitive action. Integration means addressing those situations, not simply calling a model.
We first assess whether AI fits the problem and whether the required data is available. Then we build a limited, measurable scope connected to your tools. Retrieval, generation and actions are separated so each risk has appropriate controls.
Deliverables
The model is only part of the system.
Data and integrations
Access to the required documents or APIs, permission rules and traceable sources. The model receives only information that is useful and authorised.
A controlled assistant or agent
A usable interface and clearly scoped tools. Sensitive actions can require human approval and should be auditable.
Evaluation and operation
Regression tests, quality monitoring and cost controls. Known limitations, fallback procedures and conditions for future changes are documented.
Example engagement
Find an answer in your documents.
An assistant searches an authorised document set, proposes an answer with sources and makes it clear when evidence is insufficient. Access follows the user’s permissions, and an evaluation checks responses before a gradual rollout.
An illustrative example, not a client reference.
Method
A process shaped around your project.
Qualify
Identify the expected benefit, risks and data that is actually available.
Evaluate
Compare approaches against representative business examples.
Integrate
Connect the system to existing products, permissions and workflows.
Supervise
Monitor outcomes and test each model or configuration change.
Frequently asked questions
Before getting started.
Do we need to train our own model?
Not necessarily. An existing model, appropriate instructions and retrieval from your data may be sufficient. Fine-tuning is considered only after identifying a need those approaches do not cover.
Does our data have to leave our infrastructure?
That depends on the architecture. Hosting options, providers, retention and data flows are reviewed with you before integration. We do not assume an external service is appropriate for every use case.
Can an agent act without approval?
Only within an explicitly defined scope. We distinguish reversible operations from sensitive actions, with permissions, limits and human approval appropriate to the risk.
How do you measure reliability?
With a set of business cases, expected responses or evaluation criteria, followed by repeated tests. We also examine errors, refusals, permissions and the effects of changes to the system.
Contact
Have a tech or AI project to bring to life?
Whether you are launching a product, evolving an existing system, or putting an AI use case into production, tell us what you need and let us build the next step together.
Prefer to email us directly?
romain.sempe@gmail.com