Use-case assessment
Define users, permitted data, failure consequences and the alternative without AI.
Applied Artificial Intelligence
Proposed service: evaluate focused AI use cases with representative data, measurable criteria and appropriate human oversight.
A scope built around your requirements
An AI pilot should begin with a well-defined task, not a model choice. This proposed scope explores whether classification, extraction or assisted knowledge retrieval can support a specific workflow within its data and risk constraints.
What the scope can include
Evaluation prototype
Compare outputs against reviewed examples and document limitations.
Controlled integration
Design review points, access boundaries and a way to handle uncertain answers.
Proposed deliverables
Feasibility brief
Task definition, data needs, constraints and evaluation criteria.
Reviewable prototype
A bounded demonstration using approved examples and an evaluation record.
Adoption decision
Documented findings, limitations and the conditions for any next phase.
Where this service can fit
Document classification
Assisted internal knowledge search
Routing of incoming requests
From discovery to handover
1. Define the task and risk context. 2. Prepare authorised representative examples. 3. Build and evaluate a limited prototype. 4. Decide whether to refine, integrate or stop.
How delivery would be evaluated
Evaluate task quality, unsupported outputs, latency and operating cost on an agreed dataset. Critical decisions require an appropriate review mechanism; a prototype does not establish production readiness.
Questions before starting
Define the next step for your business
Describe your current systems, the challenge you want to address and the constraints that matter. A focused brief helps establish a practical scope.
Continue exploring
Discover the services and solution concepts behind your next step.