Prototype data is ambiguous
Identify the owner of examples, use fictional or redacted cases and specify content that must not reach a model.
GenAI feasibility: decide before a proof of concept
An LLM demo looks convincing, but permitted data, expected quality and responsibility for its answers remain undefined.
Test a business use case and decide whether it merits a prototype, needs adjustment or does not justify an LLM.
Services
Problems addressed
A plausible answer is treated as success
Define expected outputs, unacceptable errors and who approves or rejects model recommendations.
Cost per use is unknown
Estimate volume, exchange size and retries; these scenarios are not measured operating spend.
What you receive
- Decision briefAssisted task, admissible data, success and failure criteria, human oversight and a recommendation to proceed, adjust or stop.
- Conditions for a possible pilotProposed architecture, cost scenarios, risks and permissions needed for a distinct experiment.
Scope and prerequisites
Prerequisites and options. One use case, a few permitted examples, a business owner and a data owner. Assess Azure AI Foundry or Azure OpenAI options and their region and access constraints, without deploying them.
Outside scoping. No connection to real data, executable proof of concept or production release without a separate mandate and approvals.
A question about your environment? Tell me about it: I'll define a useful scope with you before starting.
Contact me