Who this is for
- Product teams moving beyond a prototype
- Companies adding AI to an existing platform
- Founders building an AI-native product
Workflows
- Product discovery and feasibility
- AI-native web and mobile applications
- Model and data integration
- Production rollout and managed improvement
What we need
- Target workflow and users
- Representative data
- Existing product and API context
- Security and deployment constraints
What you receive
Validated product scope
Production application and integrations
Evaluation and observability suite
Handover and operating documentation
Acceptance and handover
Agree task completion criteria, an error taxonomy and a representative test set. Handover includes application code, deployment instructions and ownership of monitoring. Infrastructure charges, model usage and ongoing support are scoped explicitly rather than assumed included.
Integration and deployment
Identify the existing identity provider, application APIs and data owners. Agree versioned interfaces and failure responses before integrating the model into the product.
Choose a hosted, private-cloud or on-premise boundary around data and operational requirements. Include model usage, infrastructure and support ownership in scope.
Security and human review
Define what users can verify, correct or escalate. Gate releases on task quality, access-control tests and recovery behaviour rather than a successful demo.
Boundaries
Model capability does not replace product discovery
Acceptance criteria require representative evaluation data
Timelines depend on integration and governance scope