Productized engagement
AI → SI Production Accelerator.
Turn a selected RAG or agent workload into an operable release candidate with explicit evaluation, security and runtime controls. MCP is considered only when the workload needs a bounded tool interface.
Who it is for
- Product, engineering and AI teams with a working prototype that needs a path through production review.
- Enterprise architects who need operational, security and cost considerations addressed before release.
Problems addressed
- Prompts, models, retrieval settings and deployments change without repeatable evaluation or release controls.
- RAG retrieval, agent tools and data access lack clear authorization, isolation or audit boundaries.
- Production owners cannot inspect response quality, failure modes, service health or token consumption.
Typical scope
- Review workload architecture, RAG data flow, agent tools and dependencies; choose a bounded target path.
- Define evaluation datasets and checks for relevance, groundedness, safety and task behavior.
- Implement identity, data access and tool controls; include MCP only where justified by the design.
- Add deployment pipeline gates, runtime observability, operational readiness and cost controls.
Deliverables
- Target workload architecture and documented security and operational decisions.
- Reference implementation or workload changes within the agreed RAG or agent boundary.
- Evaluation harness and release workflow with explicit promotion checks.
- Telemetry and cost instrumentation, operational runbooks and readiness review findings.
Related service: Enterprise AI.