Model Routing, Caching & Resilience

Model routing and resilience architecture sends each task to an appropriate model, reuses stable computation where safe, and provides tested fallback behaviour when a provider or component degrades.

Matchpoint approaches AI as an operating capability with accountable owners, explicit decision gates, measurable acceptance criteria, documented architecture and a practical path from discovery to production.

A production AI service should respond deliberately when models, providers, tools or retrieval components degrade. We map task classes to capability, privacy, latency and cost requirements, then define routing and fallback behaviour for each class.

Caching is designed around the stability and sensitivity of the content. Prompt caching, semantic caching, retrieval caching and reusable intermediate results can reduce repeated work, while freshness rules, access boundaries and invalidation protect the workflow from stale or unauthorised reuse.

Resilience testing simulates rate limits, timeouts, partial responses, malformed outputs, tool failure, retrieval gaps and degraded model quality. The expected behaviour may be retry, alternate provider, smaller task, deterministic fallback, human escalation or a clear temporary failure, with state and evidence preserved.