Multi-model routing and fallback gateway

Reduce model spend and failed requests while keeping one integration. For product and platform teams running AI features in production, convert their prompts, quality rules, latency budgets and cost limits into a reviewed routing policy with automatic fallback. The benefit is a testable hypothesis, measured through cost per accepted response, fallback success rate and quality pass rate against a documented baseline; do not assume that AI output alone produces business value.

Confirm the buyer's problem and scope, collect their prompts, quality rules, latency budgets and cost limits, then follow this sequence: 1. Connect many AI models through one account. 2. Accept plain-language prompts and structured requests. 3. Select the most appropriate model per prompt automatically. 4. Route on cost, latency and quality trade-offs. 5. Switch to another model on timeout, overload or bad response. Resolve uncertain cases with qualified reviewers, approve the reviewed routing policy with automatic fallback, and measure cost per accepted response, fallback success rate and quality pass rate against a documented baseline.

How the AI works

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the routing, comparison and fallback modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One fixed provider set and approved model pool; final routing policy and quality thresholds remain under named human ownership. A model suggestion is never a verified fact, professional decision or authorization to act.

Safeguards

Preserve prompt confidentiality, source attribution, data residency and usage permissions. Named owners approve routing changes and production scope. One fixed provider set and approved model pool; final routing policy and quality thresholds remain under named human ownership. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.

What to build first

Pilot scope: One fixed provider set and approved model pool; final routing policy and quality thresholds remain under named human ownership. Implement one approved input format, a bounded representative prompt set and the first two task modules: connect many AI models through one account; select the most appropriate model per prompt automatically. Support the third module with operator review: switch to another model on timeout, overload or bad response. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.

What it can connect to

Customer-owned prompts, provider accounts and permitted evaluation data. Cloud logging, alerting, CI pipelines and existing API gateways. Start with file exchange and validate destination specifications before promising direct production cutover. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.

The screens in detail

Primary screens: Routing policy setup, Live request and fallback monitor, Usage and cost dashboard. Use a project list, a central policy editor with per-route rules, and a right-hand panel for model pool, limits and test results. Let users compare models side by side on the same prompt set. Display draft, active and paused policy states. Provide a request log with the chosen model, fallback chain and reason. Make the task-specific outcome reviewed routing policy with automatic fallback visible beside its evidence, review state and value baseline.