AI Control Plane — LLM routing and governance
Eval, routing, cost controls, and governance for every LLM call in your stack.
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As your product ships more LLM features, you need a layer that sits above individual model calls: a place to route by cost and latency, run evals automatically, catch quality regressions, and give finance a real cost number. AI Control Plane is that layer — built on your infrastructure, not a third-party SaaS you're locked into.
What we build
Single proxy layer for OpenAI, Anthropic, Gemini, Bedrock, and local models — swap providers with a config change.
Route calls by latency budget, cost cap, task type, or model capability. A/B test prompts without touching application code.
Automated quality checks on every response: faithfulness, toxicity, format compliance, and domain-specific rubrics.
Token usage and cost broken down by feature, team, and customer. Showback reports and budget alert thresholds.
Per-tenant and per-feature rate limits with automatic provider failover and circuit breakers.
How we Deliver
Best practices for AI Control Plane
Route every LLM call through the gateway from day one
Retrofitting a proxy into an existing codebase is significantly harder than starting with it. The earlier you standardize, the easier observability becomes.
Set cost alert thresholds before going to production
Unmonitored LLM spend surprises finance before it surprises engineering. Budget alerts cost nothing to configure and save real money.
Treat model changes as config, not code
Hardcoding provider names and model IDs in application code creates coupling that makes A/B testing and failover painful.
Run evals on 100% of traffic in staging
Sampling in staging hides systematic failures. Reserve sampling for production where volume makes full coverage impractical.
From Evolve Edge
“We don't ship AI without an eval harness. Not because clients ask — because it's the only way to know the system is actually working in production.”
FAQ
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