Rivac LabsAutonomous Optimization Architect
Systems that call multiple LLM APIs need to route intelligently between them based on cost, latency, and quality — otherwise you're either overpaying for a frontier model on trivial requests or under-serving complex ones with a cheap one. We design intelligent LLM routing with shadow testing, so new routing rules are validated against real traffic before they affect production, cutting cost without silently degrading output quality. This is for teams running meaningful LLM API spend across multiple providers or models who suspect they're not routing optimally. You get a routing layer with the cost savings measured, not assumed.
How We’d Approach This
A clear, staged plan — not a black box
- 1
Analyze current API usage patterns and costs to find where cheaper models could serve requests just as well.
- 2
Build a pilot routing rule and run it in shadow mode against live traffic without affecting real responses.
- 3
Review shadow-test results — cost, latency, quality — with your team before promoting the rule to production.
- 4
Roll out validated routing rules to production with ongoing shadow testing for future rule changes.
What You Get
Deliverables from this engagement
- Intelligent LLM routing layer in production
- Shadow-testing results validating cost and quality trade-offs
- Cost savings report comparing before/after API spend
- Routing configuration documented for future model additions
Six Ways We Could Architect This
Different engagement, different build — pick the shape that fits
There’s more than one way to deliver on this service. Browse a few of the ways we’d structure the work, depending on your speed, budget, and integration needs.
Ready to get started?
Tell us what you’re trying to get done and we’ll help you find the highest-leverage place to start — scoped small enough to prove itself before you commit to anything bigger.
Talk to us about Autonomous Optimization Architect