LLM Orchestration & Routing
Multi-model routing that matches each request to the right LLM.
What we deliver
We design orchestration layers that route prompts across multiple LLMs based on task type, cost, latency, and quality requirements.
We build LLM orchestration and routing systems that send each request to the model best suited for the job. Instead of locking your application into a single provider, we set up a routing layer that evaluates task complexity, context length, latency targets, and budget rules, then directs traffic to the right model. Our work covers provider abstraction, fallback logic, retries, and graceful degradation when a model is rate limited or offline. We integrate with OpenAI, Anthropic, Google, and open source models hosted on your infrastructure, and we add caching and batching where it makes sense. The result is an AI stack that stays fast, controls cost, and avoids vendor lock in. We document the routing rules and hand over a system your engineers can operate and extend.
Built for teams like yours
Who it's for
- AI product teams
- SaaS platforms with LLM features
- Enterprises piloting multiple models
- Customer support automation teams
- Internal AI platform groups
Pain points we solve
- Single provider lock in
- Unpredictable LLM bills
- Slow response times on complex queries
- Outages from one provider taking down features
- No fallback when models fail
Capabilities
Everything we cover in this engagement.
- Routing rule design
- Provider abstraction layer
- Fallback and retry logic
- Latency and cost benchmarking
- Caching strategy
- Model selection policies
- Rate limit handling
- Integration with existing APIs
Our process
A clear, predictable path from kickoff to outcomes.
Audit
Review current LLM usage and traffic patterns.
Design
Define routing rules and provider mix.
Build
Implement the orchestration layer and adapters.
Test
Run load tests and validate fallback paths.
Deploy
Roll out to production with monitoring.
Deliverables & outcomes
What you get
- Routing architecture document
- Orchestration service code
- Provider adapter library
- Fallback playbook
- Performance benchmark report
- Operations runbook
Outcomes you can expect
- Lower average cost per request
- Reduced provider dependency
- Faster median response times
- Higher uptime for AI features
- Clear operational visibility
What clients say
We were paying three agencies and a lifecycle freelancer to argue over attribution. RevoraOps absorbed all of it in 30 days, killed our worst-performing Meta ad sets, and rebuilt the welcome flow from scratch. CAC dropped 31 percent in the first full month. Honestly the relief of having one weekly call instead of four was worth it alone.
Holiday season was about to break us. We needed 22 agents in six weeks and our internal hiring pipeline could not move that fast. They staffed it, trained on our tone guide, and ran nesting alongside our senior reps. CSAT actually went up by three points during peak. First Q4 in four years my support lead took her vacation.
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Lower AI spend without giving up on quality.
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ExploreFrequently asked questions
Quick answers to the questions we hear most.
Which providers do you support?
Will this slow down our application?
Do we need to rewrite our application?
How do you decide which model to use?
Can we add new providers later?
Need to route across multiple LLMs?
We can design an orchestration layer that fits your stack and budget.