What it is
I design multi-LLM routing layers that send each request to the cheapest model that can actually handle it, with automatic fallbacks so nothing breaks at 2am. Classification tasks go to a fast small model. Complex reasoning hits the frontier model. Everything in between gets matched intelligently — with full cost instrumentation so you can see exactly what’s running and what it’s costing you in real time.
Who it's for
Teams whose LLM bill is climbing faster than their actual usage. Founders who defaulted to GPT-4 for everything because it was the fastest path to launch, and now need to optimize for real. Anyone running production AI who’s never actually measured cost per task.
What you get
- A routing layer with per-task model selection and automatic fallback logic
- Prompt-cost instrumentation and usage dashboards you can actually read
- Caching strategy for high-frequency, low-variance queries
- Typical result: 40–60% lower model spend with no quality drop on the tasks that matter
Common questions
Only where it makes sense to. Hard reasoning tasks, complex generation, anything where quality is the whole point — those stay on the strong model. The bulk of cheap, repetitive calls move down the chain. You see the eval results before anything ships.