From Corporate Cards to Finance Control Planes
Your AI runs. Now make it economical.
Sia Inference Labs optimizes the inference layer of your AI estate, selecting the right model for each task to reduce cost and latency while delivering the predictable performance and reliability needed to scale.
The Scaling Problem
Pilots are cheap. Production is not. When an agentic workflow moves from a demo to thousands of daily runs, inference spend becomes a real line item, and most enterprises discover three things at once: they are paying frontier prices for tasks a smaller model handles equally well, they have no visibility into cost per task or per business outcome, and every provider price change or model release forces a manual re-evaluation.
AI budgets do not fail on ambition. They fail on unit economics.
What We Deliver
/ Inference audit. A full map of your AI workloads: which models run which tasks, at what cost, latency, and quality. Most audits find 30% or more of spend sitting on the wrong model for the task.
/ Model routing and orchestration. Architecture that sends each task to the model that clears the quality bar at the lowest cost, with caching, batching, and fallback built in. Multi-provider by design, across OpenAI, Anthropic, Google, Mistral, and open-weight models.
/ Continuous benchmarking. The model landscape moves monthly. We benchmark your actual workloads against new releases and price changes, so your routing decisions stay current without your teams re-testing everything by hand.
/ Cost governance. Token spend dashboards tied to business outcomes: cost per case handled, per document processed, per interaction resolved. Finance-grade visibility, so AI spend is managed like any other production cost.
How It Works
No quality regression is the standing rule. Every substitution ships with evaluation results, and any change that fails your quality bar is rolled back.
Weeks 1-2: Audit.
We instrument your existing workloads and map spend, latency, and quality per task.
Weeks 3-6: Optimize.
We deploy routing, caching, and model substitutions on the highest-volume workloads first, measuring quality before and after every change.
Ongoing: Operate.
Continuous benchmarking and governance as a managed service, or full handover to your teams with the tooling and playbooks in place.
Why Sia
Sia partners with the leading model providers and resells none of their tokens on margin. Our only incentive is your cost per outcome.
Inference Labs works on the systems our Forward Deployed Engineers ship and the estates our clients already run. Benchmarks reflect your workloads, your data, and your constraints.
Backed by 3,000 consultants across 19 countries, we tie inference metrics to the business unit paying for them. The deliverable is a cost per outcome your CFO can read, not a latency chart.
Where Inference Labs Makes The Difference
When AI is in production but its unit economics are not.
- Agentic workflows whose token spend is scaling faster than the value they deliver
- High-volume document, case, or interaction processing running entirely on frontier models
- Multi-provider strategies that exist on paper but not in the architecture
- AI platform teams spending engineering time on manual model evaluation after every release
- Finance and procurement teams asked to budget AI spend with no unit economics to stand on
Get Started
Send us one production workload and its monthly bill. Within two weeks, the audit tells you what it should cost, and what it takes to get there.