A frontier-model bill that didn't scale.
Global Fashion Brand set out to deliver personalized styling advice at scale through an AI-powered fashion agent. Their existing OpenAI-based solution worked — but at an unsustainable monthly cost, with 20-second response times that undercut the very experience it was meant to elevate.
Scaling the agent to the full customer base meant scaling that bill linearly. The economics, not the capability, were the constraint.
A domain-tuned SLM, on the hardware they already owned.
Bud AI Foundry replaced the full inference stack with a domain-tuned SLM and an LLM-as-judge architecture — CPU-native on existing Intel Xeon, no GPU upgrade required.
A repeatable playbook: migrate enterprise AI agents off costly frontier-model stacks to optimized SLM deployments — equivalent accuracy, dramatically lower TCO.
Before and after.
| Metric | OpenAI (before) | With Bud | Δ |
|---|---|---|---|
| Monthly run-rate | baseline | 80% lower | −80% |
| Response time | 20 sec | 6 sec | −3.3× |
| Accuracy | 85.46% | 85.44% | held |
| Time-to-market | baseline | 6.5× faster | +6.5× |
How the data flows.
"The agent already worked. What we needed was for the economics to work too — so we could put it in front of every customer, not just pilot it. Same answers, a third of the latency, a fraction of the bill."
VP, Data & AI Platform · Global Fashion Brand
The stack behind this outcome.
Put your data on it.
The fastest way to see what an integrated AI operating system does for your enterprise is a proof-of-concept on your infrastructure, with your data.