Case Studies

Real-time guardrails that beat GPT-4.1 Mini by 4% in accuracy with 50% fewer false positives. Ada's AI customer service agents must stay on-policy across fintech, e-commerce, SaaS, and travel. From just 20 labeled examples, Oumi generated a synthetic dataset spanning 250 SOPs and thousands of conversations, then fine-tuned Qwen3 4B into a sub-second adherence classifier Ada fully owns.

An AI-first CRM scaling research without paying frontier-model rates. Aurasell's research agent extracts structured insights from web search results — Sonnet 4.5 hit cost and latency walls as the customer base grew. Oumi built a custom 8B Qwen3 model that outperforms Sonnet 4.5 by 8% in coverage and 12% in groundedness, approaching Opus-level quality at a fraction of the cost.

Divisions Maintenance Group coordinates facility maintenance across thousands of properties — contractors submit invoices that must be validated for formatting and reasonable charges. A 0.6B Qwen3 model fine-tuned on a synthetic data recipe lifted validity accuracy from 72% to 99% and appropriateness from 52% to 91%, beating frontier GPT5.2 by 6% on both — at 100× lower cost, and small enough for edge deployment.

“Every job we handle is bespoke — even the same HVAC unit breaking down twice runs differently. I'm convinced our future is to have our own fine-tuned models. The results have only gotten better.”

— Kumar Srinivasan, Chief Product Officer

Custom AI for the institutions that can't afford to get it wrong. Frontier models failed 50% of code translation tests. A top-5 U.S. bank is modernizing 100 million lines of legacy code. Open-source models delivered 85% of Sonnet 4.6's quality on codebase comprehension — no proprietary code ever left the bank's environment.

“It's pretty powerful if I can take that model… when I deploy it to production, that data's not going anywhere. The cost and deployment model you guys offer is kind of ideal for an enterprise.”

— Head of Modernization Architecture, Top-5 U.S. Bank

20% higher quality. 70% lower cost. — permanently replacing frontier LLM APIs. A custom vision model extracts structured patient data from medical records in real-time across 30 practices and 3 systems, scaling to 80+. $2.3M in annual savings. GPT and Claude delivered inconsistent results on specialized formats.

“We spent three months trying to fine-tune internally — the infrastructure was quickly obsolete. With Oumi, the same team ships production models in minutes. We've permanently migrated away from LLM APIs.”

— ML Engineering Lead, Healthcare Provider

100× cost reduction on high-volume claims triage. $0.10 per classification. Not $10. Custom models trained on your policy schema learn the specific rules, formats, and edge cases your claims require — consistency that frontier APIs can't match at this price.

“We can't keep paying $10 per human review on claims that a custom model classifies for pennies. The accuracy has to be near deterministic — our policy rules don't change based on what the model ate for breakfast.”

— Claims Operations Lead, National Insurer

Specialized small models 26 markets. 20+ languages replacing frontier APIs for real-time sports interactions — from natural-language-to-query on structured databases to multilingual agentic agents running worldwide. Production-ready model. Lower cost. Lower latency.

“Oumi's synthesis recipes took us from schema to 500 training samples in just a few iterations. Controlling data distribution was simple, and evolving from basic to complex queries required only small config changes. The declarative, version-controlled approach enabled rapid iteration and a production-ready model, without manual data creation.”

— Ioanna Sanida, Data Science Team Lead