Case Studies

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

A personal-twin product that replicates a specific person's voice, tone, and conversational style. The hard part wasn't fine-tuning — it was quantifying authenticity. Oumi let Original Voices design custom LLM judges over proprietary persona data; the authenticity pass rate climbed from 52% to 83% (+31 points) through better evaluation rubrics alone, with a 64% failure-row reduction in 73 minutes.

“Oumi enabled us to take our proprietary data and easily create and run custom evaluations on any model. We were also able to start training on our data in minutes.”

— Vedad Šoše, CTO & Cofounder

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

Specialized clinical NLP for the messy reality of medical records — OCR'd notes, templated forms, scanned PDFs. Wired Informatics classifies medical terms across concept validity, clinical category, and clinical applicability without sending patient data to third-party APIs. With Oumi, concept-validity precision climbed from 84.5% to 88.9% on clinical text.

“Oumi enabled us to rapidly develop a specialized model for clinical text that delivers high-precision word sense disambiguation — something general-purpose LLMs struggle to achieve. Its modular framework allowed us to move quickly from problem identification to deployment, while integrating seamlessly into our clinical workflows.”

— Murali Minnah, Strategy Officer

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