Case studies
AI spend, brought under control
Real deployments across eight industries — anonymized, as enterprise work should be. Different workloads, same outcome: AI spend back under control, with the model quality the business already depended on.
Ad tech
Ad tech: classifying every impression without a frontier-model bill
Real-time ad decisions meant an LLM call on staggering volume. Routing put routine calls on smaller models and the bill under control.
Routine calls no longer pay frontier prices
Finance
Finance: an analyst copilot the whole firm could afford to use
Research summarization and drafting were rationed by cost. Routing plus a fine-tuned house model changed the economics — and adoption stopped being gated.
From rationed pilot to firm-wide tool
Healthcare
Healthcare: documentation help with weights the hospital owns
Clinical documentation was consuming clinician time. A fine-tuned model on dedicated GPUs — owned outright — gave that time back.
Documentation time given back to clinicians
Retail & e-commerce
Retail: a storefront that writes and answers in its own voice
Product copy and support answers ran on a premium model across the whole catalog. Right-sizing the models changed the economics.
The bill stopped tracking the sales calendar
Legal
Legal: diligence at deal speed, with a model the firm owns
Contract review was bottlenecked by hours and a rented general model. A fine-tuned model on the firm’s own clause library changed that.
Drafts arrive in the firm’s own clause language
Manufacturing & logistics
Manufacturing: manuals that answer back
Maintenance knowledge lived in binders and inboxes. A fine-tuned model over the company’s own documentation brought downtime down significantly.
Downtime brought down significantly
Media & telecom
Media: moderation that keeps up with the feed
Content tagging and moderation ran on a premium model at feed speed. Right-sized open models let the pipeline keep up.
The pipeline stopped capping publishing
Insurance
Insurance: claims intake that no longer waits in a queue
Claims intake meant adjusters reading every submission end to end. A fine-tuned model on dedicated GPUs cleared the queue.
Adjusters start at review, not at reading
Clients are anonymized in these write-ups; details are available under NDA. Talk to us
Where the value comes from
Different industries, same mechanics. Every engagement above moved at least one of these levers.
| Lever | Before | With Run BiOS |
|---|---|---|
| Cost per answer | One premium model for every request | The right model for each request |
| Rollout | Pilots rationed by budget | The whole team on the tool |
| Model fit | Generic voice, generic answers | Fine-tuned on your own documents |
| Ownership | Capability rented by the token | Weights you own outright |
| Scaling | The bill grows with success | Spend stays flat as volume grows |
Route
BiOS Adaptive sends each request to the lowest-cost model that answers it well, so premium models stop doing routine work.
Own
Fine-tuning on your own documents produces a model in your voice — and the weights belong to you, not to a vendor.
Scale
Serverless inference and dedicated GPU clusters absorb growth and seasonality, so spend stops tracking success.