Case studies
AI spend, back under control
Real deployments across eight industries — anonymized, as enterprise work should be. Different workloads, one outcome: the bill answering to the business again, on the model quality it already relied on.
Ad tech
Ad tech: every impression classified, no frontier-model bill
Real-time ad decisions meant an LLM call at staggering volume. Routing landed the routine calls on smaller models and pulled the bill back in.
The routine calls stopped paying frontier prices
Finance
Finance: an analyst copilot the whole firm can afford
Cost had research summarization and drafting on ration. Routing plus a fine-tuned house model rewrote the economics — and adoption stopped being gated.
A rationed pilot became a firm-wide tool
Healthcare
Healthcare: documentation help, on weights the hospital owns
Clinical documentation was eating clinician hours. A fine-tuned model on dedicated GPUs — owned outright — handed those hours back.
Documentation hours returned to clinicians
Retail & e-commerce
Retail: a storefront writing and answering in its own voice
Product copy and support answers ran on one premium model across the entire catalog. Right-sizing the models rewrote the economics.
The bill quit tracking the sales calendar
Legal
Legal: diligence at deal speed, on a model the firm owns
Contract review was throttled by hours and a rented general model. A fine-tune over the firm’s own clause library broke the bottleneck.
Drafts land in the firm’s own clause language
Manufacturing & logistics
Manufacturing: manuals that answer for themselves
Maintenance knowledge sat in binders and inboxes. A fine-tune over the company’s own documentation pulled downtime down significantly.
Downtime pulled down significantly
Media & telecom
Media: moderation that stays level with the feed
Content tagging and moderation ran on a premium model at feed speed. Right-sized open models let the pipeline hold the pace.
The pipeline quit capping publishing
Insurance
Insurance: claims intake out of the queue
Claims intake had adjusters reading every submission end to end. A fine-tuned model on dedicated GPUs drained the queue.
Adjusters begin 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, the same mechanics. Each engagement above pulled at least one of these levers.
| Lever | Before | With Vidman AI |
|---|---|---|
| Cost per answer | A single premium model for every request | The fitting model for each request |
| Rollout | Pilots rationed by the budget | The entire team on the tool |
| Model fit | A generic voice, generic answers | Fine-tuned on your own material |
| Ownership | Capability rented token by token | Weights held outright |
| Scaling | The bill climbs with success | Spend holds flat as volume grows |
Route
Vidman AI Adaptive hands each request to the lowest-cost model that answers it well, so premium models stop performing routine work.
Own
Fine-tuning over your own material produces a model in your voice — and the weights belong to you, never to a vendor.
Scale
Serverless inference and dedicated GPU clusters absorb growth and seasonality, so spend quits tracking success.