Retail & e-commerce · A national retail group
Retail: a storefront writing and answering in its own voice
The situation
The group runs AI across the storefront: product descriptions for a catalog that never stops changing, review summarization, and the first line of customer support. Every SKU update and every support conversation was an LLM call, and every call went to the same premium model.
Sales seasons compounded it: the busiest weeks for the business were the most expensive weeks for the AI bill, exactly when the spend was hardest to question.
What moved to Vidman AI
The workloads moved to Vidman AI and were right-sized: catalog copy and review summaries now run on open models matched to the task, with the premium model kept only for the copy that carries a campaign. The integration stayed one OpenAI-compatible endpoint — the same code, different economics.
Support answers were grounded in the group’s own policies and catalog data, so answers read like the brand instead of a generic assistant.
The outcome
- AI spend reduced significantly — and the bill stopped spiking with the sales calendar
- Catalog coverage increased significantly, because cost no longer decided which products got copy
- Support answers improved noticeably once grounded in the group’s own data
Before
One premium model across the entire storefront, bill tracking the sales season
With Vidman AI
Right-sized models per task, seasonal peaks absorbed
Illustrative, not measured.
Facing the same bill?
Estimate your workload on the rate card, or talk to us about the deployment pattern that fits.
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