Search that understands “waterproof jacket under $100”.
A small model fine-tuned on your catalog turns natural-language queries into structured filters and ranked results. Self-hosted next to your existing search engine, answering in under 100 milliseconds — at a flat monthly cost, not a per-query API fee.
Natural-language catalog search puts a small model fine-tuned on your catalog in front of your existing search engine. It translates free-text intent into structured filters and a cleaned query in tens of milliseconds, self-hosted in your VPC, while Elasticsearch, OpenSearch, or Algolia keeps doing retrieval and ranking. No re-platforming, no per-query API fee — and we measure the result against your own search logs, before and after.
Shoppers type intent. Keyword search returns the wrong aisle.
“Warm mid-layer for hiking, not down.” “Office chair for a bad back under $300.” Keyword search returns zero results or the wrong category. Frontier APIs parse that intent, but at per-query prices and 1–2 second round trips that don't belong in a search box — so most catalogs either stay on keywords and lose the sale, or pay frontier rates for what is, per query, a narrow translation task.
A search box has a latency budget — and a per-query fee breaks the cost one. Illustrative math from our cost model and public list prices, August 2026; The Crit replaces it with your query volumes: at 1M searches a month, parsing intent through a frontier API at $0.005–0.015 per query costs $5,000–15,000 a month, with 800 ms+ round trips that force aggressive caching or a degraded experience. A fine-tuned 1–3B model on a single L4-class GPU runs $400–700 a month flat — roughly 10–20× less at this volume, by the same illustrative math — and answers in tens of milliseconds. The pattern matches published results elsewhere: Forethought reports 66–80% lower inference costs after moving routine workloads to fine-tuned small models (on AWS), and Gartner expects task-specific small models to see three times the adoption of general-purpose LLMs by 2027.
| Frontier API per query | Fine-tuned model in your VPC | |
|---|---|---|
| Cost shape | Per query, linear with traffic | Flat: one mid-range GPU, regardless of query count |
| Latency | 800 ms+ round trips — outside a search box's budget | Tens of milliseconds — inside the budget |
| Data exposure | Every customer query streams to a third party | Queries never leave your VPC |
| Your stack | Cache layers and workarounds bolted on | Elasticsearch, OpenSearch, or Algolia stays — the model is a layer in front |
If your catalog runs under a few thousand SKUs with low search traffic, better keyword synonyms probably solve it — and The Crit will say so. We also make no revenue-lift promises: conversion effects depend on your catalog and traffic, so we show you zero-result and click-through deltas on your own dashboard instead of quoting someone else's.
A layer in front. Not a re-platform.
- Own the query-understanding model trained on your catalog and search logs — the weights are yours
- Deploy it in your VPC as a layer in front of your existing search engine
- Keep a frozen eval set of real customer queries, plus a written accuracy report
- Measure zero-result rate, first-page click-through, and p95 latency against your log baseline
- Retrain from a runbook when the catalog or seasonal assortment changes
Do we have to replace Elasticsearch or Algolia to add AI search?
No. The small model sits in front of your existing engine as a translation layer: it converts a natural-language query into structured filters and a cleaned search string, and your current engine does the retrieval and ranking as before. No re-platforming, no index migration.
What does self-hosted AI product search cost to run?
A fine-tuned 1–3B model handles production search traffic from a single mid-range GPU — roughly $400–700 a month at current cloud GPU prices, flat, regardless of query count. The alternative — parsing each query through a frontier API — scales linearly with traffic: at 1M queries a month, roughly $5,000–15,000 by our illustrative math from public list prices, August 2026. The Crit replaces these assumptions with your actual volumes.
How do you measure whether AI search actually works?
Against your own logs, before and after. We baseline zero-result rate, first-page click-through, and reformulation rate from your existing search analytics, then track the same metrics after launch — plus model accuracy against a frozen eval set of real customer queries, which we gate before rollout. No industry-average claims; your dashboard is the evidence.
Customer data stays home.
- Conversations never leave
- Card data can’t reach a prompt
- No new subprocessor
- Region-pinned by design
Your search logs already list the lost sales.
Zero-result queries, reformulation chains, abandonment points — The Crit turns them into a baseline, a fine-tuned model, and a dashboard that proves the difference on your own numbers.