About Product Gale
We watch AI shop your store,
and fix what makes it leave
Product Gale is the observability platform for AI commerce. When an assistant answers a question about your products, or an agent tries to buy from your store, we see it, record what happened, put a dollar figure on what went wrong, and repair the pages responsible.
01
The problem, in one minute
A growing share of shoppers now start with an AI assistant instead of a search bar. They ask ChatGPT for the best waterproof boots under $150, or send an agent to compare and buy for them. That traffic is high-intent: these are people ready to purchase.
Much of it leaves without buying, for two mundane reasons. The assistant couldn't read the product page, so it answered wrong or recommended a competitor. Or the agent could read the page but couldn't finish the journey — it failed to pick a variant, add to cart, or get through checkout.
Nothing in a standard stack records any of this. That's the gap Product Gale exists to close.
02
Where we sit in your stack
When an AI shopper fails on your store, here is what each tool you already own actually sees.
Web analytics
GA4
An unattributed session, if anything. No notion of the answer the shopper was given.
APM / monitoring
Datadog, New Relic
A successful 200 response to a request that failed the shopper. Nothing errored.
LLM observability
LangSmith, Braintrust
Your own outbound model calls. Nothing about the assistants coming in.
Product Gale
The interaction itself: who came, what was said, where the journey ended, what it cost.
03
How it works
Four stages, run as a continuous loop. No score-and-stop: every finding ends in a priced failure and a verified fix.
- 01
Detect
We identify the AI assistants and shopping agents already reaching your store: which ones they are, which pages they read, and which pages they give up on. No tag to install — we start from your public storefront, plus your server logs if you have them.
- 02
Test
We put your catalog in front of the assistants your shoppers actually use, ask the questions they actually ask, and record every wrong answer: wrong prices, invented specs, competitors recommended instead of you. Then we drive an agent through your real buying flow and log exactly where it stalls.
- 03
Price
Every failure gets a dollar figure built from your own numbers — traffic, conversion rate, average order value. Assumptions are shown inline and the conservative case is what gets reported. You get a monthly leak total and a ranked list of what's costing the most.
- 04
Fix
We repair the pages behind the top failures so assistants answer correctly and agents complete the purchase, then re-run the exact tests that failed to prove the answer changed. The loop repeats as your catalog and the models move.
04
The two numbers we report
Everything we find rolls up into two metrics, defined precisely enough to quote in a board deck.
AI Revenue Leak
The monthly dollar value of business lost to AI interactions that failed. Computed per failure, from your own numbers — assumptions shown inline, conservative case reported.
leak = AI sessions with that intent × win probability × conversion rate × average order value
Agent Completion Rate
The share of AI agent journeys against your live storefront that reach checkout. For every failure we record the failing step, the reason, and a replay you can watch.
search → product → variant → cart → checkout · scored against your real buying flow
05
Coverage, today and next
AI touches a commerce business on three surfaces. One schema covers all three.
Assistants and agents arriving from outside
ChatGPT, Perplexity, shopping agents — the AI traffic reaching your store right now. Detection, testing, pricing, and repair all ship today.
The assistant you run for your own shoppers
The chat widget or shopping assistant on your own site, monitored with the same schema.
The models your own systems call
Outbound model calls from your product feeds, search, and internal tooling.
07
The practical details
- Setup
- No tag or code to install. We work from your public storefront; server logs are optional and add depth.
- First report
- Your first AI Revenue Leak report arrives in under a week.
- Pricing
- Start free with $20 in lifetime credit, then usage-based from $0.05 per product covered. Volume tiers and enterprise contracts are available.
- Verification
- Every fix is verified: we re-run the exact test that failed and show you the answer changing.
- Your data
- Used only to deliver the analysis and fixes you asked for. Never sold, never used to train models.
Full details on pricing and data security.
08
The company
Product Gale was founded in 2024 and is headquartered in Tirana, Albania. We started with a simple observation: finding out what AI assistants tell shoppers about your products, one query at a time, is slow and unrepeatable. We built the platform that gives teams running large catalogs that answer continuously, and fixes what it finds.
Reach us at contact@productgale.com or through the contact page.
See what AI shoppers see
on your store
Your first AI Revenue Leak report in under a week. No code, no tag to install.