AskVio is the intent layer for your website. It is live in production at askvio.app.
Every day, visitors ask for the size you don't stock, the policy you never wrote down, the product that would have closed the sale. None of it shows up in GA4. GA4 tells you the bounce rate and heatmaps show where people clicked, but neither tells you what the customer typed, got no answer for, and quietly left to find somewhere else. I spent ten years in analytics teams looking at that blind spot. AskVio is my attempt to close it.
How it works
AskVio answers each visitor from the site's own content (sitemap, help docs, PDFs), says so honestly when it doesn't know, and captures every miss in the customer's own words. It answers only above a confidence threshold the site owner controls, so it doesn't invent a shipping promise. When the content doesn't cover a question, the question is logged as a gap.
Each week the Monday Brief lands in the inbox: what customers asked for most, the costliest gap, and the one fix worth making first. A chatbot gives you conversations; AskVio gives you a to-do list.
Built by an analyst
The part I care most about is measurement. Every answer, refusal and gap is sent as a structured event to the customer's own GA4 property, so their analyst can compare sessions that asked a question with those that didn't, using their own attribution. No vendor ROI slides.
That data is also where the Failed Intent Rate comes from: the percentage of visitor questions and searches that got no useful answer, or none above a meaningful confidence threshold. It is the KPI I think every GA4 dashboard is missing, and I presented it at MeasureCamp Los Angeles (the full definition is on the AskVio blog).
Safe to go live
- One script tag, on any platform, not only Shopify
- Every change is validated in an isolated test environment, then published with one click and versioned, so reverting takes seconds
- Origin allowlists, signed init tokens, per-IP rate limiting and abuse blocking
- Answers visitors in their own language, with personal data scrubbed before any analysis
- A WebMCP layer, so AI agents visiting the site can use its tools, and an AI agent traffic report to see what they ask
Under the hood
The widget is a lightweight embeddable JavaScript component. The backend runs on Firebase and Cloud Functions for routing, logging and multi-tenancy, with a RAG pipeline over a vector index and OpenAI models used selectively to balance cost, latency and answer quality. Pricing is usage-based, because conversational search scales with questions, not seats.
Read more, run the free Silent Demand audit, or start for free at askvio.app.
