TL;DR: datavessel chat now accepts file uploads. Drop in a PDF report, a dashboard screenshot, a Markdown doc, an HTML export, or a plain text file — and ask about it in the same conversation as your live GA4, Search Console, and store data. The thing you used to paste in chunks is now one drag-and-drop.
The most common workaround in datavessel chat used to be the same one in every AI assistant: someone has a PDF report from an agency or a screenshot of a dashboard from another tool, and they want to ask questions about it. They paste a wall of text. They describe what’s in the image instead of showing it. They get a worse answer than they should because the model is working from a translation, not the source.
Chat file uploads close that gap. Drop the file into the chat window. Ask your question. The agent reads the file directly and answers against both the file and your connected data sources in the same conversation.
What Chat File Uploads Support Today
Five formats are live:
- PDFs — Reports from your accountant, audit deliverables from an SEO agency, monthly recaps you’ve been emailed, contract documents. The agent extracts the text and reads it in context.
- Images — Screenshots of dashboards, photos of receipts, charts from competitor reports, design mockups. The model sees the image and can describe, compare, or extract from it.
- Markdown — Spec docs, meeting notes, internal wikis. Drop a `.md` file and ask the agent to summarize, find action items, or cross-reference it against live data.
- HTML — Saved web pages, exported email campaigns, scraped content. Useful for “this is what my competitor’s pricing page says — compare it to ours.”
- Plain text — Anything you’d otherwise paste. Logs, CSV exports, transcripts.
You can attach multiple files in one message, mix formats, and the agent treats all of them as part of the same query. Drop two PDFs and ask “what changed between these two reports?” — that works now.
Why PDFs Were the Priority
Of the five formats, PDFs were the one we knew most users would reach for first. They’re the format business documents live in. Quarterly reports come as PDFs. Agency deliverables come as PDFs. Bank statements, invoices, audit reports, contracts, signed proposals — PDFs.
Before chat file uploads, asking the agent about any of those required copying the text out, losing the formatting, and pasting a degraded version into the chat. Now you drop the PDF and ask. Some examples that work today:
- “Here’s the SEO audit our agency sent. Summarize the top five issues by potential impact.”
- “This is last quarter’s board deck. Compare the traffic numbers in here against what GA4 actually shows for the same period.”
- “Read this customer interview transcript and pull out the three most common complaints.”
- “Here’s a competitor’s annual report. What product categories did they invest in this year?”
The combination of “PDF the user uploaded” plus “live data from the connected sources” is where the chat gets sharp. The PDF gives context the live data doesn’t have; the live data verifies or contradicts what the PDF claims. Neither alone gets you the answer.
Why Images Matter for Analytics
Images sound like a consumer feature — drop a photo, ask what it is. In analytics chat, they’re more useful than that. The single most common use case so far is dashboard screenshots from tools datavessel doesn’t have a connector for.
You’re looking at a chart inside Stripe, or a campaign report inside Meta Ads Manager, or a Mixpanel funnel. You take a screenshot, drop it into chat, and ask “what’s going on here?” The agent reads the chart, picks out the numbers, and can compare them against your GA4 or store data without you having to type the numbers in. It’s the fastest bridge between a tool we don’t connect to and the conversation you’re already having.
The other useful pattern is competitive: a competitor’s pricing page, their feature comparison table, their email screenshot from a customer. Drop the image, ask what’s notable, get a sentence back.
Markdown, HTML, and Plain Text — The Quiet Wins
These three formats won’t drive headlines, but they remove specific small frictions:
- Markdown covers the spec docs, meeting notes, and internal wikis that already exist in your team’s tooling. Drop the `.md` file, ask the agent to extract decisions or open questions, done.
- HTML handles the case where someone saved a page, exported an email, or scraped competitor content. The model parses it and ignores the markup.
- Plain text is the catch-all. Log files, CSV exports, chat transcripts, anything that didn’t come from a polished format.
The point of supporting all of them is that you stop thinking about whether a file is “the right kind” to bring into the conversation. If you have it on your machine and it’s text-ish or visual, drop it in.
Where Files Fit With Live Data
The shape of chat changes when files are in the mix. Before, every question was answered against live, connected data: GA4, Search Console, Shopify, Search Console, the rest. Now, files give the agent context that isn’t in any connector — historical reports, external benchmarks, internal documents, competitor materials.
The interesting questions are the hybrid ones. “The board deck I’m uploading says we hit 50k monthly visits in March. Does GA4 confirm that?” The agent reads the PDF, queries GA4, and tells you whether the numbers line up. “My agency’s audit says 47% of pages have thin content. Cross-check against my Search Console impressions — which thin pages actually still earn traffic?” The audit alone is a list of problems; the cross-reference is a prioritized to-do list.
This is the kind of question that used to require an analyst with three tabs open. Now it’s a drag, a drop, and a sentence.
Limits and Trust
A few specifics, because they matter:
- Files are not stored permanently. They’re attached to the conversation and used as context for that session’s queries. Delete the conversation and the file is gone.
- The model that reads your file is the one on your own AI account. Datavessel doesn’t run inference on your uploads — your connected Claude, ChatGPT, or Gemini account does. Your provider’s data handling terms apply.
- File size is bounded by your provider’s limits. Very large PDFs (hundreds of pages) get chunked; the agent will tell you if it had to skip sections.
Try It
Open a chat, drag a file in, ask a question. The drop zone appears when you start dragging — no separate button to find. If you’re not on datavessel yet, you can start free and have a file in chat inside two minutes.
Sources
- Web Chat Interface — Background on the chat surface that file uploads now live inside.
- Inline Charts in Web Chat — Companion update on how chat responses render, including charts the agent produces against uploaded data.
- Design Sweep and Shopware Connector — Yesterday’s release notes, including the new Shopware source connector.
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