Data Panda brings the Convex project behind your application together with the data from the rest of your business. From one place, we turn it into dashboards, automations, AI workflows and custom apps your team uses every day.
Convex is a backend platform that gives every app a reactive document database, server functions written in TypeScript, file storage, scheduled jobs and vector search behind one API. The team launched it in 2021 in San Francisco and the founders come out of Dropbox, where they ran the storage and database groups.
For a Data Panda customer the interesting data is the project itself: the tables you defined in your schema, the documents your queries and mutations read and write, the file metadata next to your blobs, and the scheduled-job records that say what fired and when. The system fields _id and _creationTime on every document make it straightforward to land that data in a warehouse and join it to the rest of your stack.
Convex is popular with teams shipping AI apps and agentic products on top of Replit and similar AI app builders, where the reactive sync and vector search land naturally next to LLM workflows. That same data is worth more in a warehouse for everything that is not a live user request.
Documents, file metadata and scheduled-job activity joined in one place.
Let document changes inside your Convex project fire actions across the rest of the stack.
Score, classify and generate on the operational data you already capture.
Tools for support, finance and ops that read across your project without hitting the live backend.
A list of concrete reports, automations and AI features we have built on Convex data. Pick the one that matches your situation.
Who signed up this month and which of them used the product?
Your users table joined to the document tables that record real activity, broken down by source, plan and cohort. Marketing sees which channels brought activated users and which brought ghosts, on the same numbers product is looking at.
What is each tenant costing us in Convex right now?
File-storage size, action invocation volume and scheduled-job runtime aggregated per tenant, against the plan they pay for. Finance and product see who is loss-making before the next price review, not after.
Which dashboards will break the next time we change the schema?
A change log on your Convex schema linked to the dashboards, automations and AI workflows that read each field. The next deploy stops being a surprise for reporting because the impact is visible before it ships.
File-storage, action-invocation and scheduled-job usage per tenant lined up against the plan they pay for, joined to Stripe revenue. The cost of serving each customer becomes visible on the same record as their MRR.
Auth and product-usage signal on every CRM account, sourced from the Convex project rather than a custom export. Reps see who is on the verge of expanding and who is going quiet, before the renewal call.
Schema drift, action errors and scheduled-job failures tracked in one place. Reporting stops being collateral damage of the next deploy and becomes part of the release check.
You keep the reporting tool you already have. We connect it to the warehouse where your Convex data lives.
OAuth authentication. Read-only by default. We sign a DPA and your admin keeps the keys.
Data flows into your warehouse on your schedule. Near real time or nightly, your call. You own the data.
We build the first dashboard, workflow or AI feature with you, then hand over the keys. Or we stay on for ongoing delivery.
We set up the foundation. Your team builds on top.
Best fit Teams that already have a BI analyst or data engineer and want to own the build.
We build the whole thing, end to end.
Best fit Teams without in-house BI or dev capacity. You tell us what you need and we deliver it.
You do. It lands in your warehouse, on your cloud account. We don't resell or aggregate it. If you stop working with us, the warehouse stays yours and keeps running.
Near real time for most operational systems. For heavier sources we schedule hourly or nightly. You pick based on what the reports need.
No. If you don't have one, we help you pick one and set it up as part of the first delivery. Common starting points are Snowflake, Microsoft Fabric, or a small Postgres start.
Every table in your project schema replicates as a flat table in the warehouse, with the system fields _id and _creationTime preserved on each row. That means joins on document references and time-based queries work the same way they do inside your Convex functions, without having to call a query from outside.
Yes. The interesting picture is the join across your document tables, the file-storage metadata Convex keeps next to your blobs, and the scheduled-job and action history. The connector replicates all of that so the warehouse can answer questions about who signed up, what they uploaded, and which background jobs ran on their behalf.
We review your Convex setup and the systems around it. Together we pick the first thing worth building.