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Datamagics.ai is a unified visual workspace to clean, ingest, and validate data for AI applications. Build node-based RAG pipelines into Pinecone and Qdrant in a drag-and-drop canvas. Automatically audit unstructured report claims against live SQL databases and datasets simultaneously in secure, isolated sandboxes. Create repeatable data-cleaning recipes to auto-fix formatting anomalies, and monitor real-time ML observability drift metrics. Zero code required.
Hey Product Hunters! 🐱
I’m Shubham, founder of Datamagics.ai.
Some of you might remember our early prototype launched a while back under a different domain. Since then, we went back to the drawing board, completely rewrote the engine from the ground up, moved to datamagics.ai, got selected for the AWS Activate program, and built a secure, production-grade workspace.
Building context-aware AI applications is simple, but the data engineering around them is painful. I got tired of writing repetitive Python scripts to parse PDFs, configuring manual chunking routines, and setting up vector indexes. On top of that, auditing the outputs of our reports against live database records manually was eating up hours of engineering time.
That’s why we built Datamagics 2.0—a unified, secure data workspace designed to help developers and data teams clean, ingest, and validate data with zero code.
Here is what you can do in the new workspace today:
🔌 Orchestrate Visual RAG Pipelines: Build chunking, embedding, and ingestion flows directly into Pinecone & Qdrant inside a visual node canvas, then test queries instantly in our built-in simulator.
📑 Double-Sided Report Validator: Automatically audit numerical claims in text reports/PDFs against static datasets and live SQL connections simultaneously using secure, isolated sandboxes.
🧼 Zero-Code Data Cleaning Recipes: Automatically fix schema anomalies, standardize formats, and schedule transformations as repeatable recipes.
📊 ML Observability: Monitor live inputs, detect column-level data drift, and configure health notification alerts.
We’re live and free to try today!
I’d love to hear your feedback, feature requests, or answer any technical questions about our ingestion engine or validation sandboxes. What features should we build next?
Thank you for all the support! 🚀
— Shubham & The Datamagics Team
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About Datamagics AI on Product Hunt
“Visual RAG pipelines and automated data auditing.”
Datamagics AI was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #44 on the daily leaderboard. Datamagics.ai is a unified visual workspace to clean, ingest, and validate data for AI applications. Build node-based RAG pipelines into Pinecone and Qdrant in a drag-and-drop canvas. Automatically audit unstructured report claims against live SQL databases and datasets simultaneously in secure, isolated sandboxes. Create repeatable data-cleaning recipes to auto-fix formatting anomalies, and monitor real-time ML observability drift metrics. Zero code required.
Datamagics AI was featured in Developer Tools (515.9k followers), Artificial Intelligence (473.8k followers) and Data & Analytics (5.7k followers) on Product Hunt. Together, these topics include over 186.8k products, making this a competitive space to launch in.
Who hunted Datamagics AI?
Datamagics AI was hunted by Shubham Kumar. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
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Shubham Kumar