Datamagics AI
Visual RAG pipelines and automated data auditing
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Datamagics AI - Visual RAG pipelines and automated data auditing.

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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.

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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

Would love to see a version control system built right into the canvas so you can branch a pipeline, test changes against the same dataset, and roll back if a cleaning recipe breaks something downstream.

Pulled in a messy CSV and the auto-fix recipe caught the date formatting issues without me touching anything, which honestly saved me an hour of cleanup. The node-based RAG pipeline going straight into Pinecone felt surprisingly smooth for a no-code setup.

Finally tried dragging a few nodes to push cleaned data into Pinecone and the visual flow made it so much easier to debug than my usual notebooks. The audit against live SQL was a nice surprise too.

The visual pipeline approach is really cool. One idea: add a way to version and diff entire node graphs, so when a teammate modifies a recipe you can see exactly which cleaning steps changed before approving it. Right now it feels like a single merge could quietly break downstream RAG behavior.

The drag-and-drop RAG pipeline setup was way smoother than I expected, and honestly the live SQL audit feature caught an inconsistency I would have totally missed. Solid tool for anyone wrangling messy data for AI projects.