Vectorize

Hi Product Hunters! 🏹 I’m excited to introduce Vectorize, a cloud data integration platform designed to make building AI apps faster and with less hassle. We solve the tough, annoying parts of implementing Retrieval-Augmented Generation (RAG) applications so you can focus on the fun parts of AI development. Here’s how: 🔄 Automate Data Extraction: Seamlessly pull data from documents, SaaS platforms, and more. Forget manual data wrangling—we take care of it. 🗂️ Source Connectors: Connect to a variety of sources, including Amazon S3, Azure Blob Storage, Confluence, Discord, Dropbox, Google Drive, Google Cloud Storage, Intercom, and more. 📊 Vector Databases: Supports integrations with Astra DB, Couchbase Capella, Elastic Cloud, and Pinecone 🧪 RAG Evaluation: Automatically test and find the best vectorization strategy for your unique data. Instead of writing throw-away code to try different embedding models, let us handle it for you. ⚡ Real-Time RAG Pipelines: Keep your vector indexes fresh and up-to-date. We take care of data ingestion issues like error handling, retries, and back-pressure so you don’t have to spend time troubleshooting. ⏱️ Accelerate AI Development: From concept to deployment, we help you move faster by automating the difficult parts of RAG implementation. Your vector indexes will stay current without the constant manual upkeep. 🎯 Stay Accurate & Relevant: We ensure your data is always fresh and your AI stays accurate. You’ll have real-time visibility into your vector data and know exactly what’s being processed. 💸 Cost-Effective: We offer a forever free tier for developers, and as your needs grow, our pricing scales affordably with you. 📈 Scalable & Flexible: Whether you’re a startup or an enterprise, Vectorize adapts to your needs. It integrates with your existing vector database, so you maintain full control over your data. I hope you will give us a try. If you do, I’d love to hear your feedback here in the comments! Chris Latimer Co-Founder & CEO, Vectorize

Looks really easy to use. Just one question, how can I use the vectorized data by a different application?

Huge congrats to the Vectorize team on today's launch! I'm intrigued by the promise of optimized RAG pipelines tailored to specific data. Quick question: How do you handle scenarios where the underlying data structures or schemas are constantly evolving - does Vectorize's auto-vectorization adapt to these changes in real-time?