Why we built Unbody and why you should care?
When building traditional software, we’ve had a clear structure: databases to store data, APIs to query it, and frontends to present it. But building AI-driven products breaks these assumptions entirely. It’s not just adding another layer of complexity—it’s a fundamental shift in how we design and develop software.
Data isn’t static anymore.
It’s not just sitting in tables waiting to be retrieved. Data in an AI-driven product needs to be alive—processed, vectorized, enriched, and ready to interact with AI models. You can’t just store raw data and call it a day; you need a pipeline that transforms it into knowledge that AI can work with.
Relational structures need to become semantic structures.
AI doesn’t think in rows and columns. Relationships between data points must now be fluid, dynamic, and semantically rich to allow for advanced queries like “What’s the most relevant document for this idea?” or “What products are similar to this image?” Building these relationships with traditional databases is cumbersome and often impossible.
AI models speak a different language.
AI-native development means your stack needs to bridge the gap between unstructured data and AI-ready data. Large Language Models (LLMs) and vision models can’t simply plug into raw data—they require cleaned, chunked, vectorized, and often enriched inputs to perform effectively. Without a seamless pipeline to prepare this data, you end up duct-taping multiple tools together, creating a fragile workflow.
APIs can’t just retrieve data—they need to deliver intelligence.
In AI-native products, your APIs aren’t just fetching rows from a database. They need to serve insights—semantic matches, recommendations, generative responses—without forcing you to build complex middleware. APIs now need to work as intelligent layers that understand and interact with your data meaningfully.
Unbody is designed to address the challenges of AI-native development by rethinking the entire product development stack. It transforms raw, scattered data into AI-ready knowledge through a dynamic data pipeline that handles ingestion, vectorization, and enrichment. With a vectorized database that enables semantic queries and dynamic relationships, Unbody eliminates the need for rigid relational structures while scaling effortlessly. It integrates seamlessly with AI models like OpenAI, Cohere, or custom LLMs, ensuring your data flows directly into the intelligence layer. Finally, Unbody’s APIs go beyond CRUD, delivering generative responses, semantic recommendations, and multimodal search without requiring additional layers in your stack.
What You Can Build with Unbody
This reimagined stack enables developers to create products that were previously complex or impossible to build.
- Build Preplexity, but for your data.
- Build Tinder, but for your products.
- Build an Alexa, tailored to your customers.
- Build a smarter Wikipedia, for your company.
- Build Shazam, but for images.
- Build your own AI-first CMS.
What’s Next?
Now that you understand why Unbody exists, it’s time to see how it works.