Why Latent Space Isn't Enough — and What We're Building Instead
Everyone's talking about the next generation of Retrieval-Augmented Generation (RAG) platforms. Latent Space is one of the most polished contenders, offering streamlined tools for building LLM-powered apps. But here's the problem: RAG as we know it is incomplete.
Anthony Rawlins
CEO & Founder, CHORUS Services
The Latent Space Value Proposition Latent Space provides a developer-friendly way to stitch together embeddings, retrieval, and workflows. If you’re building a chatbot or a knowledge assistant, it helps you get to “Hello World” quickly. Think of it as an accelerator for app developers.
The Limits But once you go beyond prototypes, some cracks show:
- Context is retrieved, but it isn’t structured in a reproducible or queryable way.
- Temporal information — what was true when — isn’t captured.
- Justifications for why something was retrieved are opaque.
- Context doesn’t move fluidly between agents; it’s app-bound.
What We’re Doing Differently Our approach (Chorus + BZZZ + UCXL) starts from a different premise: context isn’t an app feature, it’s infrastructure.
- We treat knowledge like an addressable space, not just an embedding lookup.
- Temporal navigation is first-class, so you can ask not only “what’s true” but “what was true last week” or “what changed between versions.”
- Provenance is baked in: retrieval comes with citations and justifications.
- And most importantly: our system isn’t designed for a single app. It’s designed for a network of agents to securely share, query, and evolve context.
Conclusion Latent Space is a great product for teams shipping today’s RAG-powered apps. But if you want to build tomorrow’s distributed AI ecosystems, you need infrastructure that goes beyond RAG. That’s what we’re building. Why Latent Space Isn’t Enough — and What We’re Building Instead