// Signal → Semantics → Query
A platform that ingests any sensory data — 3D scans, documents, audio, RF signals — extracts structured semantic scene graphs, and makes them available for natural language reasoning. Built for robotics. Designed for every domain.
Frontier language models are remarkably capable reasoners — but they operate on text and images that are already semantic. Point clouds, RF spectra, volumetric scans, and sensor telemetry are signal-first: they require substantial domain-specific processing before any meaning can emerge.
This gap — between raw physical signal and queryable semantic knowledge — is where robotics systems still struggle most. A robot that can navigate a known map is not the same as a robot that can answer "is there enough clearance for a wheelchair between the desk and the door?"
"The most defensible position is to be the signal-to-semantics translation layer that sits upstream of whatever foundation model the world prefers."
I'm building a universal semantic memory platform — a system that ingests any sensory or documentary data source, extracts meaning through domain-specific adapters, and exposes that meaning through a unified graph store and natural language query interface. The 3D spatial search product is the first instantiation. Every adapter compounds the value of the infrastructure already built.
The critical architectural boundary: adapters handle raw signal processing and produce a normalized graph. The universal core handles everything else — storage, retrieval, reasoning, and user interaction.
Each adapter translates a raw signal domain into the universal graph schema. Building a new adapter doesn't require touching the core — it only requires implementing the extraction contract.
This platform started from a simple question: what if you could upload a 3D scan of any space and ask it questions in plain English? The deeper we got, the more we realized the question generalizes — to documents, to audio, to any signal domain where meaning has to be extracted before it can be reasoned over.
We're at the early stages and actively looking for research collaborators — particularly in robotics, spatial computing, and signal processing — who see the same gap I see and want to work on closing it together.
// Open to Collaboration
If you're working on problems where physical signal needs to become queryable knowledge — in robotics, spatial computing, medical imaging, or anywhere else — we'd like to hear from you.
Particularly interested in: robotics navigation, spatial AI, signal processing, and embodied AI research.