Standard RAG is blind to structure. We use XML-native vector search to map the spatial and semantic reality of your presentations, schematics, and reports. Don't just search text — search the structure.
Standard embeddings flatten a P&ID into tokens, destroying the spatial grid, bounding boxes, and cross-references engineers actually rely on.
Retrieves text for part #A4472 in the adjacent column. Confidently returns the wrong torque spec. In a maintenance context, a 1.2 Nm error is a compliance incident.
Maps the XML DOM, preserves the table grid, walks the schematic callout → engineering table relationship, and returns the exact bounding box.
Upload a corpus. Ask a question. OmniStructure returns the answer with the exact bounding box, page, and DOM path — verifiable by your engineers in under a second.
Purpose-built infrastructure for structural understanding at enterprise scale.
We leverage an advanced multimodal diffusion model to natively embed spatial layouts and bounding boxes.
We don't chunk blindly. We preserve your document's DOM tree, mapping exact relationships between text, tables, and telemetry graphs.
Built for strict EU data sovereignty compliance. Perfect for highly regulated aerospace, pharma, and manufacturing IP.
See why standard AI fails in high-stakes engineering environments, and how structural understanding solves it.
"Standard RAG solutions completely miss the context hidden in complex presentations and structural data. OmniStructure AI is the first system I've seen that actually solves the multimodal wall at an enterprise scale."
Our team combines deep AI research with proven enterprise GTM execution.
Deep AI research heritage spanning more than a decade.
Track record of shipped enterprise AI implementations in production.
Built high-load Enterprise AI products serving tens of thousands of users.
Join the enterprises already unlocking knowledge hidden in their most complex documents.