What this makes possible
Our multimodal architecture combines high-resolution visual encoders with state-of-the-art vision-language models and hybrid sparse-dense vector indices. Every extracted entity is tied to exact spatial bounding boxes or temporal video timestamps for complete verification and compliance.
Where we focus
- Vision-Language Model (VLM) fine-tuning & routing
- Complex document intelligence (tables, stamps, handwriting)
- Cross-modal semantic search & vector retrieval
- Dense video understanding & temporal grounding
- Multimodal reasoning & structured JSON extraction
- Audio transcription & acoustic event recognition
- Technical diagram & schematic parsing
- Unified multimodal evaluation frameworks
How we build it
Document Graph Intelligence
Parse multi-page unstructured enterprise documents into validated, structured schemas with zero loss of spatial layout semantics.
Cross-Modal Vector Retrieval
Search visual archives using natural language queries, or search document stores using image snippets and diagrams.
Verifiable Pixel Grounding
Eliminate hallucinations by forcing models to supply exact bounding coordinates and citation anchors for every extracted fact.
Cost-Optimized Model Routing
Route simple OCR tasks to lightweight edge models and reserve large VLMs for nuanced, high-complexity multi-modal reasoning.