Multimodal AI
Cross-Modal Understanding & Information Synthesis
Real-world business knowledge doesn't live solely in neat text fields. It lives in scanned PDF contracts, complex engineering blueprints, audio calls, CCTV footage, and structured databases. Alector Lab builds multimodal systems that align diverse data modalities into unified semantic vector spaces and reasoning pipelines.
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.
Architectural Building Blocks
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.
Full Technical Coverage Areas
All Multimodal AI systems are delivered as production code, custom microservices, or fully managed appliances with complete IP transfer to clients.
Request an Architecture Assessment for Multimodal AI
Speak directly with our senior systems architects regarding your latency, throughput, and data requirements.