The data platform for physical AI.

AI data infrastructure has split into two camps, and neither serves the physical world. Vector databases — Milvus, Weaviate, Qdrant, Pinecone — were built for text retrieval. Columnar formats were built for analytics on tabular data. The fastest-growing frontier of AI is neither: autonomous vehicles, robots, and embodied systems generate synchronized streams from cameras, LiDAR, radar, and IMU at petabytes per fleet per month. Today, every serious AV and robotics company builds its own data infrastructure from scratch. No commercial product handles the full lifecycle.

Modalink is that platform. Our storage format treats a scene — a time-windowed, multi-sensor recording with calibration, ego-pose, and annotations — as the fundamental unit, not the row. Every modality is compressed natively: octree and I/P-frame encoding for LiDAR sequences, temporal delta encoding for IMU and GPS, ALP for float embeddings, lightweight GPU-decodable encodings throughout. Filters push down through compressed data without decompression; an S3 → NVMe → GPU path streams synchronized batches directly into training. Semantic deduplication, quality scoring, and per-sample lineage run as storage-layer primitives, not external tools bolted on after the fact. A single platform spans on-vehicle selective recording, depot ingest, cloud-scale curation, and training.

Physical AI is entering its infrastructure build-out phase. Humanoid robots, L4 driving, and embodied foundation models are all hitting data-scale problems that text-era tools cannot solve. We're building the default data layer for this wave. Request access →