We build the data platforms and inference pipelines behind smart-city perception, automated enforcement, logistics optimization, and industrial vision applications. Anchored in production work at Hayden AI and Cargomatic.
Physical-world systems generate the messiest data in the AI economy: real-world video from edge devices, multi-sensor streams with timing variance, environmental conditions that change frame-to-frame, and inference latency requirements measured in milliseconds, not minutes.
Most AI services teams are good at GenAI but have never shipped real-time perception at scale. Most computer vision teams are good at models but not the data infrastructure to feed them. The gap is where projects fail.
We build the end-to-end stack — from edge ingestion through structured datasets to production inference — for systems that need to perceive and act on the physical world.
Real-world video and sensor data ingestion from edge devices. Frame-level structured datasets. Data lake architecture for perception-driven systems. Production-tested at smart-city scale on Hayden AI's automated traffic enforcement platform.
Computer vision pipelines that turn raw video and sensor streams into structured outputs. Object detection, tracking, classification, anomaly identification. Built to operationalize — not just demo.
Production ML inference pipelines for real-time video analysis. Latency-optimized architectures. Edge-cloud orchestration. Continuous evaluation and drift detection.
Routing, dispatch, and operational decisions powered by ML — anchored in our work at Cargomatic on logistics routing and container unloading workflows. Data-driven optimization applied to real-world ops.
The pattern we deploy for perception-driven systems: real-world data ingestion from edge devices, structured frame-level datasets, production inference pipelines, and a reasoning layer that contextualizes ML outputs and routes to action.
Where Claude fits: anomaly explanation on top of perception outputs, exception handling in real-time pipelines, operator copilots for human-in-the-loop systems, and reasoning over multi-modal outputs from CV and sensor fusion.
// pattern
01 · Edge ingestion — Video and sensor data from edge devices. Resilient to network conditions. Structured at point of capture where possible.
02 · Frame-level structuring — Raw streams turned into queryable datasets. Annotation, labeling, lineage. The foundation everything else sits on.
03 · Production inference — CV models, sensor fusion, real-time output. Latency-optimized. Continuously evaluated and monitored for drift.
04 · Reasoning + action — Claude reasons over CV outputs and operational state. Explains anomalies. Routes exceptions. Generates human-readable reports. Supports operator copilots.
Real-world systems break in ways no benchmark predicts. Tell us what you're building — we'll tell you what we'd build to make it survive contact with reality.