Physical AI
Autonomy programs rarely fail for lack of models. They fail because sensor, telemetry and simulation data are scattered, unversioned and untraceable. We design the data layer that fixes that.
One agreed model for machine, sensor, telemetry, jobsite, simulation and operational data, so teams stop rebuilding the same schema.
Each product has an owner, a schema, quality rules, freshness and access policy. Bad data fails loudly instead of reaching a model.
From raw capture to curated dataset to trained model to deployed model. You can answer which data produced which result.
Ingestion, curation, labeling, versioning and feedback from deployed models, designed as one loop rather than five projects.
Train, fine-tune, evaluate, register, deploy and monitor, with an evaluation harness that blocks a worse model from shipping.
1. Weeks 1-2
Current-state data landscape, stakeholders, and the three data products worth doing first.
2. Weeks 3-6
Domains, canonical models, contracts, lineage and evaluation standards, reviewed with your platform leads.
3. Weeks 5-10
Working pilots on public or approved data: a flywheel, a lifecycle with an evaluation gate, and a single-node GPU platform.
4. Weeks 11-12
Standards, an implementation plan for your platform teams, and knowledge-transfer sessions.
Scope, deliverables and acceptance are agreed in writing before we start. Pilots use public datasets with checked licenses or data you approve. Simulation output is labeled as simulation.
Built
Led iKLEWS, an NSF-funded system over roughly 750,000 pages of ethnographic text: cloud pipeline, custom transformer models, an auto-indexing service and research APIs. Delivered in 12 months.
Built (core), designed (platform)
A governed extraction spine with human review, benchmarked on public datasets, inside a tiered platform design with schema registry, calibration, evaluation and a hash-chained audit log.
Built
Quality, maintenance and energy agents over a real-time signal layer, with a shadow-mode validation design. The venture around it was wound down; the architecture remains.
Built
Object detection and tracking over conveyor video at the edge, feeding streaming analytics on Kubernetes using Spark. Architected and implemented for an on-demand manufacturer.
The physical-AI research canvases are public, and published research is on the research page and at Google Scholar.
A 30-minute conversation to agree what is in, what is out, and whether a reference architecture is the right first step.
Book a 30-minute conversation