Aitomatic · The AI Factory for the Industrial World
Industrial AI is not primarily a model-selection problem. It is a lifecycle problem. Aitomatic turns private operational knowledge, expert judgment, physical constraints, and production evidence into governed intelligence that compounds across the estate.
Our category
Generic AI factories produce tokens. Aitomatic produces operational intelligence: qualified models, agents, cognitive ontologies, evaluation systems, and Operational Packs that can run inside critical physical operations.
Create and evolve intelligence from domain knowledge and operating evidence.
Deploy it locally and govern it inside the systems that run the operation.
Measure, monitor, and requalify every material change.
The expertise-retention problem
Your best operators prevent scrapped batches, equipment failures, and line stoppages. None of it is written down.
Even when documented, the why behind decisions gets lost.
One expert can only be in one place. Their judgment doesn't multiply.
When experts leave, retire, or move on, their expertise leaves with them.
With closed AI, every query trains their models — not yours. Your expertise becomes their training data.
Our approach
Cognitive Ontology captures not just what your enterprise knows, but how things connect and why decisions get made — a living knowledge graph that grows smarter with every interaction, where procedures connect to reasoning and decisions link to downstream effects.
Aitomatic's agents help build and evolve the ontology from documents, systems, and expert practice. Changes are versioned, evaluated, and promoted through governance.
A domain knowledge graph at the center lets agents reason with your institutional knowledge — not just retrieve it.
The complete cycle of ontological map-making and map-using: capture, organize, and put institutional intelligence to work.
Institutional intelligence accumulates with every operation, rather than eroding as people move on.
Business impact
Before: an expert reviews 200 cases/day with tribal knowledge. After: an agent processes 5,000 with encoded expert judgment.
Before: a 3 AM anomaly waits for the morning shift. After: it's resolved at 3:04 AM.
Before: a new hire shadows veterans for six months. After: they work alongside an agent that already holds the veteran's knowledge.
Before: a regulatory change means months of manual procedure work. After: instant impact analysis and draft remediation.
Who we are
Led by Panasonic, Google, and Amazon veterans, pushing the leading edge of neurosymbolic and industrial AI research — a founding member of the AI Alliance alongside IBM and Meta.
Chief Architect of the AI Alliance's Project Tapestry, working alongside Yann LeCun on open, sovereign AI infrastructure. Previously co-founder & CEO of Arimo (acquired by Panasonic) and an engineering director at Google. PhD, Stanford; BS EECS, UC Berkeley. LinkedIn →
Reclassified with governed agent assistance at 94% accuracy. Root-cause analysis cut from 3 days to 20 minutes.
Reviewed in 6 hours with full audit trails. Previous: 4 analysts, 3 weeks.
Diagnosed within a governed operating envelope, with the authorized resolution path verified before action.
Bring one consequential operation. We’ll show the path from expert knowledge to governed production intelligence, deployed within your boundary.
Book a Demo →