Built in Detroit, for the plants that built Detroit.
Moe Tanabian spent his early career on the floor — first as a maintenance technician in stamping plants in Southeast Michigan, then as a process engineer at a powertrain sub-assembly supplier near Warren. He learned to read PLC traces before most OEM software vendors had ever visited the shop floor. He also watched a predictable pattern repeat across every facility he worked in: fault occurs at shift's end, knowledge stays in the outgoing engineer's head, incoming engineer loses the first hour re-diagnosing what their predecessor already partially understood.
The OEE dashboard told everyone the cell was down. Nothing told anyone which station, why, and what to order. Moe started prototyping a trace-reading tool in 2023 using data from facilities he had worked in. The company was incorporated in Detroit in 2024. Intuigence AI is the tool he wished had existed on his third shift at Warren.
Moe Tanabian
Moe began his career as a maintenance technician on a stamping line in Southeast Michigan, before moving into process engineering roles at Tier-1 automotive suppliers — covering both stamping and powertrain sub-assembly environments near Warren, MI. He spent years reading PLC traces by hand and writing work orders from memory at shift's end.
The problem he couldn't solve with existing tools: OEE dashboards showed which cell was down; nothing told you which station caused it, why, or what to order from the CMMS. Every senior engineer had the pattern recognition to answer those questions — and that knowledge retired with them. In 2023, Moe started building what would become Intuigence AI, using trace data from facilities where he had personally worked. The company was incorporated in Detroit in early 2024.
His non-negotiable for the product: every AI output must be verifiable by the engineer who receives it. Confidence intervals on every hypothesis. Signal citations the engineer can follow back to raw tag values. Override and correction capability that feeds the model — not because it looks credible in a demo, but because engineers who cannot verify will not trust, and engineers who do not trust will not use.
A small team with deep plant-floor roots.
Every person at Intuigence AI has either worked on a plant floor, built software for one, or spent significant time embedded with process engineering teams.
Four principles, non-negotiable.
Evidence over assertion. We show the signal, not just the answer. Every AI hypothesis comes with the data that generated it.
Engineer respect. The tool augments judgment, not replaces it. Engineers override, correct, and improve the AI — that's by design.
Floor-first. If it doesn't work on a real floor, it doesn't ship. Every feature is validated in an actual plant environment before release.
Radical transparency. Confidence intervals, not magic scores. If the AI isn't sure, it says so — and says why.
Talk to Moe about a pilot.
Pilot access starts with a direct conversation about your plant's configuration, shift structure, and recurring fault patterns.