A generative-AI assistant that lets a factory floor ask its own data a question, and get back the stoppage, the likely cause, and the fix, in plain English. One question fans out across MES, sensor logs, ERP and Salesforce, and fans back into a single answer.
Confidentiality note. Proprietary figures are withheld. Screens, data and metrics shown here are sanitized recreations built for this portfolio, directionally accurate but not the client's actual numbers.
| Factory | Line | Shift | Machine | Min | Cause | Operator note |
|---|---|---|---|---|---|---|
| Plant 07 | 3 | 2 | Filler 1 | 312 | Changeover | "purge step ran long again" |
| Signal agent: 4 of 5 overruns this week begin at the same valve-purge step. Recommend re-sequencing the 23:00 changeover. Est. recovery ~3.5 h/wk. | ||||||
| Plant 07 | 3 | 3 | Capper 2 | 205 | Infeed jam | "caps bridging at infeed" |
| Plant 07 | 3 | 1 | Labeler 1 | 148 | Misfeed | "roll L-22 again" |
| Plant 07 | 3 | 2 | Conveyor 3 | 96 | E-stop | "belt slip, product backed up" |
| Plant 07 | 2 | 2 | Detector 1 | 61 | False reject | "metal detect over-sensitive" |
| Plant 07 | 2 | 1 | Filler 2 | 48 | Seal temp | "weak seals on the line" |
A designed walkthrough of the deployed assistant. The operator types one question; underneath, an orchestrator fans the work out to specialized agents across MES, sensor logs, ERP and Salesforce, then fans it back into a chart, a sensor read, a live table, and a work order, all in plain English.
On a connected line the data that explains a stoppage already exists. It is just scattered across the historian, the MES, fault codes, and a maintenance tech's free-text shift note. Unplanned downtime runs around 20% of total production time, and the people closest to it can't get answers, because the answers sit behind ERP screens and BI dashboards they were never trained to drive.
Off-the-shelf chatbots don't close the gap either. They reason over static text; they can't reach a live plant database, don't speak the plant's vocabulary, and can't take an action. The floor needed something that could read the live system and reason about it in the plant's own language.
I scoped and led the build of the Manufacturing Copilot: a conversational assistant with real-time access to live plant systems. An operator asks a question the way they'd ask a colleague; it works out what they mean, plans the analysis, queries the production systems itself, and hands back readable insight, a chart, a ranked cause list, a recommended fix, instead of a wall of SQL.
"It can generate and act on solutions by reading the latest operational data, a more direct way to cut downtime than any dashboard."
The operator sees one plain-English answer. Underneath, an orchestrator breaks the question into a plan and hands the pieces to specialized agents that fan out across the plant's systems and fan back in:
Because the agents are grounded in how the plant's data is actually structured, they can narrow a fault the way an engineer would, from "Line 3 keeps stopping" to "the 23:00 changeover's purge step." New data, like scheduling and forecasting, drops into the same frame, moving the system from explaining downtime toward heading it off.
The manuals are not just text. We ingest the OEM PDFs, photos and exploded diagrams, build multimodal embeddings over both the words and the images, and index them in a vector database. So when a fault traces back to a torque spec or a purge sequence buried in a diagram, retrieval-augmented generation pulls the right step, image and all, instead of guessing, keeping every answer grounded in the actual procedure.
It looks like a chat. Underneath, every question runs through an agentic platform, an orchestrator that plans the work and dispatches a team of specialized agents across four live enterprise systems, each speaking that system's own schema and protocol, then reconciles their answers into one grounded response.
This was a joint effort with the client's Innovation & Technology group. I ran the discovery that scoped where generative AI could actually help on the floor, designed the multi-agent architecture, then led a cross-functional team, ML engineers, data engineers, plant operations leads and enterprise IT, from prototype to a pilot running against live plant systems. A big part of the job was the unglamorous half: data access, guardrails, and a hand-off the plant could own.
Beyond the headline savings, the system compounds into labor efficiency, raw-material conservation and quality, an early, concrete step toward end-to-end automation across a connected plant.