Line analytics and the digital twin.
Real-time sensor data from sauce and condiment lines becomes a live twin dashboard. In production at a global manufacturer's plants.
Solutions · Manufacturing & Plant Operations AI
Query ten thousand factory tags in plain language
A coworker for plant operations that reads sensor data, builds digital twins, and writes shift reports from real-time telemetry.
Ten thousand tags. One question.
- 10,000 tags queried in plain language
- Historian and OPC/MQTT sources
- Grafana
- Databricks
- Snowflake
- SAP extracts
- Runs in your environment
- Approval before action
The problem
Data everywhere, no interface to ask
Ten thousand tags. A Grafana wall nobody reads at 2 AM. PLC logic that one engineer understands. A kitchen-batch simulator built by hand in Excel that breaks when the formula changes.
The information is there. The interface is a ticket to the controls engineer.
Ask the plant a question. Get an answer with the tag it came from.
How it works
Connect. Ask. Watch. Report.
Connect the sources. Historian, OPC or MQTT via connector, Grafana, Databricks or Snowflake, SAP extracts. Each engineer's own permissions.
Ask in plain language. Tags, streams, and logic queried through a semantic model of the line: stations, products, shifts.
Watch. A live digital twin from real-time sensor data. Alerts to the channel where the shift lead reads, with the tag and the trend.
Report. Shift data to the plant manager's deck, weekly, in the template, every number cited. Recommendations go through approval before anything is written back.
Example workflow
Example workflow: Shift lead queries evaporator temperature
Use cases
Live twins, telemetry Q&A, and shift reports
Telemetry Q&A.
Ten thousand factory tags queried in plain language. Grafana streams inspected. PLC ladder logic debugged with the controls engineer, who says the agent found the interlock in minutes.
Batch simulation and critical inputs.
Kitchen-batch automation simulations and digital twins of critical inputs across dozens of factories.
R&D on the same platform.
Rheology research threaded through the same platform as the plant data, so the lab and the line share a vocabulary.
The plant manager's deck, written from shift data.
Kitchen-batch automation simulations auto-fed from the ST-One historian evaporator tag, with technical specs and test procedures written by the agent. The process engineer runs it for a week.
Escalation to a human, with the evidence attached.
An alert fires at 2 AM. The agent posts the tag, the trend, the last similar event, and the SOP step to the shift channel. The shift lead decides.
What customers say
“He wrote the entire stuff. By itself. And I was there, and I was thinking, alright. If I had to write all this stuff, I mean, I would've spent days, days, days.”
“I send that to the programmer and he said, alright. I read your file. I copied the section of the code because he has the right interface, and it works. Wow. Just like magic.”
“They said that the human cannot multitask, which is true, but Athena can. Without any issue. So I have on a regular basis four or five sessions open.”
“I am not a programmer. I cannot read a line of program in SQL or anything... The only thing that I'm doing is just writing prompts.”
“It's good to understand the capability of the platform. That how far, and how quickly you guys can get these things going [...] that has been impressive overall.”
“Lets us get a lot more out of our data than [...] We can sit on top of all of the data in [our data warehouse] as opposed to just, like, our little ingested, you know, three gigabytes semantic models. I think we have a lot of power here.”
“Use the Athena platform probably closer to the concept and how it was designed, like, with the spaces architecture where you've got a set of primitives and capabilities, and you bring together the right set of capabilities to solve a task.”
“Digital worker, it gets spun up, it gets given access to the toolkits that it needs. It gets given access to the data that it needs. Just exists for the period it needs to exist to perform the task. And then it gets torn back down again afterwards.”
“I like that direction, that concept of the agent is the agent. It becomes an entity in its own right with its own ownership and its own missioning, and you get rid of the gray area and the blurry lines.”
“It was PowerPoint... there was no kind of distinction between the two. So that was killer. And I literally had a meeting on it today, and people said how great it was.”
Verbatim from customer calls. Customers anonymized.
Platform
Manufacturing & Plant Ops on one platform
Agents for the question at 2 AM. Workflows (Agent Operating Procedures) the recurring: the digital twin refresh, the alert rules, the plant telemetry. Applications for the many: the twin dashboard every shift lead opens on the floor with access included in Athena services.
Stack
Historians, OPC, Grafana, and your plant network
Historians and OPC/MQTT sources via connector, Grafana, Databricks, Snowflake, SAP extracts, Teams. Runs in your environment, on-prem or air-gapped if the plant network requires it.
What to plan for
Athena does not control the line. It reads, reasons, and recommends. Any action on plant systems goes through your approval and your controls team.
FAQ
What can AI agents do on the plant floor?
Answer questions from sensor and production data, help investigate line and quality issues, and prepare shift and production reports.
Do operators need technical skills?
No. They ask in plain language and get the answer with the data behind it.
Can agents change equipment settings?
Only through actions you approve. Agents analyze and recommend; people decide.
Where should we start?
With one line or one recurring investigation.
One platform underneath
AGS · Athena Governance System
Records every change by a person or an agent, rolls back one contributor's edits without losing anyone else's, and keeps the model, instructions, and sources behind each agent action.
Palladium · deployment
How the platform is deployed: Athena's managed cloud, your cloud on AWS, GCP, or Azure, on-prem, air-gapped, or GovCloud. Same platform in every option.
How it fits together
Build, Work, and Data on top; 150+ connectors and every surface in and out; one map of the whole platform.
Related
Related products and stories
Athena Library
Run this on a schedule
Ready-made operations briefs you can run on a schedule.
Bring one line's tag list.
We will build the twin, ask it a question, and cite the tag.