Plant leaders
One morning question replaces five status meetings.
// reference implementation
Ask your planta question.
An industrial AI copilot that answers operational questions from live meter, machine, and automation data. Every number is read from the plant, never invented by the model.
Plant Copilot
Live data · never estimated
Which machines consumed the most last month, and what did that cost?
Top 5 energy consumers last month
| Rank | Machine | kWh | Cost | Share |
|---|---|---|---|---|
| 1 | Line 1 Main Drive | 2,015,552 | 362,799 | 40.7% |
| 2 | Line 3 Drive Motor | 851,840 | 153,331 | 17.2% |
| 3 | Compressor A | 485,768 | 87,438 | 9.8% |
| 4 | Extraction Fan A | 325,537 | 58,597 | 6.6% |
| 5 | HVAC Unit | 270,350 | 48,663 | 5.5% |
Key takeaways
- Line 1 Main Drive accounts for more than 40% of total metered energy.
- The top 5 machines represent roughly 80% of plant consumption.
- Compressor A shows a compressed-air efficiency loss worth investigating.
5-10%
energy-cost reduction potential
6.7%
billing-factor error found in reference data
30d
bearing damage predicted before failure
21
machines monitored in the reference plant
The use case
Normal language in. Defensible numbers out.
Operators, energy managers, and plant leadership ask questions that usually require dashboards, exports, and meetings. The copilot runs deterministic queries against live data, explains the cause, and attaches the operational recommendation.
query
Why did costs rise yesterday?
query
Which machine is likely to fail next?
query
How do we avoid the next load peak?
query
Which invoice is wrong?
query
Where do we lose energy after hours?
query
What should we fix first to save the most?
Architecture
The AI talks. The meters remain the source of truth.
Language models translate intent. Agents execute the work. Retrieval grounds recommendations in manuals, procedures, and standards. A verification layer rejects any answer that cannot trace its numbers back to plant telemetry.
01
Devices
02
Gateway
03
Digital twin
04
AI agents
05
Operator questions
Deterministic queriesLive telemetryTraceable sourcesRole-based accessOn-premises or private cloud
Operating value
Built for the people carrying the plant.
Energy managers
Waste, penalties, and invoice defects are found and priced.
Maintenance
Failure risk is named before it becomes urgent.
Management
Savings and risk appear in money, on demand.
Proof, not theatre
Reference plant, not invented client mythology.
The implementation runs on FairPlay Digital reference data: two sites, 21 monitored machines, and 90 days of 15-minute measurements. It demonstrates invoice checking, anomaly detection, load-peak avoidance, predictive maintenance, and natural-language operations without claiming a customer project where there is none.
reference plant
2 sites · 21 machines · 90 days
15-minute meter intervals
Invoice checking against plant telemetry
Predictive maintenance from electrical signatures
Load peak and power-factor penalty detection
// engage
Want your plant to answer?
We can show the reference implementation live and identify which data sources should be connected first in your environment.