Operational Intelligence for the World's Critical Systems
Muun AI turns existing machine and operational data into actionable intelligence. See problems earlier, understand them faster, and direct the next practical action while operators remain in control.
Batch timing
Where does Muun AI fit?
Choose the operation closest to yours. Each profile shows the data, equipment, source, decision, and matching public demo.
Repeatable batch operations
Cycle data becomes ramp, target-band, hold-window, and cycle-end evidence.
- Equipment pattern
- Batch Process Equipment
- Data source
- Files and exports · Historians and databases
- Decision supported
- Throughput or schedule tuning · Quality hold or release · Energy review
Continuous utility or process loops
Steady streams become baseline, productive-load, spike, and recovery windows.
- Equipment pattern
- Utility / Process Loop
- Data source
- Historians and databases · Live operating signals
- Decision supported
- Energy review · Process adjustment · Throughput or schedule tuning
Run-to-run asset trajectories
Ordered runs become degradation, drift, and review candidates before failure labels exist.
- Equipment pattern
- Rotating Equipment
- Data source
- Historians and databases · Site collectors
- Decision supported
- Maintenance review · Service escalation · Support report
Fluid dosing and delivery systems
Dose windows become stable-flow, drift, and off-target evidence for process review.
- Equipment pattern
- Fluid Dosing System
- Data source
- Live operating signals · Historians and databases · Site collectors
- Decision supported
- Process adjustment · Service escalation · Support report
Connection options
Works with the data you already have
Files and exports
Start from operational files teams already pull from machines, historians, or quality systems.
Best fitBest when the first goal is evidence, segmentation, or a compatibility read before live integration.
Representative connections
- CSV drops
- Process event logs
- Historian exports
- Quality spreadsheets
Let's find the right integration path for your operation.
Share a sampleWhy Muun AI
Find the operating margin hidden in your data, then prove it phase by phase.
Each phase earns the next
Start with evidence. Add prediction or automation only after the value is clear.
Find the opportunity
Recover cycles, baselines, and variation from the data already available.
Explain the loss
Test where energy, time, quality, or reliability is being lost and why.
Recommend the next move
Prioritize the highest-value action, with bounded forecasts and human review where needed.
Measure and extend
Track the result, capture operator feedback, and extend what works to similar lines or sites.
FAQ
What does Muun AI do?
Muun AI converts machine and process data into inspectable operational evidence. Depending on the available evidence and validated deployment profile, that can include labelled process states, forecasts, anomaly candidates, and risk-aware recommendations.
What data does Muun AI need?
Muun AI works best with files, historian/database records, live operating signals, or site-collector feeds that include stable asset identity, timestamps or ordered cycle counters, physical process signals, and enough continuity to recover cycles, windows, phases, or degradation patterns.
Does Muun AI require historical failure labels?
No. Muun AI is designed for label-sparse environments where failures are rare and manual annotation is expensive.