The Agentic Troubleshooting System

Troubleshoot
Proactively.

The Agentic Troubleshooting System for energy and chemical plant operations. A system of agents that work in concert to detect and mitigate anomalies, so your plant operates harmoniously.

Meet the Agents

Refining · Fertilizers · Petrochemicals · Ammonia · Sulphuric Acid · LNG · Syngas · Speciality Chemicals

Trusted by leading operators
What is ControlRooms?

Alarms tell you after. We tell you before.

Traditional alarms wait for a threshold to break. ControlRooms inverts the model — Large Harmony Models evaluate whole-system behavior to surface anomalies well before alarm limits are crossed, then arm operators to mitigate them.

four agents working in concert
to keep your plant in harmony.

01
Detect
Large Harmony Models surface the unknown unknowns — hours before alarms fire.
02
Advise
Knows first principles, knows your plant, knows now — in app, Teams, and mobile.
03
Log
A living plant memory — effortless voice and text capture that never evaporates.
04
Handover
AI-drafted shift summaries and audio recaps — full context, zero manual reporting.
Detectfinds it
→
Advisehelps solve it
→
Logrecords it
→
Handovershares the context
The Problem

Your DCS is reactive.

Analytics tools don't prevent the next event. Five gaps cost plants millions.

Static, lagging alarms

Thresholds trip only after a limit is crossed, meaning no head start.

Laborious issue resolution

Needle in haystack searches, manual trend hunting, hundreds of SOP pages — slow response during live events.

Production loss

Surprises (the bad kind) lead to yield loss, costly repairs, or worse.

Context loss across shifts

Shift observations evaporate into ad-hoc notes — the next crew loses the story.

The great retirement

Seasoned operators who know how the plant should run are retiring — risking institutional knowledge loss.

R-104 · Column System · 3 Correlated Tags
Large Harmony Model · multivariate · 48-hr window
ANOMALY
LIMIT AI DETECTED 14 hrs before alarm C3 Pressure · Inlet Temp · ΔP — breaking harmony 06:00
Normal operation
Harmony break — AI flagged
Limit
Agent 01

Detect

Large Harmony Models are the core troubleshooting mechanism. Detect learns the multivariate relationships across entire systems, flagging anomalies to expected plant behavior and surfacing the unknown unknowns no alarm rule could anticipate.

  • Monitors every single tag in your plant, continuously
  • Flags multivariate anomalies in real time, hours before alarms
  • Search trends of similar patterns across 20,000+ tags in seconds
  • Low false positives — hands precise windows to Advise
14 hrs
before the alarm
would have fired
Agent 02

Advise

Ask this conversational sidekick anything the moment an anomaly fires. Advise grounds Detect's findings in your SOPs, logs and design docs — the best ops engineer and superintendent in one, 24/7.

  • Plain-language root cause and ranked hypotheses
  • Step-by-step actions. SOPs matched by meaning, not keywords
  • In-app, Teams, or mobile
  • Feedback continuously improves answers
  • "This is priceless…" — Plant Manager
Advise · Troubleshooting SidekickLive
R-104 Column 3 Pressure Anomaly · Active
What's causing this pressure rise?
Root Cause Hypothesis
Progressive shell-side fouling on HX-17. Pattern matches March 2024 event — same seasonal inlet temp drop, same rate of climb.
SOP Match · Rev. 14
Check V-312 valve packing at next walkround
Schedule HX-17 cleaning — next maintenance window
Escalate if rate exceeds 0.5 MPa/hr
Historical Match
3 similar events · Mar 2024, Nov 2023, Jul 2022 · Avg resolution: 4.2 hrs
Ask a follow-up question...
SEND →
ALSO IN
TEAMS
MOBILE
Feedback trains your agent
Agent 03

Log

Every observation — voice or text — transcribed, tagged to the live anomaly, and stored as searchable plant memory. Knowledge stops evaporating. Memory of past events resurfaces when the pattern repeats, and context flows into the handover.

  • Voice notes that are auto-transcribed, zero typing
  • Annotated directly onto live tag trends, stored permanently
  • Feeds directly into the Shift Handover
Voice / Text Note
→
AI Transcribes & Tags
→
Shift Report
Log · Intelligent LogR-104 · Active
Tom Chen · Night Shift
02:14 AM
"V-312 feels stiffer than usual during my last walkround — could be a packing issue."
R-104 Anomaly ContextAI Transcribed
Keisha Williams · Engineer
03:42 AM
"Confirmed fouling on HX-17. Scheduling cleaning for Saturday 06:00 maintenance window."
R-104 Resolution Notes→ Shift Report
Hold to record — auto-transcribed & logged to active anomaly
Agent 04

Handover

Your complete operational picture, auto-built every shift. AI synthesizes live data, logs and voice notes into a structured report and audio recap, ready for sign-off with no manual reporting needed.

  • Auto-populated from live data with zero manual entry
  • AI-drafted executive summary for sign-off
  • Logs and voice notes flow in automatically
  • Alerts, watchlist, key metrics — all structured for the next shift
  • Stored as a searchable annotation
Shift Report
Springfield Methanol · Auto-generated by ControlRooms AI
Night Shift · 18:00 → 06:00
September 3, 2026
AI Executive Summary
Two AI-detected anomalies this shift — both flagged well ahead of alarms. R-104 column pressure began a multivariate deviation at 23:07; root cause identified as progressive shell-side fouling on HX-17. Resolved during the 03:00 maintenance window — all three tags returned to baseline by 03:42. Pre-reformer feed train (FT-201 / FV-201 divergence) flagged at 01:20 and remains active — within monitoring limits, but day-shift FV-201 packing inspection is required. LOTO active on P-103 (seal replacement, expected clearance 08:00). No safety incidents. No environmental exceedances. All quality samples within specification.
▼Watchlist3 Items
R-104 Column System
23:07 → 03:42 · 3 tags
RESOLVED
C3-PRES.PV_Out · COLUMN 3 PRESSURE
TI-301.PV · INLET TEMPERATURE
PDI-405.PV · DELTA-P HX-17
Sep 3 18:00Sep 4 06:00
Pre-Reformer Feed Train
01:20 → ongoing · 3 tags
ACTIVE
FT-201.PV_Out · FEED FLOW
FV-201.MV · VALVE POSITION
TI-204.PV · INLET TEMP
Sep 3 18:00Sep 4 06:00
P-103 Feed Pump
LOTO 01:47 · Clearance 08:00
LOTO
P-103-SPEED.PV · PUMP SPEED
P-103-PRES.PV · OUTLET PRESSURE
P-103-AMPS.PV · MOTOR CURRENT
Sep 3 18:00Sep 4 06:00
▼Key Tags · Handover Values
C3-PRES.PV_Out9.82 MPa ↓
TI-301.PV342 °C
PDI-405.PV4.2 MPa
FT-201.PV_Out287 t/h ↑
FV-201.MV38.2 %
TI-204.PV318 °C
P-103 Speed0 RPM · LOTO
AT-301.PV93.2 %
FI-301.PV2,841 Nm³/h
Shift Timeline — Night Shift 18:00→06:00
Anomaly
Advise / Voice
LOTO
Log / System
23:07
Anomaly Detected — R-104 Column Pressure
Multivariate harmony break across 3 tags · 14 hrs before alarm
23:12
Advise Analysis
Pattern matches HX-17 shell-side fouling — March 2024 event · Recommend V-312 inspection · Escalation threshold: 0.5 MPa/hr
01:20
Anomaly Detected — Pre-Reformer Feed Train
FT-201 / FV-201 position divergence · Monitoring in progress
01:47
LOTO — P-103 Feed Pump
Seal replacement in progress · Expected release 08:00
02:14
Voice Note — Tom Chen
"V-312 confirmed stiff on walkround — packing gland needs service. Flagged for day shift."
03:42
Resolved — HX-17 Shell-side Fouling
All 3 tags returned to baseline · Cleaning scheduled Saturday 06:00
05:30
Shift Report Auto-Generated
All events, voice notes & anomalies compiled by ControlRooms AI
What Makes Us Different

Built for the control room

A Process-First approach that keeps your plant in harmony 24/7.

Operations Reliability & Maintenance Weeks–Months Minutes–Days
ControlRooms
Seeq
Cognite
AspenTech
Palantir
Process. Real-time. Operations.
The Compounding Advantage

The Context Flywheel

Every interaction your operators have with ControlRooms builds context — and that context makes the agents smarter, catching more issues earlier and guiding faster resolutions.

context
loop
01
Anomaly Detected
AI flags the deviation before alarms fire
02
Operator Responds
Labels, voice notes & root cause captured
03
Context Captured
Living knowledge, stored & searchable
04
Agents Get Smarter
Catch more issues; resolve faster
More context› Smarter agents› Catch more, earlier› Faster resolution↺
Proven at Scale

Real savings, in production today

In production at major operators worldwide — high-asset-density plants where downtime costs millions a day.

$25M
publicly traded customer projected incremental EBITDA by 2030
Tens of $M
in realized savings annually across deployments
30+
live deployments in refineries, ammonia & petrochemicals
<28 days
to live anomaly detection & troubleshooting
Detection in Practice

Case Studies

Real anomalies surfaced hours before conventional alarms.

Case Study 01
Pump Cavitation
Drift across suction pressure and motor current surfaced cavitation long before vibration alarms — avoiding an unplanned trip.
Caught early
Case Study 02
Converter Anomaly
A harmony break across converter bed temperatures alerted operators before a trip — an upset no single-tag limit would have caught in time.
Whole-system
Case Study 03
Fan Current Fluctuations
Oscillation in fan motor current — invisible to static thresholds — surfaced as an early mechanical signature, enabling a planned fix.
Early signature
The Agentic Troubleshooting System

Troubleshoot Proactively.