Sequence and symptoms
Event chronology, alarms, operating changes, process conditions, symptoms, inspections and immediate response.
AI-powered Root Cause Analysis
Stop spending months hunting for the cause.
AI connects the evidence. An engineer challenges the causal chain and signs it.
Inputs
Bring the evidence out of timelines, systems and people's heads.
We agree the minimum evidence needed to test plausible causes.
Asset and work context
Prior failures, work orders, maintenance changes, condition trends, modifications and repeat interventions.
Documents and people
Manuals, procedures, drawings, photographs, lab results, interviews and existing hypotheses.
AI-connected evidence
The RCA becomes a traversable chain.
Every step stays linked to the evidence for and against it.
EventSymptomFailure modeMechanismCauseConsequenceAction
EventP-204 forced outage
SymptomRising vibration and temperature
MechanismAccelerated bearing wear
CauseLubrication delivery degraded under duty
ActionDesign control + task update · validation pending
Workflow
A disciplined RCA, with the manual grind compressed.
AI retrieves and connects. It never replaces hypothesis testing.
Scope the event
Set the problem statement, boundaries, impact and investigation team.
AI maps the evidence
Connect timelines, records, signals, observations and source documents.
Navigate causal paths
Relate symptoms to modes, mechanisms, contributing conditions and causes.
Engineer tests hypotheses
Challenge support, contradiction, gaps and confidence for each proposition.
Approve actions
Validate the causal chain and assign corrective actions, owners and checks.
Deliverables
A decision package your team can inspect.
The reasoning stays visible, from event to action.
- Validated problem statement and event timeline
- Evidence register with source lineage
- Tested causal chain and rejected hypotheses
- Contributing factors and uncertainty register
- Corrective action plan with owners and verification
- Engineer-reviewed RCA report and graph view
What remains connected
The next investigation starts with accumulated plant knowledge.
The reviewed causal model and its evidence stay in Factory Brain. Recurrence gets recognized.
- Event and symptom pattern
- Confirmed and rejected causes
- Evidence and source links
- Known failure mechanisms
- Actions and verification status
- Engineer approval record
Human validation
AI maps the evidence. Engineers own the conclusion.
Every deliverable is engineer-reviewed. Final approval on safety and plant changes stays with you.
R//Safe
Never trains a public model
Source-linked outputs
Engineer-validated
You keep the knowledge
Weeks, not months
Resolve one failure without losing months to the search.
Bring the event, the evidence and the people who know the equipment.
Start an RCA case