See It Work
Documentation Support Trust Center Pricing Partners
See It Work
SYSTEM: OPERATIONAL OT/IT CONNECTORS: 150+ AUTONOMOUS OPERATION: 15+ DAYS GOVERNED AUTONOMY: ENFORCED AUDIT TRAIL: IMMUTABLE INDUSTRIES: ASSET-INTENSIVE & MISSION-CRITICAL DEPLOYMENT: 3-6 MONTHS VIA APEX CONTROL LOOPS: 3,400+ SYSTEM: OPERATIONAL OT/IT CONNECTORS: 150+ AUTONOMOUS OPERATION: 15+ DAYS GOVERNED AUTONOMY: ENFORCED AUDIT TRAIL: IMMUTABLE INDUSTRIES: ASSET-INTENSIVE & MISSION-CRITICAL DEPLOYMENT: 3-6 MONTHS VIA APEX CONTROL LOOPS: 3,400+
Available WATER-WWTP-RCA-ASSISTANT-001 AI Assistant

Root-Cause Analysis AI Assistant

Conversational investigation across historian, CMMS, LIMS, SOPs, and incident history — cuts typical root-cause analysis from days to minutes.

Water & Wastewater Investigation

Target outcome · Root-cause analysis from days to minutes. 70 to 80% reduction in investigation labour per incident.

Business problem

When operational anomalies occur — unexpected turbidity spike, odour complaint, clarifier blowout, alarm cluster — operators and engineers spend hours correlating data across historian trends, maintenance logs, lab results, SOPs, and shift logs to identify the root cause. For complex incidents, full analysis can take days. The information needed is almost always present in the plant's data systems. The bottleneck is the human labour of searching, correlating, and synthesising.

Read more Show less

For an operations team handling 15 to 30 significant investigations per year, manual root-cause analysis typically consumes 200 to 600 engineer and operator hours. It also delays corrective action, allowing repeat incidents while the investigation runs.

What it does

Conversational interface.

Read more Show less

An operator or engineer asks a natural-language question. The Assistant synthesises across historian, CMMS, LIMS, shift logs, SOPs, P&IDs, and incident history, then responds with evidence links. Example interactions: "Why did effluent turbidity spike at 14:30 yesterday?" — The Assistant correlates influent load, chemical dosing, clarifier performance, and similar historical events, then proposes a probable root cause with evidence. "Has this happened before?" — The Assistant retrieves similar incidents with outcome and corrective action.

Current process vs. with AI Assistant

TODAY · INVESTIGATIONREACTIVE
×
What caused incident XHours of manual correlation
×
Has this happened beforeMemory-based or rarely searched
×
Which SOP appliesManual PDF lookup
×
What does an agent team's decision meanAsk an engineer

Outcomes and measurement

Time to identify root cause

Baseline Hours to days
With agent Minutes to hours

Investigation labour per incident

Baseline 10 to 40 hours
With agent 2 to 8 hours

New operator time to competence

Baseline 6 to 12 months
With agent 3 to 6 months

*All figures are typical ranges. Achievable range depends on existing control maturity, data quality, and site-specific conditions.

Data inputs

Historian

all tags

CMMS

work ordersfailures

LIMS

lab results

Other

shift logsP&IDsprocess descriptionsincident history with outcomes

Records

alarm history. Knowledge base: SOPsOEM documentation. Context: audit trails from any deployed Agent Teams

regulatory documents

*Categories only — no tag names or system-specific field references. Exact data mapping is scoped per site.

Scoping questions

Expect these questions in a first scoping conversation. They signal engineering discipline and help narrow the template to your specific site context.

  1. What historian, CMMS, and LIMS systems are in use and what are their APIs?
  2. What SOP and P&ID archives exist and in what format?
  3. How is incident history currently captured?
  4. What data access governance applies?
  5. Who owns root-cause analysis sign-off today?

Want our AI to walk you through these scoping questions?

SPEAK WITH OUR TEAM

Get specialist advice on scoping this for your site.

Our specialists will help you understand how the Root-Cause Analysis AI Assistant fits your operations, what data you'd need, and what a scoping engagement typically looks like.

← Browse all templates