Tacit AI connects maintenance, inspection, operating, and engineering evidence to proposed risk, maintenance, resilience, and capital actions across treatment and networks. Utility experts approve every material decision.
Structure source-linked failure modes and consequences for pumps, blowers, clarifiers, dosing systems, valves, and network assets. Operations and water-quality owners validate barriers, dependencies, and public-health implications.
Bring redundancy, isolation, access, spares, repair history, and customer impact into a common review. Tacit AI supports scenario preparation; incident command and operating decisions stay with the utility.
Compare renewal candidates using utility-approved condition, consequence, resilience, service, environmental, and cost criteria. Each recommendation exposes its data limitations and remains subject to investment governance.
Assess current tasks against observed failures, inspections, OEM sources, and local operating context. Proposed gaps, overlaps, and interval changes move through existing engineering and document controls.
Show where asset identity, location, condition, failure coding, or operating context is incomplete. Cited answers and proposed revisions link back to approved work orders, manuals, drawings, inspections, and procedures.
Agree access, hosting, data residency, integrations, and model boundaries with utility security and technology owners. Tacit AI proposes operating content; only authorized utility personnel can approve and issue it.
Run a scoped baseline evaluation on one service or asset decision, measuring source coverage, expert corrections, review effort, criticality rationale, and usefulness to a maintenance or capital decision.
Built for utility executives, asset and capital-planning leaders, operations and maintenance teams, resilience owners, engineering authorities, and water-quality professionals balancing continuity, affordability, environmental duties, and long-lived infrastructure.
Use whatever approved history exists across CMMS, GIS, inspections, condition records, incidents, and engineering documents. Tacit AI makes fragmentation and missing context visible instead of inventing completeness.
Prepare an initial risk and maintenance baseline from design, vendor, commissioning, and operating-intent sources, with assumptions clearly marked for validation after handover.
Tacit AI can organize evidence for asset-management and FMEA workflows. Drinking-water, wastewater, environmental, resilience, and reporting obligations are jurisdiction-specific and remain with the utility and its authorized professionals.
The utility sets the operating boundary, evidence rights, reviewers, and acceptance criteria before the evaluation begins.
Customer-defined
Select one treatment process, pumping system, or network zone and one maintenance, resilience, or capital decision. Agree sources, consequences, reviewers, and current effort.
Gate
Proceed when scope, data use, decision rights, and acceptance method are approved.
Bounded evaluation
Produce source-linked analysis, expose evidence gaps, and record expert corrections, review effort, and usefulness to the selected utility decision.
Gate
Expand only if utility reviewers accept the evidence quality and decision value.
Approved rollout
Add processes or network areas under agreed configuration, security, integration, revision, and approval controls. New evidence creates reviewable proposals.
Gate
Continue while service value, governance, and source quality remain acceptable.
Bring one service boundary, the evidence you trust, and the maintenance or capital decision you need to improve.
