Tacit AI brings work orders, inspection findings, operating history, and engineering documents into a governed reliability workflow for generation and transmission and distribution assets. Engineers review every proposed FMECA, maintenance, and outage-planning change.
Connect failure history, defects, operating context, and consequence assumptions to a reviewable asset-risk baseline. Leaders can see which proposed actions address generation loss, network impact, safety, or cost.
Prepare source-linked FMECA and maintenance evidence for turbines, generators, transformers, switchgear, and balance-of-plant. Planners decide what belongs in an outage, what can be monitored, and what requires further engineering.
Compare existing preventive and condition-based tasks with observed failure mechanisms and source requirements. Tacit AI proposes gaps, overlaps, and interval questions; asset owners approve any change through existing controls.
Bring legacy records and current operating context together to examine obsolescence, duty changes, and life-extension decisions. Remaining-life implications require sufficient condition and engineering data and remain subject to specialist approval.
Use a common failure taxonomy while preserving OEM, configuration, duty, site, and operating differences. Reuse is proposed with its source and exceptions visible; local engineering authorities decide whether it applies.
Search manuals, inspections, work orders, and prior analyses with cited answers. Proposed revisions retain links to the underlying source, assumptions, reviewer comments, and approval status.
Run a scoped baseline evaluation on one agreed asset system, measuring source coverage, engineering corrections, decision usefulness, review effort, and compatibility with your document-control process.
Built for generation and network executives, asset management, reliability and maintenance leaders, outage managers, and engineering authorities who need a defensible line from operating evidence to asset decisions.
Use available work orders, inspections, condition data, manuals, and prior studies to test risk and maintenance assumptions. Tacit AI identifies evidence gaps rather than treating missing history as fact.
Prepare an initial reliability baseline from OEM, design, commissioning, and engineering sources. Assumptions are explicit and are revised only as authorized operating evidence becomes available.
Tacit AI can structure FMEA/FMECA and asset-management evidence for customer workflows. These references do not confer regulatory compliance; applicable obligations, including any NERC requirements, remain governed separately by the utility.
Scope, baselines, reviewers, security boundaries, and decision rights are agreed before data is processed.
Customer-defined scope
Select one asset system and decision, inventory approved sources, record the current review effort, and agree validation criteria and authorized reviewers.
Gate
Proceed when scope, evidence rights, baseline, and acceptance method are approved.
Bounded evaluation
Produce source-linked analysis and proposed actions. Engineers record corrections, unsupported assumptions, review time, and whether outputs improve the chosen outage or maintenance decision.
Gate
Expand only if customer reviewers accept the evidence quality and decision value.
Approved rollout
Add assets or sites with configuration rules, integration controls, named approvers, revision history, and periodic checks against the agreed operational baseline.
Gate
Continue while value, governance, and source quality remain acceptable to the asset owner.
Bring one asset boundary, the sources you trust, and the outage or maintenance decision you need to improve.
