Tacit AI turns maintenance, deviation, and CAPA evidence into source-traceable FMEA proposals—reviewed before anything enters a validated workflow.
Bring work orders, deviations, CAPAs, PM history, manuals, and procedures into one evidence view. Tacit AI proposes failure modes and priorities with links back to the records used, so teams can focus on consequential equipment risk.
Compare new events with approved risk analyses and flag possible coverage gaps for investigation. Tacit AI does not close deviations or determine CAPA effectiveness; authorized Quality and Engineering reviewers retain those decisions.
Review proposed FMEA rows, ratings, controls, and source citations before approval. Accepted changes can enter the customer’s document-control and change-control processes; rejected proposals remain outside the controlled record.
Approved content can be proposed for comparable equipment classes, while site, process, duty, configuration, and qualification differences stay visible. Each receiving team reviews applicability before inheriting any conclusion.
Begin with bounded data extracts and controlled outputs instead of changing validated source systems. Integration, deployment boundary, access, Part 11 applicability, and validation responsibilities are agreed with the customer’s Quality and IT teams.
Measure completeness, source coverage, and terminology consistency across work orders and PM records. Show where weak documentation limits equipment-risk decisions so teams can improve the evidence at its source.
For Manufacturing, Quality, Engineering, and Reliability leaders making controlled equipment-risk decisions.
Use selected work orders, deviations, CAPAs, PM records, and approved documents to test whether the current equipment-risk baseline reflects known operating evidence.
Use OEM manuals, specifications, qualification evidence, and approved process information to prepare a draft baseline. The customer’s validation, Quality, and Engineering owners decide acceptance and use.
Applicability and final compliance decisions remain with the manufacturer and its authorized personnel.
Before work starts, your team agrees the evidence set, baseline, reviewers, output status, and expansion criteria. No proposal enters a controlled record without the customer’s authorized approval.
Choose one system or line, relevant records, intended decision, system boundary, and accountable Quality and Engineering reviewers.
Tacit AI prepares source-linked FMEA and data-quality proposals. Named reviewers assess relevance, unsupported assumptions, and fit with site methods.
Add equipment classes or sites with local exception review, documented approvals, integration controls, and ownership for future revisions.
Bring a bounded set of work orders, deviations, CAPAs, and the current FMEA. We will define what a useful, source-traceable draft must show.
