No. Tacit AI prepares cited analysis and specific change proposals; qualified engineers determine scope, ratings, controls, applicability, and release. Every accepted or rejected proposal is attributed and versioned. The value is leverage: specialists review structured evidence instead of assembling the first draft manually.

Data quality is scored field by field before analysis, so missing identity, dates, failure coding, and operating context remain visible. Messy records, PDFs, and spreadsheets can still be used within those limits. For greenfield work, approved specifications, BOMs, OEM manuals, and customer-selected reference libraries provide the starting evidence. See how standardization works

Keep them. Tacit AI compares existing analyses with approved work orders, failure history, inspection results, and source documents. The review package surfaces possible missing failure modes, unsupported ratings, stale assumptions, and controls that may warrant reassessment. Your team decides whether the controlled analysis changes.

Generic assistants can produce plausible text, but they do not provide a governed engineering workflow. Tacit AI links proposed rows to page-level evidence, scores source quality, applies controlled taxonomies, records reviewer decisions, and flags source changes for reassessment. See the full comparison

Recommended path: VPC deployment in your Azure, AWS, or GCP account, live in one day, with a dedicated database per customer. On-premise installation with your own models for production. Fully disconnected deployment is assessed against your infrastructure and authorization requirements. Connect to your existing enterprise LLM gateway (Azure OpenAI, Bedrock, internal endpoint) or use ours; model choice is yours and the risk baseline is portable. Data stays inside your approved boundary. Encryption at rest and in transit, MFA, RBAC, audit logging, and full data export are supported.

Start with one system and named reviewers. Deliver cited output in familiar formats such as Excel or PDF, or use a controlled PLM/CMMS integration after validation. Measure corrections, acceptance, review effort, and decision usefulness before expanding. If the output does not meet the agreed standard, the pilot stops.

Tacit AI preserves the evidence and decisions that were actually recorded: source citations, corrections, reviewer dispositions, applicability rules, and change history. It does not invent rationale that was never documented; it flags the missing support for expert resolution. That gives the next engineer a reviewable trail instead of an unexplained spreadsheet.

The pilot starts with your baseline: current engineering hours, analysis backlog, review cycle, repeat-event cost, or production exposure. Tacit AI reports observed review effort, accepted coverage, and decision value for the selected system. The expansion case uses your measured results—not a generic ROI assumption.