PROPOSED AI APPLICATION

Inspection report draft assistant

Qualified teams repeat narrative preparation

Engineering inspection and NDT firmsSpecialist evaluationProposed solution scope

The problem and the proposed outcome

Application: Engineering inspection and NDT firms — Inspection report draft assistant
Problem to solve: Qualified teams repeat narrative preparation
Target buyer: Inspection service firms, qualified inspectors
Intended use: Employee / expert own workflow; internal business solution
Benefit to test: Test whether approved drafts require less preparation effort at the required factual standard.
Potential delivery: Internal application, reviewer dashboard, private assistant or authorized user portal; choose one surface after validating the workflow.

AI method: Language drafting and summarization
Method code: L
AI contribution: Use a language model to organize permitted facts into a requested draft or summary; link facts to inputs and flag unknown details.
Output artifact: Editable draft or summary grounded in supplied facts.
Core modules: Approved facts / brief → draft generation → factual checks → human approval
Simple baseline: Reusable text templates, manual writing and rule-based mail merge.
Why test AI: Could help when language and structure vary between cases; template filling remains a useful lower-complexity baseline.

Pilot track: Specialist evaluation
Required data: Inspector-approved findings; controlled templates
Data readiness: Verified source facts, content rights, an approved template and examples of acceptable drafts are needed.
First development step: Gather permitted source-to-approved-draft examples and establish a factual review rubric.
Initial MVP scope: One defined task, one permitted dataset and one reviewer workflow. Manual upload or authorized export first; no unattended external action. Validate the model before live integration.
Demo data: Synthetic or licensed public samples may illustrate the workflow. Real benefit and model performance need representative authorized held-out data.
Buyer validation question: Ask Inspection service firms, qualified inspectors: show a recent case where qualified teams repeat narrative preparation, record the present effort or error, and confirm whether the required data can lawfully be used.

Primary success metric: Qualified-reviewed preparation time
Model evaluation: Assess factual consistency, completeness, unsupported statements, reviewer edits and approved preparation time.
Metric target: Set after baseline measurement; no improvement or accuracy result asserted.

Specialist review: Yes
Human review boundary: Qualified domain specialist reviews evidence, interpretation and any consequential action. Start with controlled offline or shadow evaluation; no autonomous clinical, financial, employment, industrial or safety decision.
Domain review: Qualified technical teams approve specifications and procedures. The first MVP provides reviewed evidence or planning; it does not directly control machines or safety systems.
Related micro-ERP catalogue IDs: IWA-B-8E7D450EC553; IWA-B-1E5495D6CAC2; IWA-B-A341C8BAEEEA
Integration status: Potential segment link only; no product integration implemented.