Review engineering documents with AI-assisted checks

Start with the operational bottleneck

Engineering handovers accumulate drawings, measurements, photographs, certificates, and client requirements. Review time grows when the team must reconstruct which revision is current and which comment has already been addressed. AI can help organise the review, but document completeness and engineering correctness are different questions.

Define a bounded review package: an equipment service report, an inspection dossier, or a manufacturing handover. List the checks that can be performed from the supplied documents, and the checks that need physical inspection or an engineer’s calculation.

Design the target workflow

Build a manifest of expected files and revisions before generating any summary. Extract text and tables with page references. Use deterministic checks for absent files, inconsistent identifiers, empty required fields, and mismatched revision dates. Ask a model to prepare a comparison of the remaining text-level differences.

Each finding should name the document, page or section, observed difference, and required reviewer. Present the evidence alongside the finding. The model can draft a handover summary from accepted findings, but final approval belongs in the existing document-control workflow. A well-written summary must never silently turn a missing certificate into a complete package.

Connect to the existing systems

Connection	Information	Control
Document control	Package manifest and revision history	Freeze the input set for the review
Extraction pipeline	Text, tables and page references	Flag unreadable scans and low-confidence extraction
Approval system	Findings and reviewer decisions	Keep approval independent of the model

Use stable identifiers and versioned inputs. A retry should resume the same piece of work, with the original permissions and evidence attached. Treat an upstream outage as a visible workflow state.

Fit the model to the task

Use a small local model for classification and short text comparisons first. Escalate unusually long or technically complex sections only after checking extraction and retrieval. Image interpretation is a separate capability: test it on actual drawing types rather than assuming a text model can read every diagram.

Keep a clear human boundary

Reviewers accept or dismiss each finding against its source. Preserve their decision and the revision they reviewed. A new revision should reopen affected checks. The model does not certify compliance, dimensional accuracy, or fitness for service.

Measure the complete operating cost

Separate ordinary file-management gains from model-assisted review. Measure time collecting the package, checking it, and resolving findings. Include false positives: a system that produces many unnecessary warnings can increase the reviewer’s workload despite fast inference.

Pilot checklist

• Create a versioned package manifest.
• Seed missing files, conflicting references, and superseded revisions.
• Require a source location for every finding.
• Measure missed issues and false-positive review effort.
• Retain the existing engineering sign-off and recovery path.

Agree on acceptance thresholds before testing, then compare the proposed workflow with the same work handled through the current process. Expand only after the operation can explain both successful and failed cases.

Further implementation detail

• Choose the lowest-cost model that meets the task requirements
• Serve a local model to a team
• Ollama’s local API reference