Is one overall accuracy percentage enough for technical certificates?

No. Evaluate business-critical fields separately, especially identifiers, units, decimals and values that influence downstream checks or decisions.

Can safety-critical approvals be automated?

Only where the organisation has explicitly defined and authorised the rule. Mandatory technical, safety, quality and compliance decisions should remain with the appointed decision-maker when required.

Is the 99.5% result a universal guarantee?

No. It is a tested Micro AI result under the tested document conditions. Each production workflow must be validated using representative technical documents and agreed acceptance criteria.

Oil and Gas Technical Certificate Automation Singapore: Extraction, Validation and Human Review

Why technical certificate processing needs field-level control

Technical certificates, Certificates of Analysis, material records, inspection reports and supplier documents can contain dense tables, decimals, units, dates and identifiers. A workflow can be mostly correct and still create risk if one critical value is wrong.

The design should separate extraction, deterministic validation, human review and downstream release.

Technical documentExtract fieldsValidateHuman reviewApproved output

Start with an approved technical data schema

Document, supplier, sample, batch or material identifiers
Required analysis or inspection fields
Expected units and decimal precision
Permitted formats, ranges or specification checks where formally approved
Output templates and record-count expectations
Fields or decisions that always require authorised confirmation

Apply layered validation after extraction

Extracted valueFormat checkUnit / range checkCross-field checkPass / exception

Required-field and format checks
Unit and permitted-value checks
Cross-field consistency checks
Cross-output record parity
Duplicate, version and filename controls
Confidence thresholds for human confirmation

A tested extraction benchmark

In a tested Micro AI workflow, one 40-page multi-record PDF generated 40 Excel files, 40 XML files, 40 CSV files and 40 JSON files: 160 structured files in total. The batch completed in under 10 minutes with 99.5% extraction accuracy under the tested document conditions.

40 pages40 validated records4 formats each160 files

40 pages × 4 formats = 160 files
100 pages × 4 formats = 400 files
500 pages × 4 formats = 2,000 files
1,000 pages × 4 formats = 4,000 files

Use human review where technical uncertainty matters

Unclear scans, unexpected units, out-of-range values, missing identifiers and high-impact exceptions should be routed to the appointed reviewer with the source location, proposed value and failed rule.

Automation should make the exception easier to investigate. It should not bypass mandatory technical, safety, quality or compliance authority.

ExceptionShow source + failed ruleReviewerConfirm / correctRelease or hold

Build an audit trail around the business event

Original source file and processing reference
Workflow version and processing time
Extracted fields and validation results
Reviewer, decision and final value
Generated output versions and destinations
Completion or exception notifications

What a strong pilot should prove

Field-level performance across representative document variation
Correct handling of decimals, units, dates and identifiers
Clear routing for poor-quality and exceptional cases
Parity across required output formats
A recoverable record of every processing run
Measured staff handling time and exception data for scale planning