Digitization QA
Full-coverage scan and index acceptance review
This review tests whether a digitization vendor's output is accurate enough to accept and use. EE checks scan quality, OCR extraction, document classification, metadata, asset attribution, duplicate handling, and source-file traceability. AI helps sample broadly and find inconsistencies; records specialists review the exceptions. The output is an acceptance register showing what passes, what needs rework, and what should not be loaded into operating systems yet.
When this review is needed
- The file arrives with a deadline tied to Digitization project handover.
- Several record classes need to be checked together.
- Source documents are present but the index is not trusted.
- The team needs findings ranked by decision impact.
The problem
Digitization projects often measure completion by file count. That misses the real question: whether the scanned and indexed records can be searched, trusted, and traced back to the original image without losing aircraft, component, serial, date, or document context.
What gets reviewed
- Read page-level completeness using the source file and note the evidence path.
- Compare physical inventory using the source file and note the evidence path.
- Locate legibility scoring using the source file and note the evidence path.
- Challenge index metadata accuracy checked using the source file and note the evidence path.
- Summarize AI assistance using the source file and note the evidence path.
Scope this review
Tell us the asset, the event, and the evidence in scope, and we will outline a focused first engagement.
Send a representative, redacted record set and we will scope the review.
What gets validated
- A source link must exist for page-level completeness; absence creates a finding.
- Reviewer notes must explain why an AI flag was closed.
- Conflicting dates, serials, or references stay open until the source hierarchy is clear.
- The digitization output verification scope must include the records that drive the current decision.
Evidence normally required
- Page-level completeness
- Physical inventory
- Legibility scoring
- Index metadata accuracy checked
- AI assistance
Common discrepancies
- Originals warehoused or destroyed after accepting incomplete scans.
- Index errors that make retrieval fail at the next transaction when nobody can find the page that exists.
- The issue appears only after the acceptance point.
What is at stake
Accepting bad digitization output pushes errors into maintenance tracking, records searches, lease returns, and transactions. Fixing the data after it is loaded is slower than rejecting or correcting it at acceptance.
How the work runs
Set acceptance criteria
Define scan quality, indexing, metadata, OCR, and traceability requirements for the project.
Test the output
Compare digitized records with source scans, file names, metadata, and expected document classes.
Review quality failures
Classify OCR errors, misclassification, duplicate handling, missing pages, and attribution gaps.
Prepare acceptance decision
Deliver the acceptance register, rework list, and load-readiness recommendation.
What the buyer receives
- scan acceptance discrepancy register
- source-linked evidence map
- risk-ranked closure plan
- missing-record request list
Who uses the output
- Project leads use the register to decide which exceptions affect the handover.
- records manager use the evidence map to request or close source records.
- operators leaders use the summary to brief the next approval, release, or deal meeting.
How the work fits into the transaction or program
This belongs before vendor acceptance, system loading, or archive handover. The review gives records leadership a source-based quality position for the digitized set. It does not replace the original records. It decides whether the digital output is ready to be used as an index and working copy.
Start with a single asset
Reconcile maintenance tracking against source records.
Regulatory limits
The output is not a maintenance release, conformity statement, or regulatory approval. Responsible operators, owners, CAMOs, designees, and authorities keep those decisions.
What this review does not cover
- Regulatory approval activity
- Vendor procurement
- Legal drafting
- Physical conformity inspection
Specific to this review
- File count is not a quality measure unless classification, metadata, and traceability are also tested.
- OCR confidence is treated as a review queue, not as proof of correctness.
- Each sampled output must trace back to the original scan or source file.
- AI helps detect duplicate, misclassified, and low-confidence records across large archives.
- The deliverable separates vendor rework from internal data-governance decisions.
Sources
Federal Aviation Administration. FAA acceptance criteria for electronic recordkeeping systems and electronic signatures.
Federal Aviation Administration. FAA guidance on making and keeping maintenance records and acceptable recordkeeping practices.
Frequently asked questions
Does this replace manual quality control?
No. AI helps find problems at scale. Records specialists still review exceptions and acceptance samples.
What should not be accepted?
Output that cannot trace back to source scans, misclassifies records, loses asset attribution, or has OCR errors in decision-critical fields.
Relevant glossary terms
Related pages
Where this fits
Talk to an engineer who has done this work
We will walk through your current state, the records or evidence involved, and a scoped first engagement.
Talk through the aircraft, records, evidence, deadline, and next useful step.