Extraction QA
Specialist QA for AI-extracted aircraft records data
This page defines how AI-extracted aircraft records data should be verified before teams rely on it. EE checks extracted fields against source images or files, confidence scores, reviewer decisions, audit trails, and downstream use. Specialists review high-impact and low-confidence items. The output is a verification standard, exception queue, and QA report that separates usable structured data from fields that still require human review.
When this review is needed
- a records decision is approaching gives the team a fixed window for evidence review.
- A status list or package summary needs source-page testing.
- High-risk records cannot be left to sampling.
- A decision register is needed for commercial, maintenance, or certification use.
The problem
Extraction feels complete when the spreadsheet is populated, but populated fields are not the same as verified records data. OCR can miss a digit, split a serial, infer a date, or attach a field to the wrong aircraft or component.
What gets reviewed
- Map extracted fields linked to source-page images using the source file and note the evidence path.
- Check confidence-flagged low-quality reads using the source file and note the evidence path.
- Tie independent re-checks of dates using the source file and note the evidence path.
- Separate part numbers using the source file and note the evidence path.
- Record signatures 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
- Accept extracted fields linked to source-page images only when a readable source page supports it.
- Reject index-only support where no underlying document can be opened.
- Hold AI classifications that lack reviewer disposition.
- Escalate extraction verification exceptions that affect pricing, acceptance, release, or certification path.
Evidence normally required
- Extracted fields linked to source-page images
- Confidence-flagged low-quality reads
- Independent re-checks of dates
- Part numbers
- Signatures
Common discrepancies
- Transposed serial numbers.
- Wrong revision of a task card treated as current.
- Plausible-looking status line with no accomplishment evidence behind it.
What is at stake
Unverified extracted data can contaminate maintenance tracking, due lists, LLP status, lease return evidence, and transaction diligence. Once loaded, bad data becomes harder to distinguish from trusted data.
How the work runs
Define critical fields
Identify which extracted fields affect status, trace, due dates, acceptance, or search only.
Compare to source
Check extracted values against source images, files, and surrounding record context.
Review exceptions
Prioritize low-confidence, high-impact, ambiguous, and conflicting values.
Set use status
Deliver verified fields, review queues, rejected values, and audit-trail requirements.
What the buyer receives
- extraction verification discrepancy register
- source-linked evidence map
- risk-ranked closure plan
- missing-record request list
Who uses the output
- technical records manager use the register to decide which exceptions affect the event.
- asset manager use the evidence map to request or close source records.
- Asset managers 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 extracted records data is loaded into systems, used for diligence, or handed to operations. It does not replace the source record. It defines the evidence and review controls needed before extracted fields are treated as usable data.
Start with a single asset
Confirm the status list matches the underlying evidence.
Regulatory limits
EE does not make airworthiness determinations, approve maintenance, replace CAMO or quality responsibilities, or guarantee authority or buyer acceptance. The review identifies records completeness, consistency, and traceability issues.
What this review does not cover
- Physical aircraft inspection
- Issuing maintenance release statements
- Negotiating purchase or lease terms
- Repairing missing source records without owner instruction
Specific to this review
- Every extracted field needs a source location and confidence or review status.
- High-impact fields require stronger QA than low-risk indexing fields.
- AI extraction errors are handled as exception queues, not silent corrections.
- Specialists decide whether the field can be used for the intended workflow.
- The audit trail records source, extraction, reviewer decision, and final value.
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
When can extracted data be trusted?
When the field has source trace, review status, and a defined use case. A high confidence score alone is not enough for decision-critical data.
Does this keep the original records?
Yes. The extracted data points back to source records; it does not replace them.
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.