How We Built an AI-Generated MEL That Got Transport Canada Approval
We called it Project Moonshot because, honestly, we weren't sure it would work.
The goal was ambitious: build an AI system that could generate a Minimum Equipment List (MEL)—one of the most complex regulatory documents in aviation—and have it approved by Transport Canada. Not "generate drafts for humans to fix." Actually approved. Ready to fly.
Transport Canada approved our AI-generated MEL after just two rounds of feedback.
Here's how we did it.
Why MELs Are a Nightmare
If you've worked in aviation operations, you know MELs. If you haven't, here's the short version: a Minimum Equipment List defines exactly what equipment can be inoperative on an aircraft while still maintaining airworthiness. Every item requires cross-referencing multiple regulatory sources:
- Manufacturer's Dispatch Deviation Guide (DDG/MMEL)
- Transport Canada's Master MEL
- Any temporary revisions
- Supplemental Type Certificates (STCs) for modified aircraft
One MEL entry might require checking four different documents. A complete MEL has hundreds of entries.
Here's the dirty secret: nobody actually writes MELs from scratch. Operators inherit them from previous certificate holders, acquire them from other operators flying the same type, or layer amendments onto documents that have been passed around for years. The result? Files that carry forward legacy issues—formatting problems, corrupted structures, inconsistencies that compound with every amendment cycle.
Traditionally, this means weeks of manual work by subject matter experts—and multiple rounds of regulatory feedback before approval. And when Word documents corrupt (which they do), you're rebuilding on a broken foundation.
We thought: what if we could teach AI to do the cross-referencing—and start clean?
The Hard Parts Nobody Warns You About
Extraction Was the First Wall
Manufacturer documents don't play nice with software. The DDG/MMEL format uses cascading tables—complex nested structures that broke every standard extraction tool we tried. Worse, many PDFs include scanned approval cover letters that block text conversion entirely.
Our solution: An AI-trained extraction engine built specifically for aviation technical documents. Item by item, parsing structures that made standard OCR tools give up.
Assembly Was the Second Wall
Once we had the data, we needed to build actual Word documents—because that's what regulators expect, and Word's track changes feature is critical for the review cycle between tech pubs and flight operations.
We built:
- Programmatic document assembly with intelligent aircraft configuration filtering
- A custom procedure database for operator-specific maintenance requirements
- A formatting engine that handles source document inconsistencies (yes, official manufacturer documents have typos—we had to teach the system to recognize "taht" as "that")
Then We Had to Prove It Worked
Here's where it got interesting.
We built a reconciliation pipeline to compare our AI-generated MELs against existing approved operator documents. The plan was to catch AI errors before submission.
What actually happened: the AI consistently found discrepancies that humans had missed. Items that should have been flagged. Cross-references that were slightly off.
We went from "let's double-check the AI's work" to "the AI is better at this than we are."
Thorough Human Review Before Submission
The AI generated complete Word documents—the format regulators expect, with track changes enabled for the review cycle. But we didn't just submit the output blindly.
Our subject matter experts reviewed every page. Line by line. Cross-reference by cross-reference. The AI did the heavy lifting; humans verified it met the standard. That combination—AI speed with human oversight—is what made Transport Canada comfortable approving it.
The Results
| Metric | Traditional Process | Project Moonshot |
|---|---|---|
| New aircraft MEL from scratch | Several months | Less than 1 day |
| Regulatory approval cycles | 3-5 rounds typical | 2 rounds |
| Cross-reference accuracy | Human error rate | Exceeds manual review |
AI-generated MEL submitted. Two feedback rounds. Transport Canada approved.
What This Actually Means
Speed is nice, but it's not the point.
The point is that when a manufacturer releases a service bulletin, we can update the MEL in hours instead of weeks. When an operator adds a new aircraft type, they're not waiting a month for documentation. When regulatory requirements change, compliance happens fast.
And the point is that AI didn't just match human performance—it exceeded it. The reconciliation process proved the system catches things humans miss.
This isn't theoretical anymore. Transport Canada signed off on it.
The Bigger Opportunity
Here's what we learned that goes beyond our own project.
The approval process itself is ripe for transformation. Regulatory bodies use sampling methodologies to review submissions—they can't check every line of a 400-item MEL. But AI could. An AI-assisted review process could validate submissions almost instantly, flagging genuine discrepancies while eliminating the multi-month cycles that currently delay operators.
We've started conversations with industry groups about this. The same tools that help operators create better submissions could help regulators review them faster. Both sides benefit. Operators get approvals in days instead of months. Regulators can focus their expertise on edge cases and judgment calls rather than cross-reference verification.
The technology exists. The question is adoption.
What's Next
Project Moonshot was our proof of concept. The same approach—AI extraction, intelligent assembly, automated validation—applies across aviation documentation:
- Operations manuals
- Maintenance procedures
- Training documentation
- Regulatory compliance packages
We're building toward a future where documentation isn't a bottleneck. Where regulatory updates take hours, not weeks. Where human expertise focuses on judgment calls, not cross-referencing tables.
The moonshot landed. Now we're scaling it.
Interested in AI-assisted aviation documentation? Contact us to learn more about how ForIT can modernize your technical publications process.