Large language models can answer questions from unstructured documents. But aviation records have unique challenges: handwritten entries, abbreviations, dates in margin — here's how we handle all of it.

How AI Actually Reads Your Logbooks

The average aircraft logbook contains decades of handwritten entries, cryptic shorthand, and annotations in the margins. Generic OCR solutions fall over on the first "c/w AD 2018-11-07" abbreviation. Here is how we built a pipeline that handles it.

Step 1: High-resolution capture

We either scan your physical books on-site or ingest PDFs you upload. Either way, every page is stored at a resolution high enough that individual ink strokes remain legible.

Step 2: Multi-engine OCR

We route each page through Google Document AI and AWS Textract simultaneously, then arbitrate disagreements with a purpose-built aviation vocabulary model. This catches the handwritten edge cases that a single OCR engine misses.

Step 3: Aviation-specific extraction

A fine-tuned LLM reads the OCR output with context about FAA record structure — tach times vs. Hobbs, hours since overhaul, AD references, signature blocks. It produces structured events, not just text.

Step 4: Citation-backed retrieval

When you ask a question, we retrieve the relevant extracted events and anchor every answer back to the original page and line. You can always verify the AI against the source.

What it can't do

AI cannot replace a signed logbook entry. It cannot make a maintenance decision for you. And it cannot read pages that are physically illegible — if the ink is too faded to see, we'll tell you, not guess.

How AI Actually Reads Your Logbooks — And What It Can and Can't Do