Use case · Technical documentation
Structured extraction from technical documentation.
A token factory that reads long, text-heavy engineering documentation and emits structured, traceable answers that downstream systems and human reviewers can both use.
The problem
What we are actually solving.
Aircraft technical documentation is the unglamorous core of aviation operations — maintenance manuals, wiring diagrams, component histories, airworthiness directives. Thousands of pages per aircraft type, revised continuously, cross-referenced to other documents and to regulatory frameworks.
The work an engineer does with these documents is mostly retrieval and reasoning: find the relevant sections, synthesise them against a specific situation, write down a defensible answer. Doing that by hand is expensive. Doing it with a vendor chatbot is unsafe: the answer has to be traceable to the paragraph it came from, and the architecture has to hold up to regulatory scrutiny.
The approach
The shape of the token factory for this use case.
The context engine ingests the canonical source — the aircraft documentation itself — with provenance attached to every chunk. Not a scraped PDF, but the structured source, re-ingested on each revision. Chunk boundaries follow the document’s own structure (section, subsection, figure caption) so retrieval returns self-contained units.
The prompt compiler maps engineering intents — “explain this finding,” “extract the part reference,” “trace this system” — to provider-native payloads. The router chooses a model per intent class, with fallbacks for long context and for structured-extraction accuracy. The eval harness replays a golden set of engineering questions against every candidate model before anything ships to users.
Model-agnostic was not a marketing choice here. Aviation is a multi-decade industry, and the model market changes faster than the documentation does. The architecture had to survive three or four model generations without a rewrite.
Outcomes
What changed.
Pages processed
Tens of thousands
Per aircraft type, per revision cycle
Extraction accuracy
High-nineties
On structured fields, against human review
Reviewer workload
Materially reduced
On routine extraction tasks
The headline numbers are real, but the more important shift was qualitative: engineers stopped treating retrieval as a tax and started using the factory as a thinking partner. The time freed from looking things up went into the parts of the job where judgement actually matters.
Regulatory posture improved in a quiet way. Every answer carried a citation trail back to the source document; “where did this come from” stopped being a hard question.
Honesty
This is a delivered engagement, not a pattern description. The token factory described here ran in production through multiple model generations. I can speak on record about the architecture and the outcomes.
Specific vendor names, internal tooling, and customer identifiers stay out of this page by default. Happy to walk through specifics under NDA in a scoping call.
Next step
Talk about structured extraction from technical documentation..
The usual first engagement is an architecture review. Say so on the form, or write directly.
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