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AI for tech logs

Custom models that code entries and flag repeat defects.

Tech logs are written fast, in shorthand, by people who know the aircraft. A general model misreads them; a model fine-tuned on your own history learns your abbreviations and your fleet. Because the records are sensitive and the language is specialised, the custom route on your own hardware is usually right.

// what matters here

What is different in this industry.

01

Learns your shorthand

Fine-tuning on your own entries teaches the model the abbreviations, part names and habits of your pilots and engineers.

02

An engineer decides

The model codes and links; a reliability engineer confirms or corrects, and nothing changes the aircraft's records without that review.

03

Measured on entries your engineers coded

Each release is scored on a held-out set your engineers coded by hand, and it is used only if it does at least as well as the last.

04

Your records stay in your data centre

Training and inference run on a GPU server you own, so maintenance records never leave your network.

// example projects

Priced examples for this service.

Each one is hypothetical, labelled as such, and priced live from our rate card. Open any of them in the estimator and make it yours.

Example projectWeb appAICustom LLM

Custom model for tech log defects

Hypothetical. Not a client, not a result.

// The problem

An airline's reliability engineers read a steady stream of free-text tech log entries to code them by ATA chapter and spot recurring faults, and the backlog never clears.

// What we would build

A custom language model fine-tuned on your own tech log history that codes each entry by ATA chapter, links it to earlier entries on the same aircraft and system, and flags possible repeat defects for an engineer to review. A custom model fits because tech log shorthand is full of abbreviations a general model misreads; it runs on a GPU server in your own data centre, so the records stay with you.

// What is in it

  • Your tech log history cleaned and used to fine-tune the model
  • Each new entry coded by ATA chapter, with the model's confidence
  • Possible repeat defects linked across entries for the same aircraft and system
  • Engineers confirm or correct, and corrections join the next training round
  • Measured against a held-out set coded by your engineers before each release
  • Runs on a GPU server in your own data centre

// Stack

  • Fine-tuned open-weight LLM
  • PyTorch
  • vLLM
  • Python
  • Next.js
  • Lenovo GPU server on site

// estimate

Build
≈ US$171,000 to US$261,000, delivered within 33 weeksCAD 243,900 to 371,100
Hosting
≈ US$176 a monthCAD 250 a month
Support
≈ US$2,500 a monthCAD 3,565 a month
Hardware
≈ US$47,800 to US$197,000CAD 68,000 to 280,800

Prices in your currency are estimates from today's Bank of Canada rate. All invoicing is in CAD or USD.

AI route
Custom LLM
Model running cost
≈ US$176 a monthCAD 250 a month

Timeline by milestone

Discovery
2.9 to 3.4 weeks
Data and evaluation plan
0.4 weeks
Design approved
2.4 to 4.4 weeks
Core features
1.9 to 3.4 weeks
Full build
1.4 to 2.4 weeks
Training
7.4 to 12.4 weeks
Testing and fixes
0.4 weeks
Evaluation and red-teaming
3.9 to 4.4 weeks
Launch
0.9 to 1.4 weeks
Production
0.9 to 1.4 weeks

Outside our hands, and added to the calendar

Hardware delivery, after it is ordered
2 to 6 weeks
Test access to your maintenance system
1 to 4 weeks

How it is paid

Deposit 20%
CAD 48,540 to 73,980
Discovery 1.5%
CAD 3,640.50 to 5,548.50
Data and evaluation plan 7.9%
CAD 19,173.30 to 29,222.10
Design approved 1.5%
CAD 3,640.50 to 5,548.50
Core features 4.6%
CAD 11,164.20 to 17,015.40
Full build 3.1%
CAD 7,523.70 to 11,466.90
Training 23.9%
CAD 58,005.30 to 88,406.10
Testing and fixes 1.5%
CAD 3,640.50 to 5,548.50
Evaluation and red-teaming 15.9%
CAD 38,589.30 to 58,814.10
Launch 1.5%
CAD 3,640.50 to 5,548.50
Production 8.6%
CAD 20,872.20 to 31,811.40
Holdback, 30 days after launch (10%)
CAD 24,270 to 36,990
GPU time for training and testing, at cost
CAD 1,200. Billed up front, at cost, outside the milestones
GPU server for AI, for the model
CAD 68,000 to 280,800. Billed up front, at cost, outside the milestones

// questions

Questions we get about this.

How much tech log history does a custom model need?

More is better, but a few years of entries from one fleet type is a reasonable start. We review your data before quoting and tell you what to expect.

Does the model make airworthiness decisions?

No. It codes and links entries for reliability work. Airworthiness decisions stay with your certified staff and your approved processes.

Could a frontier model read the tech log instead?

For a trial on non-sensitive data, yes, and it is quicker to start. For production on your own records, a fine-tuned model on your hardware reads the shorthand better and keeps the data at home.

// next

Not quite your project?

Tell us what you have in mind. We will come back to you with a range and the questions that would narrow it. Or book a call and talk it through.

Custom AI models for tech log coding and repeat defects | Atheron Network Labs