From Manual QA Review to AI-Assisted Assurance
How an airline quality engineering team replaced slow, inconsistent contract review with automated, high-accuracy test data extraction
Overview
An airline’s quality assurance team was responsible for validating test data drawn from a portfolio of more than 600 contracts. The work was essential, but the method had not kept pace with the demand placed on it.
Reviewing contracts manually was slow and error-prone, and quality outcomes varied from project to project depending on who carried out the review and how. Inconsistency in the process introduced inconsistency in the assurance it was meant to provide.
The team needed a way to make QA validation faster and, more importantly, repeatable.
Precision developed an AI-powered data extraction solution to automate QA validation and accelerate accuracy.
The Challenge
A QA process that could not scale with the portfolio
The quality engineering function faced several interconnected challenges:
- Test data had to be extracted from more than 600 contracts.
- Manual review was slow and consumed significant QA capacity.
- Extraction was error-prone, weakening confidence in validation.
- Approaches differed across projects, producing inconsistent results.
- QA cycle times constrained wider delivery timelines.
- Governance and data protection requirements had to be maintained throughout.
The challenge was to industrialize quality assurance itself, not simply accelerate it.
The Objective
Make quality assurance faster, consistent and repeatable
The objective was to embed automation into the QA process so that validation became both quicker and more dependable.
The program focused on:
- Automating test data extraction from contracts
- Raising extraction accuracy to a validated benchmark
- Standardizing the QA approach across projects
- Shortening QA cycle times
- Designing reusable workflows for future expansion
- Maintaining QA governance and data protection controls
The Solution
Bring AI into the quality engineering workflow
Precision integrated quality engineering practice with Amazon Textract AI to extract and validate contract data automatically, reaching 98% accuracy. Rather than sitting alongside QA, automation was placed inside the validation workflow itself.
Roll out in two phases, learn as you scale
Delivery followed an agile, two-phase rollout. The first phase focused on the most critical contracts, allowing accuracy to be evidenced and the approach to be refined before scaling to the full set.
Design for reuse across projects
Configurable models and reusable workflows were built into the architecture, giving the QA function a standard method that can be applied consistently across projects and extended to new datasets as needs evolve.
Keep governance intact while accelerating
Compliance controls for QA governance and data protection were embedded within the automation framework, ensuring that faster validation did not come at the cost of control.
The Impact
98% extraction accuracy and 10x faster QA cycles
AI-assisted validation delivered 98% data extraction accuracy and QA cycles 10x faster than manual review.
The client also achieved:
- 90% reduction in manual effort:
QA capacity was redirected from extraction to genuine quality analysis. - Greater consistency:
A standardized approach removed project-to-project variation in validation. - Higher confidence in results:
Validated accuracy strengthened trust in QA outputs. - A reusable QA asset:
Configurable models and workflows can be applied to future projects. - Governance maintained:
Embedded compliance controls preserved QA and data protection standards. - Predictable delivery:
100% agile project delivery kept the phased rollout on schedule.
Test Better, Fly Smarter
We connect where you are to where you want to go— bringing strategy, technology, people and capabilities together to make quality engineering faster, smarter and more reliable.