Deployed Test Lifter to Drive Release Assurance and Traceability for a French Audit & Tax Firm
Client Overview
Writing test cases manually for every release cycle makes it difficult to keep up with timelines and keeps adding technical debt to test assets.
One of the largest financial audit & tax firms based in France, managing a wide portfolio of mission-critical applications, was dealing with this exact situation. The process was still evolving around structured requirements, regression checks, and in-sprint testing.
Disconnected Testing Process Across Stages
The testing lifecycle lacked continuity from one stage to the next. There were gaps between inputs and outputs, where teams often had to rebuild context before moving forward. This was evident across the testing process.
01
Lack of Structured Requirements
Business requirement documents (BRDs), user stories, or acceptance criteria were not available. Teams used videos, discussions, and scattered inputs to identify test scenarios.
02
No Regression Validation
Each release went out without a reliable way to validate existing functionality, which increased the risk of regression defects.
03
Manual & Reactive Testing
Test design, updates, and execution were manual, with limited automation. Most of the effort went into keeping up with releases rather than improving coverage.
04
Limited Quality Visibility
Defects were identified late, and there was little transparency into quality metrics. Teams couldn’t clearly track what improved or where issues repeated.
05
Coverage Gaps
Inconsistent coverage and unclear traceability made it difficult to track what was built, tested, and yet to be tested.
Indium Fixed the Process Behind Testing
Using Test Lifter, the client’s existing artifacts like specifications, user stories, design files, videos, logs and release notes were brought together and structured for testing.
The platform then applied AI-driven test design, along with iterative refinement and human validation, to create production-ready test assets.
Deployed within the Ecosystem
The solution was hosted in the client’s private cloud and integrated with development systems, test management tools, CI/CD pipelines, and automation frameworks across GTB (modernization, greenfield, brownfield) and RTB (maintenance).
Data Stayed Internal
All test data, code, and IP remained within the client’s environment. Test cases, automation scripts, and reports were fully owned and managed within their systems, with no external data movement.
Closed-Loop Lifecycle
Testing moved through a defined flow: Discover → Reconstruct → Generate → Validate → Automate → Execute → Optimize. Outputs from each stage flowed directly into the next.
Self-Learning QE System
The system improved over time based on past runs. Test results and defects helped refine future test cases and execution.
Numbers Highlighting the Impact
Test Lifter brought structure into testing and reduced dependence on manual effort.
2 –3x
Faster Releases
Test design and execution moved faster. Release cycles shortened, and new changes reached production without waiting for extended testing windows.
95 % +
Traceability
Requirements and the testing scope were clearly documented. Every change had clear visibility, with minimal knowledge/context loss.
30 –40%
Lower QE Costs
Manual effort and maintenance overhead dropped. Testing ran with fewer resources, and cost did not scale with release volume.
85 %+
Automation Coverage
Most of the testing moved to automation. As a result, execution ran continuously, and releases stayed on track without adding more people to the team.
70 %
Less Test Design Effort
Repeated effort in rebuilding test assets was reduced. Test creation became faster, and teams focused on validating critical scenarios.
Test Lifter Built a ConsistentTesting Process
Manual test creation for every release and growing technical debt were no longer part of the process. Test Lifter addressed the gaps at the source. It removed the need to recreate test assets every time and brought consistency across how testing was planned and executed. The client’s testing now runs steadily, and time goes into understanding critical business scenarios instead of rebuilding everything from scratch.
Claims dispatch activities moved through an automated workflow and reduced dependency on manual coordination and repetitive operational follow-ups.