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
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.
No Regression Validation
Each release went out without a reliable way to validate existing functionality, which increased the risk of regression defects.
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.
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.
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.
Two Tracks of Change in Testing
Indium worked on two tracks at the same time, one to build a strong regression base, and the other to improve how testing happened within sprints. This helped fix both the backlog from the past and the delays in ongoing releases.
Track 1 (Built a Regression Suite)
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Reconstructed requirements from existing inputs
Used source code and workflow videos to rebuild BRDs and user stories, replacing manual analysis effort.
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Generated regression test cases
Created test cases from reconstructed stories using agentic AI, with high coverage and realistic business scenarios.
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Validated and refined continuously
Human-in-the-loop ensured accuracy, traceability, and no duplication, with iterative improvements.
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Moved to automation-first execution
Converted test cases into automation scripts and integrated them with CI/CD for continuous regression runs.
Track 2 (AI-Driven In-Sprint Testing)
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Captured structured inputs early
Used business recordings, meeting notes, discussions, Figma designs, and documented requirements.
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Converted inputs into user stories
Agentic AI transformed inputs into structured user stories to support testing within the sprint.
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Enabled early test creation
Generated in-sprint functional test cases to validate features sooner.
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Automated execution within pipeline
Converted test cases into scripts and integrated them into CI/CD for ongoing execution.
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Improved defect handling
Agentic AI enabled automated root cause analysis (RCA), self-healing, and structured bug reporting.
Test Lifter’s 8-Step Testing Approach
Numbers Highlighting the Impact
Test Lifter brought structure into testing and reduced dependence on manual effort.
Faster Releases
Test design and execution moved faster. Release cycles shortened, and new changes reached production without waiting for extended testing windows.
Traceability
Requirements and the testing scope were clearly documented. Every change had clear visibility, with minimal knowledge/context loss.
Lower QE Costs
Manual effort and maintenance overhead dropped. Testing ran with fewer resources, and cost did not scale with release volume.
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.
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 Consistent Testing Process
About Indium
Indium is an Al-driven digital engineering company that helps enterprises build, scale, and innovate with cutting-edge technology. We specialize in custom solutions, ensuring every engagement is tailored to business needs with a relentless customer-first approach. Our expertise spans Generative Al, Product Engineering, Intelligent Automation, Data & Al, Quality Engineering, and Gaming, delivering high-impact solutions that drive real business impact.
With 5,000+ associates globally, we partner with Fortune 500, Global 2000, and leading technology firms across Financial Services, Healthcare, Manufacturing, Retail, and Technology-driving impact in North America, India, the UK, Singapore, Australia, and Japan to keep businesses ahead in an Al-first world.
