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 process had limited continuity between stages. Teams often had to rebuild context when moving from one stage to the next, which created gaps between inputs and outputs.
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
Test Lifter brought the client’s existing testing artifacts into one structured workflow. AI-driven test design then turned them into production-ready test assets, with human validation built into the process.
Deployed within the Ecosystem
The solution ran 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
Test data, code, and IP remained within the client’s environment. Test cases, scripts, and reports were managed internally with no external data movement.
Closed-Loop Lifecycle
Testing followed a defined flow from discovery through execution and optimization. Each stage passed its outputs directly to the next.
Self-Learning QE System
The system learned from past test runs and defects to refine future test cases and execution.
Two Tracks of Change in Testing
Indium built a regression foundation for the existing backlog and reshaped testing within sprints to address release delays.
Track 1: Built a Regression Suite
Existing requirements were reconstructed from source code and workflow videos.
Agentic AI turned those requirements into high-coverage regression test cases, with human validation to refine them.
The validated cases were then converted into automation scripts and connected to CI/CD for continuous regression testing.
Track 2 : In-Sprint Testing
Business inputs were captured early and converted into structured user stories with agentic AI.
Functional test cases were created within the sprint and integrated into CI/CD for ongoing execution.
Agentic AI also supported root cause analysis, self-healing, and structured bug reporting to speed up defect handling.
Test Lifter’s 8-Step Testing Approach
Numbers Highlighting the Impact
Test Lifter brought structure into testing and reduced dependence on manual effort.
2-3X Faster Releases
Shorter release cycles helped new changes reach production sooner.
95% + Traceability
Clear links between requirements and test coverage reduced knowledge/context loss.
30-40% Lower QE Costs
Less manual work and maintenance overhead brought down testing costs.
85%+ Automation Coverage
Most testing moved to automation, supporting continuous execution without adding team capacity.
70% Less Test Design Effort
Reusable test assets reduced test creation effort and freed teams to focus on critical scenarios.
Test Lifter Built a Consistent Testing Process
Repeated manual test creation was off the table, with less technical debt to carry forward. Test Lifter addressed the gaps at the source. The process preserved test assets for reuse and gave teams a consistent testing workflow. 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.