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A US Life Sciences Company Cuts Testing Effort 50%

Quantum AI helped a global biotechnology company automate regulated testing and validate complex data without adding headcount.

A US Life Sciences Company Cuts Testing Effort 50%

Client

Challenge

The problem was not simply that testing was slow. It was that the same high-stakes work had to be repeated whenever a system changed.

A new feature, regulatory update or system upgrade triggered another round of manual verification. Testers had to confirm that existing functionality still worked across four different platforms. Because the work was repetitive but could not be rushed, it consumed capacity that could otherwise support new initiatives.

The data-validation challenge was similar. The company's ETL pipelines moved information across databases, files, tables and processing layers. Validation depended heavily on manual SQL execution, object-level checks and spreadsheet comparisons. As data volumes and the number of objects increased, this approach became harder to scale, particularly for datasets containing millions or billions of records.

The company therefore needed two things at once: automated regression testing that could preserve testing discipline, and a scalable way to reconcile source and target data with traceable results.

Solution

The company introduced BCT's Quantum AI, an AI-enabled test automation platform, beginning with AutoPod for autonomous UI and functional testing. Rather than replacing all four systems at once, the company used a phased approach: establish the manual baseline, automate one platform, measure the result, validate it, and then use the evidence to expand.

AutoPod was deployed across Archer, Oracle eBusiness Suite R12, RTP DeltaV and MARS. Each platform was measured against its traditional scripting effort, creating an auditable comparison between manual and automated work. The company could therefore scale the programme on evidence rather than on a one-time pilot estimate.

Quantum AI also provided a broader route for quality engineering. Its modules cover data validation, test-case generation, performance testing, security testing and accessibility testing. Two additional capabilities moved quality checks earlier in the lifecycle: Impact Analyzer maps requirement changes to affected test cases and coverage, while Test Case Generator creates ALM-ready functional test cases from design documents.

For data validation, the company also adopted Data Test Buddy, a Python-based framework built on Pandas. Instead of requiring testers to run individual SQL checks and compare spreadsheets, the framework uses a central configuration layer to define source and target objects, validation queries, comparison rules and expected results. It then connects to the relevant sources, runs validations, identifies discrepancies and produces consolidated reports.

The approach kept people in the process. BCT also worked with the company's Learning & Development team to train the delivery organization so automation would not depend on a small specialist group.

Impact

The four automated platforms each achieved close to a 50% reduction in scripting effort. Across the measured cycle, manual scripting effort fell from 248 hours to 123.2 hours, saving 124.8 hours, or 15.6 person-days.

The larger quality-engineering programme recorded an 87% reduction in regression-testing effort and a 76% reduction in health-check effort. Quantum AI also saved 50% of test-design effort and 75% of test-execution effort. These gains freed capacity without adding headcount.

The broader CRT Automation programme recorded approximately $70K in FY25 cost avoidance and a further $104K in cumulative Q1–Q2 FY26 cost avoidance. At the reported run rate, the full-year projection was more than $276K.

Data Test Buddy produced additional savings across simple, medium and complex validation workloads. From October 2025 through June 2026, it delivered $83K in cost savings versus ICEDQ.

The result was a testing model that could absorb more change without requiring the same manual effort every cycle. The company retained the control, traceability and human review expected in a regulated life sciences environment while moving repetitive validation work into reusable automation.

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Phone number

+91 44 43449000,
+91 44 61739000

Email address

talktous@bahwancybertek.com

Bahwan CyberTek is a global technology transformation partner, driving innovation and equipping companies for the future.

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