Automatic identification of User story anomalies using AI techniques to cut down feedback cycle time

Auto-generation of Unit test cases coupled with impact analyzer reports to ensure high levels of code quality

Flexibility to automate End-End user journeys across heterogeneous technologies using scripted and scriptless protocols

Self Service synthetic data provisioning using Bots and domain-centric pre-fabricated data packs to minimize dependency on centralized teams

Massive parallel & distributed test executions leveraging docker containers to accelerate test velocity by 1.5x

AI-enabled self-healing engine with abilities to perform impact analysis and carry out automatic fixes to reduce maintenance efforts

AI-based application monitoring, usage pattern, and feedback analysis to improve quality & availability of applications

Centralized dashboard encompassing quality footprint at every step to monitor and identify risk and take proactive recourses