AI-First In-Sprint Testing for a High-Velocity POS Platform

Client

A fast-scaling cloud based POS platform operating in a high-change release environment

Challenge

Frequent UI and functional changes were outpacing the existing testing process
Interdependencies across multiple pods caused repeated script breakage and delayed regression readiness
Manual testing and automation needed to run in parallel without slowing sprint delivery
Maintaining complete sprint coverage while sustaining parallel delivery velocity

Solution

Implemented an AI-first in-sprint testing model, aligning automation directly to story-point releases
Used product design artifacts and feature specifications to generate test scenarios early
Synchronized automation updates with every feature deployment, enabling continuous validation as stories moved into QA
Executed a full regression suite on staging, preserving release confidence
Technology – React JS, Forge, FastAPI, LangGraph, Celery Worker, Neo4J, Blob Storage, Azure AI Foundry, Claude

Impact

From 20% to less than 2% Reduction in Defect Leakage
Delivered 100% sprint coverage with AI-first automation aligned to story-level releases
Cut regression execution to 1 consolidated staging cycle per sprint
Enabled continuous defect detection across every in-sprint deployment
Sustained parallel pod delivery velocity without increasing manual QA overhead
The testing team reduced from 30 to 6 as AI absorbed routine test execution.

Strategic Significance

Showcases Indexnine’s ability to embed AI-first testing into sprint execution itself, enabling high-change products to sustain release velocity while continuously improving coverage depth and regression confidence

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