Why Most AI Proof of Concepts Fail and How to Fix Them
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Our customer had multiple applications (approximately 25) to support their business. A Capacity Performance Validation (CPV) had to be performed for all applications before they were moved to production. Various bottlenecks that impeded the performance of the applications at different layers which include interfaces like Web, App, DB and Network etc. Absence of separate CPV environments as well as sharing of servers, especially for e-Business and OCS applications resulted in an overlap during test executions.
Moreover, the response times for eBusiness and OPUS applications for transactions were high. Quinnox Testing team was involved in the entire end-to-end CPV process. The team worked closely with Business and Development teams to identify all business critical scenarios for various applications that required CPV testing.
We identified bottlenecks in different applications and resolved it with the help of Development team, before the changes were moved to production. The network latency was identified and the bandwidth was increased accordingly. To improve the performance of the application, the indexing of the search functionality was rectified. Quinnox executed different types of performance test such as Smoke Testing, Load Testing and Stress Testing with no disruption to business.
Learn why AI PoCs stall and how you can scale AI to enterprise impact, and explore practical steps with.
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