Autonomous Validation for Production Apps

Would you like to know in minutes if a stack update changed the functionality or performance of a production application that has no test support?
Read on…

Ops teams have an obligation to keep all applications in production running. Functional, performance and security in place. However, they also have an obligation to update layers in the stack for updates and security patches. A large enterprise today may be responsible for thousands of applications which run their business. However, over time there may be no dev or QA team assigned to be sure they are working correctly after stack updates. Even the ops team themselves won’t know if they continue to function and perform unless a user calls them and reports a problem after a stack upgrade.
AIQ for OPS closes this gap by applying AI to autonomously learn how an application works today in production and compares that to results every time any change occurs. This differs from synthetic APM, where engineers write specific use cases which run periodically. With AIQ for OPS, no test cases need be written by humans.
The AI system writes them itself and maintains a database of use cases, without human involvement, to test and compare runs immediately flagging changes for web and native mobile applications.

ENTERPRISE CHALLENGE:

  • 100’s of applications not regularly maintained or tested. No automated tests exist.
  • We must update stack components to latest versions for security and compatibility
  • Applications break on updates and we have to revert

SOLUTION:

  1. Use AIQ’s AI based autonomous test creation to auto-generate 100’s of scripts with validations against current stack – no QA or dev required.
  2. Re-run those same scripts with any stack updates automatically.
  3. AIQ will flag any differences in application actions or outcomes in minutes.

Learn more by requesting a demo at www.appvance.ai/get-demo

Recent Blog Posts

Read Other Recent Articles

For decades, software testing has been built on a simple idea: humans write tests, machines run them. That model has persisted since the first commercial recorders appeared in the mid-1990s. Testers would record a flow, edit a script, maintain it as the application evolved, and repeat the cycle endlessly. Tools improved incrementally, but the basic

For decades, software quality assurance has been a human‑driven task. Teams write test cases, automate scripts, execute manually or with tools, and then maintain those tests across releases. This work is detail‑oriented, repetitive, and long resisted full automation. In the United States alone, there are roughly 205,000 software QA analysts and testers, according to the Bureau

MIT just issued a wake-up call: despite $30–40 billion poured into generative AI, 95% of corporate AI pilots are failing to deliver financial returns. Enterprises are stuck in proof-of-concept purgatory while startups are racing ahead, scaling AI-native businesses from day one. Peter Diamandis put it bluntly: bureaucracy is the trap. Large organizations are trying to

Empower Your Team. Unleash More Potential. See What AIQ Can Do For Your Business

footer cta image
footer cta image