Pcloudy

Scaling testing shouldn’t mean scaling infrastructure costs at the same pace. As teams grow, so do device requirements, parallel test runs, release cycles, and infrastructure demands. The challenge is finding a way to increase coverage without constantly adding more hardware, maintenance, and overhead. That’s where Pcloudy changes the equation. With access to thousands of real devices through the cloud, teams can scale testing capacity based on what they actually need - whether that’s a few devices today or thousands of test runs during a release sprint. The goal isn’t simply to spend less on testing. It’s to get more coverage, more flexibility, and more scale without letting infrastructure costs grow at the same rate. For enterprise QA teams, that means scaling testing capacity without scaling the operational burden that comes with it. #SoftwareTesting #MobileAppTesting #QualityEngineering #TestAutomation #DevOps #Pcloudy

AI adoption in regulated enterprises isn’t slowing down because teams don’t see the potential. It’s slowing down because the stakes are higher.   For regulated organizations, AI adoption has to work within strict boundaries around data privacy, compliance, data sovereignty, IP protection, auditability, accuracy, and control. Sensitive enterprise data cannot simply flow into external models, and AI decisions often need to remain traceable and governed.      There’s also the practical side. AI has to fit into the tools, workflows, and security systems enterprises already use. It can’t become another disconnected layer that creates more risk than value.       So the real challenge isn’t whether enterprises should adopt AI.   It’s how to adopt AI without giving up control.       That’s why local AI deployment is becoming increasingly relevant for regulated environments - bringing intelligence closer to where enterprise data already lives, while maintaining the level of control these teams need.   #EnterpriseAI #AIGovernance #DataPrivacy #Compliance #SoftwareTesting #QualityEngineering #LocalAI #Pcloudy

The #AIQualityInfrastructureBenchmark2026 Report revealed something big.   QA teams are highly confident. But they're not running nearly enough tests.   93% of teams say they're confident in their testing.   Yet only 14% are able to run 90% or more of their tests.   And 45% run 70% or less.   That's a pretty big gap.   So what does “confidence” actually mean when a significant part of the regression suite isn't being executed?   Maybe the critical tests are passing.   Maybe the suite is mature.   Maybe production has been stable.   But here's the question worth asking:   Which tests aren't running, and why?   Because the real measure of testing confidence isn't just the tests you have.   It's the testing you actually execute, observe and understand.   Confidence is easy to claim. Evidence is harder to ignore.   The Benchmark Report digs deeper into what this means for the future of Quality Engineering.   link in comments.   #AIQuality #QualityEngineering #SoftwareTesting #TestAutomation #AIinQA

The biggest barrier to enterprise AI might not be AI.   It’s where the AI runs.   For regulated enterprises, sensitive data, strict security policies, and compliance requirements can make external AI services difficult to adopt.   But what if AI could run within your own environment?   Local LLM deployment brings AI closer to your applications and data, while giving teams greater control over their security boundaries.   So what does this look like in practice?   What If Your AI Never Had to Leave Your Environment?   Join our upcoming webinar to explore local LLM deployment, enterprise security considerations, and how Qpilot brings AI powered testing into your own infrastructure.   Because enterprise AI shouldn’t have to mean choosing between intelligence and control. Register now - https://lnkd.in/gvH4BFHV

AI is changing what it means to create a test.   For years, test creation has required a combination of product knowledge, testing expertise and automation skills.   As applications become more complex, that combination is becoming harder to scale.   Now, AI is changing the equation.   The question is no longer:   “How quickly can we write automation scripts?”   It is becoming:   “How much of test creation can we simply describe?”   That shift matters.   Because when AI can understand intent and turn it into executable tests, QA teams can spend less time building the mechanics of a test and more time thinking about what actually needs to be tested.   And the opportunity goes beyond generating test cases.   AI can help create workflows, adapt them as requirements change, bring API and backend actions into the flow, and make test creation accessible beyond traditional scripting.   The goal isn’t to replace the tester.   It is to remove the friction between an idea for a test and actually executing it.   That is where AI powered testing starts becoming more than automation.   It becomes a new way of creating tests.   Try Qpilot Today - https://lnkd.in/fkk7kUJ

The #AIQualityInfrastructureBenchmark2026 report uncovered something worth talking about   AI can help you create more tests.   But more tests don’t automatically mean better testing.   That’s where the real conversation begins.   As AI makes test creation faster, the challenge is shifting.   Can teams execute those tests at scale? Can they analyze failures faster? Can they maintain what AI creates? And most importantly, can they turn all that additional testing into better release confidence?   The next phase of AI in QA isn’t about generating more and more tests.   It’s about making the entire quality process more scalable, intelligent, and actionable.   Our Benchmark Report looks at what QA teams are actually doing with AI today, where the gaps remain, and what the data tells us about where quality engineering is heading.   The hype is everywhere.   The data tells a more interesting story.   Link in Comments

Test case creation has become one of those QA tasks we accept as “just part of the job.”   Read the requirement. Understand the flow. Break it into scenarios. Write the steps. Add expected results. Repeat.   The problem isn’t that test cases are important.   It’s that creating them manually can consume time that could be spent actually thinking about quality.   This is where AI can change the equation.   The goal isn’t to replace the tester.   It’s to take the repetitive work out of test creation, while keeping the human in control of what gets tested and why.   With QGen, requirements, designs, screenshots and workflows can become structured, execution ready test cases in minutes.   Because the future of QA isn’t about writing more test cases.   It’s about spending more time asking better questions about the product.   And that’s where AI can complement the tester.   Try QGen Today - https://lnkd.in/fkk7kUJ

We’ve spent years making test infrastructure faster.   More devices. Better environments. Faster execution. Smarter CI/CD.   And yet, QA teams still spend a huge amount of time on the work that happens around testing.   Because infrastructure can make a test easier to run.   It doesn’t automatically make the test easier to create.   That’s where AI is changing the equation.   QPilot helps turn natural language into automation scripts and execute them on real devices.   QGen helps turn requirements, designs and workflows into execution ready test cases.   Together, they tackle a part of QA that infrastructure alone cannot solve:   The time it takes to go from what should we test to let’s run it.   The goal isn’t to replace QA.   It’s to take repetitive work off their plate and give them more time for the work that needs human thinking.   Because the next evolution of test infrastructure isn’t just about running more tests.   It’s about creating them faster too.

What happens when you take a team out of the office?   You get a different kind of connection.   Dancing on a cruise. Chasing sunsets on the beach. Games on the bus. Conversations in the pool. And laughter that had nothing to do with work.   Our Goa offsite was a reminder that great teams aren’t built only through projects, meetings, and deadlines.   They’re built through shared experiences.   Through the conversations you don’t schedule. The inside jokes you don’t plan. The moments where people see each other beyond their roles.   We went to Goa to unwind.   We came back knowing our teammates a little better, laughing a little more, and carrying a whole lot of memories with us.   Because sometimes, the best investment in a team is simply giving them time to be together.   And Goa did that pretty well. 🌴   #TeamCulture #TeamBuilding #Leadership #WorkplaceCulture #GoaOffsite

The #AIQualityInfrastructureBenchmark2026 revealed a crucial detail:   Test creation is becoming AI assisted.   But it is not autonomous yet.   Across QA teams globally:   59% are taking a hybrid approach, combining AI tools with existing automation frameworks like Playwright and Selenium.   10% are using dedicated commercial AI test generation tools.   14% are still creating automation scripts manually.   The direction is clear. AI is becoming part of the test creation process.   But the pace of change tells a different story.   Most teams aren't replacing their existing automation frameworks with AI. They're layering AI into the workflows, tools, and engineering practices they already trust.   And that makes sense.   AI can accelerate test creation. It can help generate scenarios, write scripts, and reduce repetitive effort.   But moving from AI assisted testing to truly autonomous test creation requires more than generating a script. It requires context, reliability, validation, execution, and trust.   The shift is happening.   Just not at the rate the AI hype might suggest.   We've explored this and much more in the AI Quality Infrastructure Benchmark Report 2026, bringing together insights from QA and engineering leaders across industries and geographies.   Download the report to get the full story on how AI is actually transforming QA.   Link in Comments