Uruguay-based quality assurance solutions provider Abstracta said it has formed a strategic alliance with PractiTest, a US-based provider of end-to-end QA orchestration solutions, aimed at advancing software quality assurance through smarter tooling, regional expertise, and deeper community engagement, particularly across Abstracta’s home market of Latin America.
The partnership combines PractiTest’s QA management platform with Abstracta’s testing and performance engineering expertise throughout the region.
Abstracta co-founder Federico Toledo confirmed his firm will serve as PractiTest’s regional partner for the Latin American market, offering full implementation services, consulting, and bilingual support.
“This is more than a technology partnership, it’s a mindset match,” said Yaniv Iny, CEO of PractiTest. “We’re both focused on helping QA teams lead, not just execute.”
Toledo echoed the synergy, stating: “PractiTest stood out—not just for its capabilities, but for its alignment with how we view testing.”
The companies will also collaborate on webinars, community events, and thought leadership initiatives to strengthen QA leadership across the Americas.
The partnership announcement comes only shortly after PractiTest launched its new SmartFox AI offering, an intelligent assistant designed to support software testing teams beyond automation.
Unlike many AI tools that focus solely on scripting or test execution, Iny said SmartFox aims to address what PractiTest describes as the “real bottlenecks” in QA: test design, duplication, and strategic execution.
“Most AI in QA has focused on test automation, but that’s only part of the challenge,” Iny explained. “SmartFox brings intelligence to the areas that truly move the needle: test design, duplication prevention, and execution strategy,” he claimed. “It was built to help teams test smarter, and make QA a strategic driver of product quality.”
SmartFox prioritises test execution based on actual value, prevents duplicate tests and issues in real-time, and helps QA teams maintain leaner, more efficient test libraries.
The assistant is embedded into the PractiTest platform and designed to deliver contextual, real-time support throughout the software testing lifecycle.
AI push
Just like most of its rivals, PractiTest is increasingly turning to AI to enhance its software testing products, with a particular focus on shift-left testing, which involves moving testing activities earlier in the development lifecycle to identify and address issues sooner, reducing the likelihood of defects and enhancing overall system reliability.
With the arrival of AI, shift left testing can be lifted to the next level, according to Iny’s colleague, PractiTest co-founder & Chief Solution Architect Joel Montvelisky.
Shift left testing offers several benefits, such as early identification of issues. By conducting comprehensive system and functional testing, potential issues and discrepancies can be identified and addressed proactively, minimizing the risk of disruptions during production rollout.
He argues that “AI can make a real difference” in the years to come. “AI has the ability to help teams tackle this challenge head-on, transforming how we define user stories and acceptance criteria, which later on directly impacts software testing and the way we define our test cases.”
Montvelisky, a well-known face within North America’s quality assurance community, stressed that the industry is already seeing AI’s influence as more companies harness its potential by integrating AI capabilities into their software solutions and teams are leveraging it in some parts of their testing processes.
“This is the beginning of a significant transformation, where AI is not just a helpful tool but a driving force to deliver better software faster and with greater confidence.”
– Joel Montvelisky
Montvelisky does not see AI as a replacement for human expertise but as a powerful tool to augment it. “One way AI can be leveraged is by identifying gaps in existing requirements and suggesting improvements to make them more comprehensive,” he explained.
For example, AI can analyse the requirements provided and flag areas where critical details might be missing.
On the other hand, AI can also generate initial drafts of user stories or acceptance criteria, providing a strong starting point for product managers, Montvelisky added.
“This ability to refine or initiate user stories ensures that teams have a more solid foundation to build on, saving time and reducing ambiguity.”
Moreover, Montvelisky stressed that AI can also analyse “a complete set of stories” already defined and developed as part of the product and point toward areas where similar functionality is already in place
“This will help product managers ensure this new functionality fits these existing similar areas in the product,” he said.
Later on, Montvelisky is convinced AI can complement this process by analysing test cases, bug reports or system usage data to recommend scenarios that might otherwise go unnoticed.
For instance, AI can flag areas where similar features have failed in the past or highlight edge cases that have historically caused issues in production.
“These insights can help teams anticipate problems and create user stories that are not only complete but also proactive in addressing potential risks,” he noted.
Of course, Montvelisky is the first to acknowledge that AI “is not perfect” as he pointed out that the output often requires refinement, and human judgment remains critical.
“AI also lacks the human factors of ingenuity and our ability to think outside the box. But when used effectively, AI can act as a second pair of eyes, helping to ensure that no detail is overlooked,” he shared.
Enhancing QA
Montvelisky continued by stressing that “the job of QA teams is not to catch every single defect but to catch the critical ones that harm user expectations from our software.”
He believes what needs to be done is to test smartly and design test cases using critical thinking that will cover the user stories from multiple possible scenarios and edge cases.
“It’s our job to anticipate how users will interact with the product, identify where it might break and ensure it performs as intended under all conditions,” Montvelisky said.
“AI is already starting to change how we approach test management. While still in its early stages, AI-powered test management tools can suggest tests based on user stories, generate test data such as steps and even analyse historical data to assess the value our test cases provide.”
For instance, Montvelisky stressed that AI can help teams determine the value of each test by analysing patterns over time—highlighting tests that catch the most critical issues or identifying redundant ones that may no longer add value.
“Of course, it still requires human intervention to refine test cases and validate generated steps,” he noted.
“To put it into perspective, AI doesn’t replace the expertise of testers but rather complements it, acting as a force multiplier that allows testers to focus on tasks their human judgment is needed like test strategy and exploratory testing,” Montvelisky concluded.
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