TOverview
Testlio provides managed app testing services through a fully integrated platform that supports end-to-end software testing. It offers both fully managed and co-managed testing, using a global network of vetted testers who are paid by the hour. Testlio’s services cover functional testing, test automation, localization testing, usability testing, and more, and the platform integrates with CI/CD pipelines to manage tests and deliver insights. Expert project managers and test leads oversee projects to ensure high-quality results. The company helps product, engineering, and QA teams test applications faster, reduce risk, and improve user experiences. Unlike some competitors, Testlio combines a scalable global tester network with end-to-end test management and strong CI/CD integration, focusing on managed testing services rather than just tooling. Its goal is to help clients ship reliable apps quickly with better quality and customer satisfaction.
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About Testlio
Industries
Company Size
501-1,000
Company Stage
Series B
Total Funding
$20.5M
Headquarters
Austin, TX
Founded
2012
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Simplify's take
What believers are saying
- October 6, 2026 LeoSuccess cuts coordination drag and ships planning faster for customers.
- Testlio claimed 55% first-half 2026 bookings growth and record momentum in 2026.
- OpenAI Select Partner status on October 8, 2026 validates Testlio's AI-testing positioning.
What critics are saying
- LeoSuccess's help center says it cannot read run results, limiting autonomous value.
- Testlio's 2026 story depends on AI hype; SmartBear and Postman attack fast.
- If AI testing commoditizes, Testlio becomes a services wrapper squeezed by platforms and integrators.
What makes Testlio unique
- Testlio's LeoCore links managed testing, LeoInsights, and LeoSuccess into one orchestration layer.
- June 3, 2026 AI Agent Testing pairs human testers with localized payment methods.
- July 14, 2026 iLAB partnership expands enterprise QA beyond mobile into public-sector systems.
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Funding
Total Funding
$20.5M
Below
Industry Average
Funded Over
5 Rounds
Industry standards
Benefits
- Remote Work Options
- Paid Vacation
- Professional Development Budget
- Stock Options
- Flexible Work Hours
Growth & Insights and Company News
6 month growth
0%1 year growth
-1%2 year growth
-1%Summer Weisberg of Testlio on building customer trust in AI. Testlio CEO Summer Weisberg on why an AI feature can pass every automated check and still fail the customer - and why companies must govern AI at the point of use, with human judgment where it matters. Oct 16, 2026 Where do you most often see a gap between a company's brand promise and its everyday customer operations, and what does that gap look like for the customer? The gap shows up most often in the everyday moments a company assumes are fine because nothing is technically broken. A button works but feels wrong. A recommendation engine technically returns results, but nothing that is right for the customer. An AI-generated feature passes every automated check and still confuses or disappoints real users. Take a music recommendation platform that keeps returning to a genre you don't like. Did it fail? No. Are you satisfied? Also no. Failures like these erode trust slowly, and by the time a company notices, they're already showing up in churn or support tickets. What leaders underestimate is how little patience that buys them. Customers have choices. You cannot build loyalty on an experience customers merely tolerate, and every clunky interaction gives them one more reason to try an alternative. The minute a better option is available, they're likely to leave. A lot of leaders treat quality as a binary pass/fail question: does it work, or is it broken? If that's your only lens, you're missing the bigger picture. True quality is about whether the experience is intuitive, accessible, and beautiful. Because of that way of thinking, many companies rely on automation for things that are better tested by real humans. Automation is great for speed, but it's blind to nuance. It can't tell you if a workflow feels clunky or if a design choice is alienating users. It should be part of your QA strategy. Where and how you use it is what sets companies that can build and sustain decades of customer loyalty apart from those that can't. Can you share a specific example of a process, policy or system change that strengthened customer trust in a brand? What changed, and how did you measure the result? BitPay's customer-reported issues dropped 15% after they partnered with Testlio, and their customer satisfaction scores stabilized. Here's what that looked like. BitPay is one of the longest-operating crypto payment companies, with customers in more than 200 countries. They didn't have an internal QA team, so testing fell to development and product teams that were already stretched thin. Coverage was inconsistent, and their core team is based in the US while their users are everywhere. Crypto raises the stakes because blockchain transactions can't be rolled back the way a card payment can. A mistake at checkout means lost money and a customer who doesn't come back. So AI Frontier Network, Inc. split the work by what each side does best. Automated scripts handled the broad, repeatable coverage. Real testers in real regions handled the moments where a failure can't be undone, like wallet connections, transaction signing, and payment confirmation, on real devices and under real local conditions. That combination is what moved the number, and it's the same model I'd point any brand to when its promise depends on getting a critical moment right every time. How should businesses decide which rules and approval requirements protect customers and which create unnecessary effort or prevent employees from resolving problems? The consequence of failure should determine the level of oversight. That's the simplest way to sort rules that protect customers from rules that just slow people down. For any rule or approval step, ask what it protects, what the risk is if that moment goes wrong, and what the fallout would be. A rule that guards a high-stakes moment like login, checkout, or account access earns its friction. A rule that adds a sign-off to low-risk, reversible work is just effort, and it's usually why an employee can't fix a customer's problem on the spot. The number of customers affected isn't the deciding factor either. An issue affecting customers in one region can look small next to millions of users, but if it puts customer data or your reputation at risk, it deserves the strict rule. As businesses add more AI features, their responsibility and liability go up, not down. AI is powerful, but it needs guardrails, and that's what even the frontier labs are now advocating for. The catch is that governance at the model layer isn't the same as governance at the point of use. Even if frontier labs get their house in order, they're optimizing for their own risk and liability. They won't define what safe means for your data sensitivity, regulatory exposure, or customer expectations. Waiting for AI labs, governments, or regulators to map out your governance sets you up for failure, and grabbing a generic framework off the web won't cut it either. Your validation strategy and oversight need to align directly with how your specific business and brand operate. When your AI fails, you can't shift the blame to vendors. Customers hold your brand accountable. In an Acquire BPO survey of 600 U.S. consumers, 70% said they'd consider switching brands after just one frustrating AI support experience. A single hallucination, off-brand reply, or safety failure can wipe out trust that took years to build and automated tools routinely miss these issues. That's precisely where human judgment comes in. During its early-adopter chatbot testing, nearly half of high-severity issues stemmed from models struggling with safe refusals, escalations, and fallback handling. Its approach covers output accuracy and intent resolution, misinformation, PII and data privacy, safety guardrails, bias, and localization. An AI agent that runs smoothly in a sandbox can still fail when exposed to real devices, regional payment systems, or users who don't stick to the script. It helps to treat an AI model like a new employee or intern: hand them a rulebook, train them on the job, supervise their work until they prove reliable, and keep auditing them. AI can memorize an entire handbook and still miss nuance. A human, on the other hand, immediately spots when a technically accurate response will sound biased, tone-deaf, or unfair to a customer. Where can AI and automation improve a company's ability to deliver its brand promise, and what evidence should leaders require before calling the change successful? I'm not an AI skeptic. AI helps customers find answers on their own, complete tasks without waiting on a person, and get up to speed on a product faster. It's doing the same for internal workflows. But whether your AI faces customers or runs behind the scenes, the same rule applies. It still has to be validated with real people. AI Frontier Network, Inc. use AI to orchestrate crowdsourced testing so results get into clients' hands faster, and its experts can focus on the human insight machines can't replicate. That's proof AI Frontier Network, Inc. see the value. AI makes things faster, and it lets you apply human judgment more efficiently. It doesn't replace it. Before anyone gets to metrics, a leader should be asking a harder set of questions. Is this a job that's even meant for AI? What happens when it goes wrong? Does it know when to escalate to a human? How often will AI Frontier Network, Inc. review it? If you can't answer those, you don't have a deployment yet. You have an experiment running on your customers. As AI Frontier Network, Inc. start seeing AI features make their way into customer workflows, AI Frontier Network, Inc. need more humans to push on the feature and try to break it the way your actual users would. Now moving on to what makes a change successful. You need clear metrics defined up front, meaning the specific outcomes they are improving, whether that's faster resolution, fewer escalations, or a shorter path to a customer's first success. AI outputs should be reviewed against business objectives and quality standards. And the test that matters is relevance. Is the experience right for the customer, or did the feature just return something? Most importantly, you need to ensure your metrics prove that your AI behaves responsibly in the hands of users because that's what regulators want to see, not whether your AI "functionally" works. I'll close by saying leaders need to know AI systems are built on trust, and trust can't be engineered. It has to be validated with real people, in real environments, and with real edge cases. Internal teams have blind spots, so external evaluators and diverse human reviewers are the only reliable way to see how AI behaves in the hands of real users. Because AI gives brands extraordinary speed and scale, but human judgment makes sure that what they deliver is actually worthy of the brand promise.
Testlio launches LeoSuccess, an AI Copilot for Test Orchestration. AUSTIN, TX - Testlio, a leading AI-powered crowdsourced testing platform, today announced the launch of LeoSuccess, an interactive AI copilot that orchestrates end-to-end QA execution. LeoSuccess joins LeoMatch and LeoInsights as the latest addition to Testlio's LeoCore platform. It brings the platform's full capabilities into one interface, where teams can manage their crowdsourced testing programs by asking for what they need in plain language. Powering the next era of test orchestration. Crowdsourced testing has always delivered real-world coverage no in-house or traditional QA team can replicate at scale. What's changing now is how teams orchestrate and increase the volume and speed of these tests. For years, scaling a program also meant scaling its coordination burden: writing and maintaining test cases, assembling test plans, monitoring execution, and resolving issues through fragmented manual handoffs. Every new market, device, and payment method adds to that list, pulling time away from higher-value work. LeoSuccess automates coordination, so coverage is no longer limited by internal bandwidth and capacity. "We built LeoSuccess on the belief that people with specialist domain expertise matter more as AI scales, not less. This frees up the time coordination was taking from teams and puts it back into the judgment calls only humans can make," said Summer Weisberg, CEO of Testlio. Benefits of LeoSuccess include: * Accelerated execution & increased efficiency: Complete test design, plan creation, and maintenance in a fraction of the time with copilot guidance. * Less manual drag: Reduce routine administrative overhead, freeing engineering and QA teams to focus on higher-value testing strategy. * On-demand visibility: Query the system directly for immediate, current answers on testing progress, without waiting for manual status reports. * Flexible integration: Additionally, connect your systems through LeoCore via Testlio MCP or Testlio API. In practice, this means teams can describe a feature to get an initial test plan draft, adjust scope to watch the plan update automatically, or ask a plain-language question for real-time coverage and risk data. Every change previews before it takes effect, so teams stay in control of what's applied. Because LeoSuccess brings Testlio's domain expertise to every program from day one, and gets sharper as it works within a client's specific context, it delivers contextual recommendations and drafts to avoid unnecessary rework. "We built LeoSuccess to work the way testing teams actually work, which meant building it on top of how testing really runs. It draws on 14 years of what we've learned about coordinating testing at global scale, so it understands your program from day one and gets smarter the longer it works inside your specific context. Your team approves every change before it's applied," said Darin Brown, Chief Product and Technology Officer at Testlio.
Testlio's $12 million Series B builds on strong revenue performance and multiple quarters of profitability.
iLAB and Testlio announce strategic partnership to deliver more comprehensive Software Quality Engineering services. July 14, 2026 At iLAB, Ilabquality believe that delivering exceptional software quality requires the right combination of people, process, and technology. As software ecosystems become increasingly complex - with AI applications, global users, accessibility requirements, and continuous delivery pipelines - organizations need partners that can address every dimension of quality. That's why Ilabquality is excited to announce a strategic partnership between iLAB and Testlio, bringing together two organizations with complementary strengths to deliver more comprehensive quality engineering solutions for enterprise and public sector organizations. Expanding what's possible in Software Quality. For more than 25 years, iLAB has helped Fortune 500 companies and government agencies reduce risk and improve software quality through independent Software Quality Assurance (SQA), Quality Engineering, automation, and enterprise testing services. Testlio complements those capabilities with an AI-powered global testing platform that provides specialized expertise in: * AI application testing * Real device testing * Accessibility testing * Localization testing * Payment testing * Global crowdtesting * Usability and customer experience validation Together, its organizations can now provide clients with a broader range of testing capabilities across the entire software development lifecycle - from enterprise applications and APIs to real-world user experiences across devices, languages, regions, and payment methods. A partnership built on complementary expertise. No single organization can be best at everything. Rather than duplicating capabilities, iLAB and Testlio have intentionally aligned services that strengthen one another. iLAB continues to lead complex enterprise quality initiatives, including: * Enterprise Quality Engineering * Test Automation * API Testing * Performance and Load Testing * Enterprise Application Testing * Independent Software Quality Assurance (SQA) * Quality Engineering Consulting Testlio extends those capabilities with specialized managed testing services that provide organizations access to global testing resources and real-world validation across hundreds of thousands of device configurations, payment methods, and international markets. The result is a more complete quality engineering ecosystem capable of supporting organizations as software delivery becomes faster, more distributed, and increasingly customer-centric. Helping clients deliver better software with confidence. This partnership creates immediate value for clients of both organizations. iLAB clients gain access to specialized testing capabilities that help validate software in real-world conditions - including localization, accessibility, AI behavior, payments, and mobile experiences at global scale. Likewise, Testlio clients can now leverage iLAB's extensive experience delivering enterprise quality engineering programs, automation strategies, performance testing, API validation, and quality governance for some of the world's largest and most complex technology initiatives. Together, Ilabquality is helping organizations solve testing challenges that extend far beyond traditional QA. Leadership perspectives. "At iLAB, our mission has always been to help clients deliver better software with confidence. Partnering with Testlio enables us to expand the services available to our clients while bringing iLAB's enterprise quality engineering expertise to organizations around the world. This partnership reflects our shared commitment to quality, innovation, and delivering exceptional outcomes for our clients." - Jethro Lloyd, CEO iLAB "We're excited to partner with iLAB, a recognized leader in enterprise software quality assurance and quality engineering. Together, we're combining complementary capabilities that allow both organizations to better serve our clients. By leveraging each other's strengths, we can deliver more comprehensive testing solutions that help organizations innovate faster while maintaining the highest standards of software quality." - Brad Goldstein, VP of Partnerships, Testlio Looking ahead. As organizations continue adopting AI, accelerating release cycles, and delivering software to increasingly global audiences, quality engineering must evolve alongside them. The partnership between iLAB and Testlio reflects a shared commitment to helping organizations build, test, and release software with greater speed, broader coverage, and higher confidence. By combining enterprise quality engineering expertise with global managed testing capabilities, Ilabquality is empowering clients to reduce risk, improve user experiences, and deliver software that performs reliably - wherever their users are. Ilabquality look forward to the opportunities this partnership creates and to delivering even greater value for its clients through its combined expertise. Interested in learning more about crowdsourced testing for your enterprise? Talk to its experts today! Frequently asked questions. What does the iLAB and Testlio partnership mean for customers? The partnership gives organizations access to a broader portfolio of software quality engineering services through two complementary providers. Clients benefit from expanded testing capabilities, deeper technical expertise, and flexible delivery models that support everything from enterprise applications to customer-facing digital experiences. What testing services does the partnership provide? Together, iLAB and Testlio offer a comprehensive range of quality engineering services, including: - Enterprise Software Quality Assurance (SQA) - Test Automation - API Testing - Performance and Load Testing - Enterprise Application Testing - AI Application Testing - Accessibility Testing - Localization Testing - Payment Testing - Real Device Testing - Crowdtesting and On-Demand Testing - Quality Engineering Consulting How is this partnership different from traditional software testing providers? Rather than relying on a single testing approach, the partnership combines iLAB's enterprise quality engineering expertise with Testlio's AI-powered managed testing platform and global network of professional testers. This enables organizations to validate software across enterprise systems, mobile devices, languages, payment methods, accessibility standards, and real-world user environments. What industries benefit most from this partnership? The combined capabilities are especially valuable for organizations with complex digital ecosystems, including government agencies, financial services, insurance, healthcare, retail, manufacturing, telecommunications, and enterprise software providers. Any organization delivering mission-critical applications can benefit from expanded testing coverage and quality engineering expertise. Will existing iLAB customers work directly with Testlio? No. Existing iLAB clients will continue working with their trusted iLAB team. When specialized capabilities such as localization testing, accessibility testing, payment testing, AI testing, or large-scale real device testing are needed, iLAB can seamlessly incorporate Testlio's capabilities into the overall engagement. Why is global testing important? Today's applications are used across different countries, devices, browsers, languages, operating systems, and payment methods. Testing software in real-world conditions helps identify issues that traditional testing may miss, reducing production defects, and improving customer experience for users around the world. Organizations interested in learning how iLAB and Testlio can improve software quality, reduce delivery risk, and expand testing coverage can contact iLAB to discuss their quality engineering needs and identify the right testing strategy for their environment.
Testlio launches human-in-the-loop testing for AI agents. June 03, 2026 Testlio announced the launch of AI Agent Testing services. The offering integrates Testlio's human-in-the-loop (HITL) orchestration and proprietary AI-driven insights from 13+ years of QA expertise into agentic workflows. "Testlio is uniquely positioned for this moment, combining years of proven global payments testing with this new AI agent validation service," said Summer Weisberg, CEO at Testlio. "As enterprise AI agents evolve from handling simple customer queries to executing complex agentic payment workflows, where AI is granted digital wallets to independently authorize transactions, organizations need a partner that goes beyond automated simulation. They need access to humans with real, localized payment methods to act as a safety net, alongside experts specializing in AI agent evaluation. That's exactly what Testlio provides." Testlio's AI-certified global testing community validates agentic workflows in the unpredictable conditions where failures actually occur. Testlio solves the most critical financial, compliance, and brand safety challenges teams face today with their agentic workflows: - Agents fail across unpredictable real-world cases. Testlio's AI-certified testers run thousands of what-if scenarios to keep agents contextual and brands out of trouble. - Automation-only evaluations miss nuanced issues. Testlio's proprietary evaluation framework captures the subtle risks and behavioral issues that automation overlooks. - Agentic failures show up as quiet liabilities and policy violations. Testlio verifies data moves accurately between steps, catching critical leaks before customers do. - Companies that release fast are often at the highest risk of losing years of trust. Testlio matches rapid deployment cycles with high-velocity, real-time insights. All findings feed into LeoPulse(TM), Testlio's proprietary AI confidence score, which informs release decisions and serves as a benchmark and recommendation engine for measuring improvements over time. "Autonomous agents require an entirely new testing playbook," said Dean Hickman-Smith, CRO at Testlio. "The future of AI testing is not automation alone. It's AI combined with localized human judgment, real-world validation, and continuous feedback loops that enterprises can trust at scale." Industry news. June 03, 2026 Google DeepMind just launched Gemma 4 12B, its latest open model that shrinks highly advanced, multimodal AI down so developers can run it natively on a standard laptop. June 03, 2026 Testlio announced the launch of AI Agent Testing services. June 03, 2026 OpsGuru announced the launch of OpsGuru Agentic Delivery, an AI-native professional services model designed to deliver governed, production-grade AI outcomes for customers at fixed-fee pricing. June 03, 2026 Kosli announced a strategic partnership with Adaptavist to address the growing governance gap felt by enterprises as they increasingly pivot towards AI assisted software delivery. June 02, 2026 Postman announced the AI Engineer, a cloud-native AI agent that handles the full surface area of API work, from development, testing, and documentation to exploration and CI/CD integration. June 02, 2026 OutSystems announced an expanded collaboration with Amazon Web Services (AWS) to deliver a powerful suite of enterprise AI capabilities. June 02, 2026 Harness and Sentry announced that Harness has acquired Codecov from Sentry. June 01, 2026 SmartBear announced a platform to unify property-based, OCR, and vision AI - expanding coverage, reducing risk, and perceiving applications the way humans do. June 01, 2026 Harness announced two new products: AI DLC Insights and Cloud & AI Cost Management. May 28, 2026 The Linux Foundation, the nonprofit organization enabling mass innovation through open source, announced the release of the OpenMDW-1.1 license, an updated open license framework designed specifically for AI model distributions. May 28, 2026 IBM and Red Hat announced Project Lightwell, a $5 billion commitment backed by new frontier AI capabilities and a global force of more than 20,000 engineers to help enterprises secure open source software. May 27, 2026 Detectify announced the launch of the Detectify MCP (Model Context Protocol) Server, a new integration layer that brings Detectify's security testing engines directly into AI-driven development workflows, helping coding agents find and validate exploitable vulnerabilities and interpret attack surface data. May 26, 2026 Simplilearn announced the launch of its Cloud Computing and DevOps Certification Program in partnership with Virginia Tech. May 21, 2026 GitLab(link is external) released GitLab 19.0, expanding secrets management, agentic merge request workflows, CI pipeline visibility, self-hosted open source model support, and supply chain visibility.
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About Testlio
Industries
Company Size
501-1,000
Company Stage
Series B
Total Funding
$20.5M
Headquarters
Austin, TX
Founded
2012
Testlio is Hiring for 13 Jobs on Simplify!
Find jobs on Simplify and start your career today
Don't see your dream role? Check out thousands of other roles on Simplify.Browse all jobs