Qualiber builds software and tools that help teams assess the quality of applications and AI systems, understand the risks, and make informed decisions.

About Qualiber

Qualiber builds the instruments QA has been missing.

Those instruments exist to do one thing: build independent proof for AI-era software — turning “trust us” into evidence anyone can check for themselves. Qualgraph, Warrant and Reality Lab each address a part of quality that testing alone was never going to solve.

Our products

Instruments for quality that holds up.

Qualgraph

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Evidence-backed quality reviews.

Checks recorded application behavior against agreed rules and brings supported evidence from across your tools into one clear review.

Shows what was checked, what was found and where more evidence is needed
Answers what you have verified, where the gaps are and what to check next
Retains reviewed findings and quality history to compare results over time

Decisions AI assists with analysis and explanation. People remain responsible for decisions.

Warrant

Oversight for AI decisions.

For AI systems that make or recommend decisions: shows how reliable the checks are, and what the AI is authorized to do.

Measures how closely an AI checker agrees with your experts, and whether that changes when the model changes
Records the AI’s scope of permission, who approved it, when it expires and the supporting evidence
Gives auditors a record they can review independently

Ownership The organization runs Warrant and owns the results.

Reality Lab

Realistic, controlled testing.

Shows how software behaves in realistic situations, including when things go wrong, with evidence teams can inspect.

Runs defined scenarios and records what happened
Helps teams understand failures and check fixes
Provides a place to test Qualgraph, Warrant and the checks themselves

Today Limited to controlled development testing. Broader enterprise simulation and independent certification remain future capabilities.

Who it's for

Built for the people who own the quality bar.

QA & Quality Leaders

Prove the bar is real.

Move past "we tested it" to evidence you can defend — where decisions held, where they slipped, and what the signals were telling you all along.

Engineering Leaders

Ship fast without shipping blind.

Keep velocity while making every sign-off traceable. When something gets through, you can reconstruct exactly how — and close the gap.

Our approach

From did it pass to is it faithful.

Every Qualiber product is built on the same three ideas.

Traceable evidence

Findings link back to the rules and records that support them.

Accountable release checks

A check can stop a release only after meeting defined reliability requirements, receiving human approval and being explicitly enabled.

Reusable quality knowledge

Reviewed findings and corrections inform later reviews, so teams can build on what they have already learned.

Our team

Founded from first-hand experience.

Qualiber was founded from first-hand experience of the problem it is trying to solve. Built by someone who has lived that complexity as a practitioner and user, not just observed it from the outside.

Karthik S.

Founder and CEO

Karthik S. is the Founder and CEO of Qualiber, bringing nearly two decades of experience in quality engineering, technical program management, and software delivery. With a career spanning Amazon and Apple, he combines deep technical expertise with experience leading complex, cross-functional programs at scale.

Prior to founding Qualiber, Karthik led end-to-end quality and release strategy for Amazon’s reading platforms, serving more than 50 million users. During his four years at Amazon, he developed a GenAI-powered testing accelerator that reduced execution time by 50% and delivered $1.2 million in annual run-rate savings. He also took GenAI and agentic AI initiatives from pilot through production adoption and served as an Amazon Bar Raiser, contributing to hiring standards across the organisation.

Previously, Karthik directed quality engineering and testing for Apple News, where his work included improving publisher-payout data accuracy and developing machine-learning-driven approaches to detect anomalies across analytics systems.

Karthik’s experience across large-scale technology environments has reinforced a central principle behind his work: successful software delivery depends not only on technical capability, but also on effective coordination and evidence-based decision-making. He brings this perspective to Qualiber, where he is building an AI-powered decision platform designed to give engineering teams clearer, evidence-based insight into release readiness.

Karthik holds an Executive MBA from the University of Chicago Booth School of Business and a Bachelor of Engineering in Electrical, Electronics and Communications Engineering from Anna University Chennai.

PG

Co-Founder

PG is a senior software engineer with eight years of experience across cybersecurity, cloud infrastructure, embedded systems, and artificial intelligence. His career spans roles at Broadcom, and Visteon Corporation, where he has worked across software development, infrastructure, security, and production delivery.

Most recently, PG served as a Staff Software Engineer at Broadcom, where he was part of the core development team responsible for authentication and authorization, security updates, and cloud infrastructure. He helped establish CI/CD processes across multiple microservices and worked across both AWS and Google Cloud environments. During his time at VMware and later Broadcom, he was also responsible for introducing AI into the engineering process, exploring how emerging tools could support software development and delivery.

Previously, PG worked as a Software Engineer at Visteon Corporation, developing embedded software for automotive systems used by the Volkswagen Group. His work included real-time operating systems, memory management, low-level drivers, software testing, and release delivery. He also contributed to establishing a production process at one of the company’s facilities.

Across his career, PG has developed technical experience spanning low-level embedded systems, cloud-native infrastructure, cybersecurity, and AI. This breadth has given him a strong understanding of how software is built, secured, tested, and delivered in complex production environments, while continuing to explore how emerging technologies can strengthen the engineering process.

PG holds a Bachelor’s degree in Software Engineering and a Master’s degree in Artificial Intelligence, with a thesis focused on computer vision for mobile devices. He has also completed Stanford University’s XCS224N course in Natural Language Processing with Deep Learning.

Get in touch

See Qualgraph on your signals.

Tell us a little about your team and the quality problem you're wrestling with. We'll set up a walkthrough grounded in the kind of evidence you actually work with.

→ Live demo, tailored to your stack

→ No obligation, no slide deck

hello@qualiber.ai