VeraxaAI is an AI-powered software development, automation, and quality engineering company founded on a single belief: if a system matters to your users, it must be validated before it reaches them.
We combine QA engineering, test automation, AI validation, and pragmatic software development into one delivery model — so teams can move from idea to launch without the usual handoff gaps, blind spots, or release-day surprises.
Our operating model
80% of our expertise focuses on QA, automation, and AI validation.
20% supports software development and product engineering.
We work best with teams that need clear proof before they ship.
AI outputs drift silently
LLM responses change across model updates, prompt variations, and context windows — without automated validation, teams find out in production.
Automation coverage has gaps
Many teams have some automation but not the right automation. Flaky suites, missing API coverage, and zero mobile testing leave real risk on the table.
Release confidence is guesswork
Without structured evidence — test reports, validation logs, coverage metrics — "is it ready to ship?" becomes a gut call instead of a data-driven decision.
Teams building AI-powered products face new categories of risk that require new categories of validation. We built VeraxaAI to sit at that intersection.
AI moves fast — validation keeps it safe
Teams building on LLMs and AI agents need specialized testing that traditional QA cannot provide. We fill that gap.
Automation reduces risk at scale
Manual testing alone cannot keep pace with modern release cycles. We build automation frameworks that grow with your product.
Quality engineering creates competitive advantage
Teams that ship with evidence ship faster and more confidently than teams that rely on hope. We help you become one of them.
At VeraxaAI, every quality engineer thinks like a software engineer. We don't just run test cases — we design test systems, build automation frameworks, and architect validation pipelines that scale with your product.
This engineering-first approach means our work integrates cleanly with your CI/CD pipeline, your development workflow, and your product roadmap — not as an external gate, but as a core part of how you ship.
What this looks like in practice
Test plans written alongside feature specs, not after them
Automation frameworks designed for maintainability and CI integration
AI validation pipelines built with the same rigor as production code
Evidence-based release reports that stakeholders can act on
AI & LLM Testing
Hallucination, prompt injection, agent behavior, workflow validation
Test Automation
Playwright, Selenium, API, mobile, CI/CD integration
Manual QA
Exploratory, functional, UAT, regression, accessibility
Performance Testing
Load, stress, scalability, and bottleneck analysis
Our mission
To make reliable, validated software the default — not the exception.
We exist to give every product team — from early-stage startups to growing enterprises — access to the quality engineering expertise that was previously only available to the largest companies.
Our vision
A world where AI systems are trustworthy by design, not by luck.
As AI becomes central to more products and workflows, we want to be the team that ensures those systems are safe, honest, and genuinely reliable — so users and companies can trust what they build.
Quality First
Testing and validation are not afterthoughts — they are built into every engagement from day one.
Engineering Excellence
We hold our work to a high standard, using modern tooling, clean architecture, and sound engineering practices.
Transparency
Clear reporting, honest timelines, and structured evidence at every milestone — no guesswork, no surprises.
Innovation
We stay at the leading edge of AI, automation, and quality engineering to give clients a real competitive advantage.
AI-Focused Thinking
We approach every engagement with an AI-first mindset — identifying where intelligent automation can reduce risk and accelerate delivery.
Integrated Engineering Approach
Development, QA, and AI validation work together in one delivery model — no handoff gaps, no silos.
A small team of specialists across AI, QA, delivery, and infrastructure. Each profile below shows what they own, how long they have been doing it, and where they add the most value.
7.5 yrs experience
7.5 yrs · Quality strategy, leadership
Leads delivery and sets the quality bar across discovery, validation, and release planning. Focused on helping teams define what “ready” really means before work moves into production.
Technologies
Focused on release confidence, edge cases, and repeatable evidence.
6 yrs experience
6 yrs · Agentic AI eval, automation
Connects product goals with the testing layer, turning vague AI behaviour into measurable checks. Spends most of his time on eval design, automation, and keeping teams aligned on outcomes.
Technologies
Works on systems that need validation, observability, and clear handoffs.
6 yrs experience
6 yrs · Automation, mobile, IoT
Works across product delivery and multi-device testing, especially when the workflow extends beyond web apps. Brings structure to delivery and keeps implementation grounded in what customers actually use.
Technologies
Bridges product, delivery, and implementation details so work stays practical.
4 yrs experience
4 yrs · Regression, functional
Specialises in the repeatable QA work that keeps releases honest. Builds confidence through structured regression coverage, functional checks, and clear bug isolation.
Technologies
Focused on release confidence, edge cases, and repeatable evidence.
3 yrs experience
3 yrs · Test execution, coordination
Keeps test execution moving with strong coordination and a sharp eye for the details that get missed in handoffs. Helps convert test plans into actual evidence during release cycles.
Technologies
Focused on release confidence, edge cases, and repeatable evidence.
2 yrs experience
2 yrs · AI systems, cloud, RAG
PortfolioBuilds the systems behind the product layer, from AI workflows to the infrastructure that keeps them observable and maintainable. Works closest to the engineering surface where validation meets deployment.
Technologies
Works on systems that need validation, observability, and clear handoffs.
2 yrs experience
2 yrs · Next.js, Node, infra
Focuses on the application layer and supporting infrastructure, with a bias toward pragmatic implementation. Helps move ideas from demoable concepts into stable product surfaces.
Technologies
Bridges product, delivery, and implementation details so work stays practical.
Work with us
Whether you need AI validation, automation engineering, or a full QA partnership — let's talk about what your product needs.