Axiontest delivers next-generation software testing — from Agentic AI validation to chaos engineering and enterprise security. Built for teams who can't afford to fail.
From intelligent agentic testing to chaos engineering — purpose-built for teams shipping complex AI-driven products.
Validate autonomous AI agents across goal-completion, planning accuracy, tool use, and safe failure modes in production-like environments.
Comprehensive QA for LLMs and foundation models — hallucination benchmarks, bias audits, prompt injection, and alignment checks.
Self-healing test suites powered by AI. Adapts to UI changes, generates test cases from requirements, and reports with context.
OWASP-based penetration testing, AI-specific threat modeling, vulnerability assessments, and compliance audits for cloud-native apps.
Simulate real-world load, inject failures, and verify system resilience. Find breaking points before your users ever encounter them.
Embed quality into every sprint with distributed tracing, continuous monitoring, and feedback loops that catch issues at the source.
We don't bolt AI on. Our entire methodology was designed around AI-powered systems, agentic workflows, and LLM pipelines.
Security and quality aren't final checkpoints. We integrate them into every stage of your SDLC from the very first sprint.
Dedicated QA leads, weekly reports, and transparent dashboards. You always know exactly where your quality stands.
At Axiontest, we bring quality architecture expertise across AI-native platforms, enterprise security, and intelligent automation — not test execution, but the engineering discipline that makes entire systems trustworthy.
At Axiontest, we architect quality systems that eliminate regression risk at the pipeline level. Where organisations once spent days on manual cycles, we design automation frameworks that deliver the same confidence in minutes — integrated into the release flow, not bolted on after it.
We work at the intersection of AI and quality engineering — designing intelligent automation systems that use Model Context Protocol and agent-driven frameworks to reason about application behaviour, not just react to it. Our automation thinks. It adapts. It does not break when your product evolves.
Our domain depth spans enterprise SaaS, zero-trust security platforms, industrial SCADA systems, and large-scale data intelligence engines. We understand the difference between software that must be reliable and software that must be provably secure. Both demand a fundamentally different quality architecture.
We are selectively onboarding founding clients — organisations where the quality of software is a strategic concern, not just a release checkpoint.
How we think about quality →
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Comprehensive quality assurance for autonomous AI agents — validating goal pursuit, multi-step reasoning, tool use, memory persistence, and safe failure handling in complex environments.
Agentic AI systems operate autonomously — they plan, reason, use tools, and take multi-step actions to accomplish goals. Unlike traditional software, they behave non-deterministically. Standard testing fails to capture emergent agent behaviors, coordination breakdowns, and misaligned goal pursuit.
Axiontest's Agentic AI Testing practice is purpose-built for this complexity. We simulate production-like environments with adversarial edge cases, evaluate agent decision trees under uncertainty, and validate system behavior at the boundary of acceptable actions.
Rigorous evaluation of large language models and foundation models — measuring accuracy, safety, fairness, robustness, and alignment before deployment.
Foundation models behave unpredictably at scale. Hallucinations, biased outputs, prompt injection vulnerabilities, and misaligned responses can cause significant business harm. Pre-deployment model evaluation is now a regulatory and competitive necessity.
Intelligent test automation that writes itself, heals itself, and reports with context. Scale coverage across web, mobile, and API layers — continuously.
Traditional automation breaks constantly. Every UI change triggers cascading failures. Teams spend more time maintaining tests than writing new coverage. Test debt accumulates. Confidence erodes. Axiontest's AI automation inverts this model entirely.
Proactive, adversarial security assessment for modern cloud-native applications and AI systems — finding what attackers will find before they find it.
Modern applications carry an expanded attack surface. APIs, cloud infrastructure, third-party integrations, and AI components introduce entirely new threat vectors including prompt injection, training data leakage, and model inversion attacks.
Simulate real-world load and inject controlled failures to verify your system's resilience, scalability, and recovery behavior — before users ever encounter them.
Distributed systems fail in unexpected ways. Cascade failures, network partitions, pod crashes, and database timeouts happen in production — often at the worst possible time. Chaos engineering flips the script: we deliberately break things in a controlled environment so you understand exactly how your system behaves under duress and can fix weak points before they become outages.
Embed quality from the very first line of code. Combine distributed tracing, continuous monitoring, and intelligent feedback loops to catch issues at the source — not in production.
Shift-left means moving testing as early as possible in the SDLC. Instead of catching bugs during a QA handoff, we embed quality practices — code review gates, unit test requirements, contract tests, linting, and static analysis — from the moment a ticket is picked up.
Combined with deep observability — distributed tracing, structured logging, metrics, and alerting — teams gain continuous signals on system health from development all the way through production.
All plans include onboarding, dedicated support, and full access to the Axiontest platform.
Yes. The Professional plan includes a 14-day free trial with full feature access. No credit card required.
Enterprise clients can fully customize their mix. Starter and Professional can add services at per-engagement rates.
Most clients are onboarded and running their first test suite within 5 business days of signing.
Perspectives from practitioners at the frontier of AI, testing, and software quality.
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Axiontest was founded because we kept seeing the same pattern across enterprise engineering organisations: genuinely complex software — agentic AI platforms, zero-trust security infrastructure, high-throughput data systems — being validated with quality methodologies designed for a far simpler era of software.
The consequence was always the same. Incidents that should have been caught. Releases that eroded trust. Quality teams stretched thin on repetitive execution rather than doing the strategic work that actually protects a system.
We built Axiontest to close that gap — bringing quality architecture expertise, AI-native automation engineering, and deep domain knowledge to organisations where software reliability is a competitive necessity, not a formality.
Modern software systems — agentic AI, distributed microservices, zero-trust infrastructure — require quality thinking that matches their complexity. Most QA approaches have not kept pace. We exist to bring that parity: quality architecture designed for the systems organisations are actually building today.
A specialist AI-native quality engineering practice — growing into a team of domain experts across agentic AI testing, enterprise security, intelligent automation, and observability. Every person we bring in is a practitioner. No one learns the craft on a client engagement.
We treat AI as a force multiplier for quality intelligence — not a replacement for it. Agent-driven automation via Playwright and MCP. Context-aware quality systems. Self-healing frameworks. Every tool we deploy is overseen by quality architects who understand its limits as well as its capabilities.
At Axiontest, we are experts in the domains where software quality is not optional — where a failure is not a bug report, it is a business event.
Our expertise spans 15+ years of quality leadership across inspection and compliance platforms, zero-trust security infrastructure, industrial SCADA systems, and AI-native SaaS — each demanding a different quality architecture and a different risk model.
We have designed and shipped AI-assisted quality systems integrating Playwright with Model Context Protocol — autonomous test agents that reason about application behaviour in context. Our AI automation is engineered, not prompted. It is in production, not on a slide deck.
At Axiontest, we are experts across enterprise SaaS, cybersecurity platforms, industrial control systems, and large-scale data intelligence engines. This breadth is not a generalist weakness — it is pattern recognition that single-domain practitioners cannot replicate.
We are assembling a team of domain specialists — agentic AI testing, cloud security architecture, chaos engineering, observability — so every engagement is matched with the right depth of expertise. The right practitioner for your domain, not the available one.
These are real engagements from our founding team's career — the systems and domains that shaped how Axiontest thinks about quality architecture.
Web, mobile (iOS/Android), and cloud microservices platform used for enterprise inspection workflows and operational analytics. Our team architected the entire Cypress automation framework from scratch — reduced full regression from 36 hours to under 10 minutes.
Enterprise zero-trust platform securing communication through encrypted peer-to-peer networking. Validated complex network topologies, firewall rules, and security policies. Network traffic analysis via Wireshark. Performance and throughput testing under high network load.
High-scale recruitment intelligence platform aggregating profiles across the web with Apache Solr-powered search. Validated large-scale candidate indexing accuracy, complex search ranking logic, and backend data integrity between UI and API layers.
Enterprise manufacturing intelligence system with real-time plant monitoring and SCADA visualisation. Validated real-time data streaming from manufacturing equipment, integration between plant-floor systems, and multi-client performance under load.
Not aspirational statements. The convictions that govern every decision we make for a client.
At Axiontest, we do not scale by diluting expertise. Every engagement is overseen by a quality architect with the domain depth to make the decisions that matter — not delegated down a chain until accountability disappears.
We have engineered AI-driven quality systems in production. We also understand precisely where AI reasoning ends and quality architecture judgment must take over. We will always be transparent about the boundary — and we will never automate away the accountability.
If a critical quality risk exists in your system, you will hear it from Axiontest before your users do — regardless of schedule pressure. That is not a policy. It is the reason quality architecture exists.
Axiontest grows by adding domain specialists who have operated at the frontier of their field — not generalists who will learn it here. The standard we apply to our team is the same standard we apply to your system.
We are selectively onboarding founding clients — organisations where software quality is a strategic concern. If that is you, we should talk.
Real engagements from our team's career — the domains, challenges, and quality architecture decisions that shaped how Axiontest operates. Documented from project records, not assembled for marketing.
A large-scale inspection and compliance platform needed continuous testing across web, mobile, and cloud services. Here is how we built the framework that made weekly releases reliable.
Motion Kinetic is a large-scale enterprise inspection and compliance platform — web applications, iOS/Android mobile apps, and cloud-based microservices used for data collection, reporting, and workflow management across complex industrial and enterprise environments.
The platform processes and validates thousands of inspection reports, integrates with enterprise data pipelines, and serves field operations teams who cannot afford system failures or data inaccuracies.
Full regression testing required approximately 36 hours of manual effort per release cycle. With weekly release cadence, the team was spending the majority of QA capacity on repetitive regression rather than meaningful exploratory and new feature testing. The existing test coverage was insufficient to catch integration regressions across web, mobile, and API layers.
Designed and implemented a Cypress-based automation framework from scratch — TypeScript, Mocha, Chai — with Page Object Model architecture and parameterised test utilities for maintainability. Integrated with Azure DevOps CI/CD for automated execution on every push. Built AI-assisted automation workflows using Playwright with Model Context Protocol (MCP) to enable intelligent test agent interactions. Extended coverage to mobile (iOS/Android) via Appium and implemented automated validation of complex reporting workflows across 1,000+ reports.
Validating encrypted peer-to-peer communication, firewall policies, and high-load network throughput for a platform where security failures have direct enterprise consequences.
Invisily is a zero-trust network access platform designed to secure enterprise communication through encrypted peer-to-peer networking and dynamic routing. It is used by enterprises that need to ensure secure remote access, strict access controls, and verifiable network security policies across distributed environments.
Zero-trust platforms require testing that goes well beyond functional correctness. The security guarantees the platform makes to enterprises must be verifiable — encrypted traffic must actually be encrypted, access policies must actually block unauthorized access, and the system must maintain these guarantees under production-level network load. Standard functional testing cannot answer these questions.
Performed functional and non-functional testing across complex network topologies. Used Wireshark for network traffic analysis to verify encrypted communication and protocol behaviour at the packet level. Developed portal automation using Selenium (Python) for administrative dashboards. Executed performance and throughput testing using JMeter and Iperf3 to validate system stability under high network load. Validated firewall rules, port routing, and network security policies across distributed environments.
Deep validation of Apache Solr search ranking, candidate data indexing accuracy, and cross-layer data integrity for a platform where search quality directly determines product value.
TalentBin was a large-scale recruitment search engine that aggregated professional profiles across the web and provided advanced search capabilities for recruiters. Search relevance and data accuracy were the core product — any degradation in search quality or data integrity directly impacted recruiter outcomes.
Search platforms present unique testing challenges. The correctness of Apache Solr indexing, the accuracy of search ranking algorithms, and the integrity of candidate data across the full pipeline from ingestion to UI display are all distinct failure modes — and all consequential. A recruiter searching for a specific skill set needs to trust that the results are accurate, complete, and consistently ranked.
Automated core recruiter workflows and search functionality validation using Selenium (Python). Validated large-scale candidate search indexing and search result accuracy powered by Apache Solr. Executed database validations using SQL to verify data integrity between UI and backend systems. Conducted API testing via Postman for candidate data retrieval and integration endpoints. Tested complex filtering and search ranking logic to ensure accurate talent discovery.
We're a small, senior team working on genuinely hard problems. We hire slowly, pay well, and give everyone real ownership. Remote-first, async-friendly.
All roles are fully remote. We move fast — expect to hear back within 3 business days.
We sometimes hire outside our listed roles for exceptional candidates. Send us your background and what you'd like to work on.
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For privacy-related questions or to exercise your rights, contact our Data Protection contact at: [email protected] · Axiontest Inc., 2025.
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Axiontest provides software testing services including AI testing, automation, security assessment, and related professional services as described on our website. Specific service terms are governed by individual service agreements or statements of work.
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Legal questions: [email protected] · Axiontest Inc., 2025.
Everything you need to get started, integrate your tools, and get the most out of Axiontest.
Welcome to Axiontest. This guide walks you through connecting your first project, running your first test suite, and reading your first quality report.
After signing up, you'll receive an onboarding email with your credentials and a link to your client dashboard. Your account is provisioned within 2 hours of your plan activation.
During onboarding, your dedicated quality architect will request read-only access to the relevant parts of your codebase via GitHub, GitLab, or Bitbucket. We define the scope of access together — we only look at what we need for the engagement, nothing more.
For CI/CD integration, we work with your existing pipeline — Azure DevOps, GitHub Actions, GitLab CI, or Jenkins. No new infrastructure is required on your side.
Before any testing begins, we deliver a written test strategy covering: scope, risk areas, test types, tooling, coverage targets, and reporting cadence. You approve it before we start.
This typically takes 2–3 business days after onboarding. You will know exactly what we are testing, why, and what success looks like — before a single test runs.
Your first quality report lands in your dashboard within the agreed timeline — typically 5–10 business days for initial coverage depending on system complexity. From there, reports follow a weekly cadence with a live dashboard you can check any time.
We integrate Axiontest into your existing GitHub Actions, Jenkins, or GitLab CI pipeline — no new infrastructure required.
We design and run test scenarios for your AI agents — goal completion, tool use, guardrail validation, and adversarial edge cases.
OWASP-aligned security testing, API penetration testing, and prompt injection analysis — findings delivered in audit-ready format.
Weekly quality reports, executive summaries, and a live client dashboard showing your quality posture at all times.
Need help getting started? Email hello@axiontest.io — your dedicated quality architect will respond within one business day.
Move the sliders to match your team. See your estimated savings with Axiontest in real time.
Estimates based on industry benchmarks. Axiontest typically reduces test maintenance time by 70-80% and production incident rate by 60-75%. Individual results vary.