Software & AI Quality Engineering

Independent testing, scoring and quality benchmarking for software, AI and GenAI systems, applications and platforms. Structured assessment across six testing areas.

AI & GenAI System Testing

AI model accuracy testing, LLM validation, bias and safety testing, prompt reliability and AI risk analysis. We assess behaviour, outputs and governance of AI and generative AI systems.

Functional & Core Application Testing

Functional testing, regression testing, integration testing, API validation and release validation. End-to-end coverage of core application behaviour and interfaces.

Automation & DevOps Quality

Test automation setup, CI/CD testing, continuous testing, DevOps QA assessment and release pipeline validation. We score automation coverage and pipeline quality.

Performance & Scalability Testing

Load testing, stress testing, API performance, capacity testing and response benchmarking. Objective metrics against your SLAs and industry baselines.

Data, Cloud & Platform Testing

Data pipeline testing, ETL validation, cloud migration testing, SaaS reliability and container/Kubernetes testing. Platform and data quality assessment.

UX, Mobile & Enterprise Apps

UI/UX testing, accessibility testing, mobile app testing, cross-browser testing and ERP/CRM testing. User experience and enterprise application quality.

Scoring output

  • Software Quality Score (0–100)
  • AI Quality Score (where applicable)
  • Performance rating and benchmark comparison

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Common Challenges

Issues organizations face that drive the need for independent assessment

Inconsistent Release Quality

Production defects slip through due to gaps in test coverage, manual testing bottlenecks or missing quality gates in CI/CD pipelines.

AI/ML Model Reliability

AI systems produce inconsistent outputs, hallucinations or biased results that are difficult to detect without structured testing and validation.

Performance Under Load

Applications slow down or crash during peak traffic because performance and scalability testing was insufficient or not done at all.

Security Vulnerabilities

Software ships with OWASP Top 10 vulnerabilities, insecure APIs or weak authentication because security testing was not part of the development lifecycle.

No Quality Baseline

Teams lack a quantified view of their software quality — decisions are based on gut feeling rather than structured measurement and benchmarking.

Technical Debt Accumulation

Rapid feature delivery without quality oversight leads to mounting technical debt, making systems harder to maintain and more prone to failure.

How AssureSQ Helps

Independent testing, scoring and improvement guidance

Structured Quality Scoring

We deliver a 0-100 quality score across functional correctness, performance, security, code quality and operational readiness — giving you a clear baseline and improvement targets.

AI and GenAI Testing

Specialized testing for AI systems including output quality validation, bias detection, safety testing, hallucination analysis and alignment with business requirements.

Performance and Scalability Testing

Load testing, stress testing and soak testing to ensure your applications handle real-world traffic patterns without degradation.

Security Assessment

Application security testing aligned with OWASP, including API security, authentication testing, data protection validation and vulnerability assessment.

Improvement Roadmap

Every assessment delivers a prioritized roadmap showing exactly what to fix, in what order, with expected quality score improvement for each action.

Frequently Asked Questions

A software quality assessment includes functional testing, performance and load testing, security assessment, code quality review, CI/CD pipeline evaluation and operational readiness checks. The outcome is a structured quality score with detailed findings and improvement recommendations.
Typical assessments take 2-4 weeks depending on the scope and complexity of the application. We can also do focused assessments on specific areas like performance or security in 1-2 weeks.
Yes. We provide specialized testing for AI and GenAI systems including large language models, recommendation systems and computer vision. Testing covers output quality, consistency, bias, safety, hallucination detection and alignment with requirements.
Yes. We can set up quality gates in your CI/CD pipeline, provide automated test suites and establish performance benchmarks that run on every deployment.
The quality score is calculated across multiple weighted dimensions including functional correctness, performance metrics, security posture, code quality, test coverage and operational readiness. Each dimension is scored 0-100 and combined into an overall score.