Software Development · Quality Assurance

Ship with confidence.

Quality assurance is how we make sure what we build meets your criteria — and your business goals. Inabia’s SQA measures raise customer confidence, credibility and efficiency, catching defects before your users ever do.

Test my product → Services we offer
An Inabia QA engineer testing a web and mobile application QA IN PROGRESS

What’s Actually Putting Your Releases at Risk

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  1. AI-generated code shipped with confidence, despite defect rates significantly higher than human-written code

  2. Test coverage chasing 100% completeness instead of focusing on the risk that actually matters

  3. Automated test suites so brittle that minor UI changes break them weekly, eroding trust in the results

  4. Manual testing skipped under release pressure, missing the usability and edge-case issues automation can’t catch

  5. No real strategy for validating AI features — recommendation engines, chat interfaces, anything probabilistic — using testing methods built for deterministic, fixed-logic software

The old question — “manual or automated?” — isn’t the one that matters anymore. The real question is whether your QA strategy can keep up with how fast and how differently software is being built today.

What Real Quality Assurance Should Actually Deliver

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  1. risk-based coverage

    Risk-based coverage that protects what actually matters to your business and users, not coverage for coverage’s sake

  2. ci regression

    Automated regression testing integrated directly into your CI/CD pipeline, so quality gates don’t slow releases down

  3. human judgment

    Human judgment applied where automation hits its ceiling — exploratory testing, usability, and the unexpected behavior scripts were never written to catch

  4. ai validation

    Validation built specifically for AI-driven features, where the same input doesn’t always produce the same output

  5. defect reporting

    Clear, actionable defect reporting that gets fixed fast, not buried in a backlog nobody reads

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QA Services Built Around Where the Risk Actually Lives

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Manual & Exploratory Testing

Structured, scoped sessions focused on usability, edge cases, and the kind of contextual judgment automation consistently misses — still the most reliable way to catch what scripts were never written to look for.

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Test Automation & CI/CD Integration

Automated test suites built into your pipeline so quality checks run on every merge, not just before a release deadline, catching regressions before they reach production.

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API & Microservices Testing

As architectures get more distributed, the connections between services become the riskiest part of the system. We validate API quality and integration points before they become production incidents.

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Performance & Load Testing

Realistic load scenarios that test how your application actually behaves under real-world traffic, not just clean lab conditions.

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Security & Compliance Testing

Testing built to satisfy regulatory requirements like GDPR, HIPAA, and SOC 2, with audit trails and documented evidence — critical for any business in a regulated industry.

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AI/ML & Generative Feature Validation

Testing designed for probabilistic systems: AI agents, recommendation engines, and LLM-driven features that behave differently from one run to the next, where traditional pass/fail assertions don’t apply.

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Mobile & Cross-Platform Testing

Real-device validation across the platforms your users actually carry, since mobile reliability issues surface differently than they do in a desktop browser.

Why Businesses Choose Inabia for QA

  1. Testers who treat quality as a strategic function, not a final checkbox before launch

  2. A risk-first approach that protects what actually matters instead of chasing meaningless coverage metrics

  3. Automation built to reduce maintenance overhead, not create a second codebase that constantly breaks

  4. Specific expertise validating AI-generated code and AI-driven features, not just traditional fixed-logic software

  5. Clear reporting that gives your team and leadership real visibility into release readiness

Our QA Process

  1. gate 01

    Assess risk

    We identify where defects would actually hurt your business most, not just where they’re easiest to find

  2. gate 02

    Define strategy

    A test plan combining automation, manual judgment, and AI-specific validation matched to your application’s actual risk profile

  3. gate 03

    Build & integrate

    Automated suites built directly into your CI/CD pipeline for continuous, not one-time, quality checks

  4. gate 04

    Execute & validate

    Functional, performance, security, and exploratory testing run against real-world conditions, not just clean test environments

  5. gate 05

    Report clearly

    Defects documented with the context engineering teams actually need to fix them fast

  6. gate 06

    Monitor & improve

    Ongoing test maintenance and strategy refinement as your application — and your release cadence — evolves

Who This Is For

Quality Isn’t a Phase. It’s What Protects Every Release.

The cost of catching a defect before release is a fraction of the cost of catching it after — in engineering time, customer trust, and in some industries, regulatory exposure. A strong QA strategy isn’t overhead. It’s what makes shipping fast and shipping safely the same thing.

Contact Inabia today →

Frequently Asked Questions

What is software quality assurance (SQA)?

SQA is the process of validating that software functions correctly, performs reliably, and meets security and compliance requirements before and after release, combining automated testing, manual evaluation, and ongoing monitoring throughout the development lifecycle.

What’s the difference between QA and software testing?

Testing is the activity of executing test cases to find defects. QA is the broader strategic discipline of building quality into the development process itself, including process design, risk assessment, automation strategy, and continuous improvement, not just defect-finding at the end.

Do you test AI-generated code differently than human-written code?

Yes. AI-generated code carries a meaningfully higher rate of logical and security flaws than human-written code, which means it requires more rigorous validation rather than less, especially around edge cases and security-sensitive logic that AI tools tend to miss.

Should we automate all our testing or keep manual testing too?

Both. Automation is essential for regression and high-volume repetitive checks, but it only verifies what it’s explicitly told to verify. Manual and exploratory testing remain critical for usability issues, edge cases, and unexpected behavior that scripts were never written to catch.

How do you test AI-driven features like chatbots or recommendation engines?

These systems are probabilistic — the same input can produce different, equally valid outputs — so they require validation approaches built around behavior ranges and risk thresholds rather than traditional fixed pass/fail assertions.

How much does QA as a service cost?

Cost depends on application complexity, the mix of manual versus automated testing required, and whether ongoing CI/CD integration is part of the engagement. We scope this with you directly based on your actual risk profile and release cadence, rather than a flat package price.