Automated testing services that give your team confidence to ship fast
Innostax builds automated test suites that catch regressions before they reach users — unit tests, integration tests, and end-to-end coverage running on every commit, maintained as your product grows. The foundation that lets your team move fast without breaking things.
The automated testing problem most teams accumulate
Test coverage that exists on paper and fails in production is worse than no coverage at all.
Most engineering teams know they need automated testing. The gap between knowing and having it is where the problems live.
Tests written under time pressure that test the happy path and nothing else. An end-to-end suite that takes 45 minutes to run and fails intermittently for reasons nobody has time to investigate. Unit tests so tightly coupled to implementation details that every refactor breaks half the suite. Coverage metrics that look healthy and miss the three user flows that actually matter.
Bad automated testing doesn’t just fail to catch bugs — it creates false confidence. A green CI build that doesn’t mean what it’s supposed to mean is more dangerous than no CI build at all, because it removes the instinct to check manually.
Innostax builds automated test suites that are designed to be maintained, not just written. Coverage that grows with your product. Tests that fail for real reasons, not flaky infrastructure. A CI pipeline that a green build on actually means something.
What we build and maintain
Core Types of Test Automation Services
Every engagement is different. Use the links below to explore focused pages for this service.
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Unit testing
Tests for individual functions, methods, and components in isolation — fast, deterministic, and the foundation of a healthy test suite. We write unit tests that test behaviour, not implementation — so they survive refactoring without needing to be rewritten alongside the code they cover.
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Integration testing
Tests for how your system's components work together — API endpoints, database interactions, service-to-service communication. Integration tests catch the failure modes that unit tests can't: the database query that works in isolation and times out under real load, the API response that parses correctly in the unit test and fails when the real service returns a slightly different format.
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End-to-end testing
Full user journey tests that simulate real user behaviour through your application — from login to core workflow to completion. We build end-to-end suites that are reliable and maintainable, not the kind that fail intermittently and get disabled because nobody has time to fix them.
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API contract testing
Tests that verify the contracts between your services — ensuring that when a service changes its API, every consumer of that API is validated before the change ships. Critical for microservices architectures where a breaking API change can cascade across the system.
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Regression test suite build and maintenance
For teams with existing products and incomplete test coverage, we build the regression suite that protects your most critical user flows — starting with the highest-risk areas and expanding from there. And we maintain it as your product evolves, so coverage doesn't decay as the codebase changes.
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CI/CD pipeline integration
Automated tests are only as useful as the pipeline that runs them. We integrate your test suite into your CI/CD pipeline — configured to run on every commit, with results that are fast enough to be actionable and reliable enough to be trusted.
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How AI fits into automated testing
AI expands test coverage. Engineers decide what coverage means.
AI-assisted test generation gives our engineers a starting point that’s broader than manual test writing alone would produce. Given a feature spec, a code diff, or an API definition, AI generates candidate test cases that the engineer reviews, validates, and extends. Coverage improves. Time-to-coverage shrinks.
AI confidence scoring on PRs runs before human review begins — flagging patterns associated with common failure modes and identifying areas of the code where test coverage is thin relative to complexity. Engineers focus their review time on the judgment calls that matter.
AI-assisted flakiness detection identifies test failures that correlate with infrastructure conditions rather than actual code behaviour — so flaky tests are fixed or quarantined rather than ignored and disabled.
What AI doesn’t replace: the engineer’s judgment about what coverage actually means for your product. Which user flows are critical enough to warrant end-to-end coverage. Which integration points carry the most risk. Which edge cases are worth the maintenance overhead of an automated test. Those decisions stay with the engineer — AI expands the options, not the accountability.
Coverage that's useful on day one and maintainable on day 365.
Coverage audit first.
For teams with existing products, we start by understanding what you have — mapping current coverage against your most critical user flows, identifying the gaps that represent the highest production risk, and prioritising what to build first. We don’t recommend automating everything at once. We recommend automating what gives you the most confidence for the least maintenance overhead.
Behaviour-driven tests, not implementation-driven.
Tests written against implementation details break every time the implementation changes. We write tests that verify behaviour — what the code is supposed to do — so the suite survives refactoring without requiring constant maintenance.
Fast feedback loops.
A test suite that takes 45 minutes to run doesn’t get run. We design test suites for speed — fast unit tests that run on every save, integration tests that run on every commit, end-to-end tests that run on PR merge. Feedback at the right speed for the decision it’s informing.
Maintained as the product evolves.
Test suites decay when the product changes and the tests don’t. We treat test maintenance as part of the development workflow — updating tests when features change, removing tests for deprecated functionality, and expanding coverage as new features are built.
A test suite that gives false confidence is worse than none. You'll know which kind we build within two weeks.
2-week free trial embedded in your pipeline.
Real automated tests on your actual codebase, integrated into your CI/CD pipeline, running on real commits. You’ll see within two weeks whether the coverage is meaningful or just metric-padding. If it’s the latter, walk away. No invoice.
1-day termination notice.
If the test suite we’re building isn’t delivering the confidence it should, you’re out tomorrow. No lock-in, no notice periods.
Engineers who maintain what they build.
Great Place to Work certified — the engineer who designs your test architecture in month one is still maintaining it in month six. Test suites written by engineers who won’t be around to maintain them are a liability, not an asset.
Automated testing on the record
What managed delivery looks like in the real world.
Technique
100% successful migration
The automated validation layer on Technique’s data migration was the difference between a successful migration and a data integrity incident. Every record was validated programmatically before the old system was decommissioned. Zero data loss isn’t a lucky outcome — it’s what systematic automated validation produces.
Ashore
Daily shipping velocity
Shipping new features on a near-daily cadence without a regression suite that runs on every commit means regressions reach users. Innostax’s automated testing layer on the Ashore codebase maintained quality at that velocity — catching regressions before they merged, not after they shipped.
Nuw
97% retention
Consumer app retention at 97% requires a product that doesn’t crash, doesn’t regress, and doesn’t introduce instability with every new feature. The automated test coverage on the Nuw build was part of the quality foundation that made that retention rate possible.
Automated Testing Services for Engineering Teams, Legacy Systems, and Scalable Codebases
Engineering teams shipping frequently
who need regression coverage that keeps pace with their development velocity. If you’re merging multiple PRs a day, you need automated tests that catch regressions before they merge — not a manual QA pass that can’t keep up.
Teams with legacy codebases and thin coverage
who need to build confidence before they can build new features. The coverage audit tells you where you’re most exposed. The build plan tells you how to fix it.
CTOs and engineering leads inheriting a codebase
who need to understand what’s tested and what isn’t before they make changes. The coverage audit is often the most valuable thing we deliver in the first engagement.
Teams scaling their engineering organisation
where new engineers joining the team need a test suite they can trust to tell them when they’ve broken something. Good automated coverage is what makes a codebase safe to hand to someone new.
Tech stack
Automated Testing Tech Stack and Tools We Use
We use automated testing tools, CI/CD, and coverage systems to ensure reliable high-quality software delivery.
Automated Testing FAQs
Coverage percentage is a proxy metric, not a goal. 80% coverage that covers the wrong 80% of your codebase is less valuable than 60% coverage that covers every critical user flow and integration point. We start by identifying what matters most — your highest-risk user flows, your most complex integrations, your most frequently changed code — and build coverage there first.
Flaky tests are a quality problem, not a tolerable nuisance. A test that fails intermittently for infrastructure reasons rather than code behaviour trains engineers to ignore test failures — which defeats the purpose of having tests. We identify flaky tests, fix the underlying cause where possible, and quarantine them where it isn't. A green build should mean something.
For a greenfield project, we build coverage alongside development — so by the time the first feature ships, it has tests. For existing codebases, the timeline depends on the size and complexity of the system. We'll give you a realistic estimate after the coverage audit, not before.
The testing pyramid is a useful starting point: more unit tests, fewer integration tests, fewer end-to-end tests — because unit tests are fast and cheap, end-to-end tests are slow and expensive. But the right balance depends on your architecture. Microservices architectures need more integration and contract testing. Monoliths with complex user flows need more end-to-end coverage. We'll recommend the right balance for your specific system.
Yes. We start with an audit — understanding what exists, what's flaky, what's outdated, and what's missing. We fix what's broken, remove what's obsolete, and expand what's thin. The goal is a test suite you can trust, not one you have to apologise for.