Quality Engineering and AI Testing

Traditional software is deterministic: given the same input, you always get the same output. You can write tests that assert exact outputs and fail reliably when the code is wrong. AI agents are non-deterministic: the same prompt can produce different valid outputs, and the right output changes depending on context, retrieved data, and model version. This means traditional assertion-based testing breaks down. Instead, AI agent testing requires: prompt regression testing — verifying that a given input still produces an output within the acceptable behavioral envelope across model versions; tool-call accuracy testing — checking that agents invoke the right tools with the right parameters for a given scenario; output consistency testing — validating that outputs meet defined structural and semantic constraints rather than matching exactly; and latency and reliability testing — AI systems have higher p99 latency variance than deterministic code, and production SLAs require specific load testing. We've built testing frameworks for production AI agents covering all four areas.

Framework selection depends on the application stack and what's already in your pipeline. For web application UI testing: Playwright is our default for modern web apps (fast, reliable, good debugging); Cypress for teams that want integrated component testing; Selenium where legacy infrastructure requires it. For API and backend testing: pytest with custom assertion layers for Python services; Jest or Vitest for Node.js APIs; Postman/Newman for contract testing in CI. For performance testing: k6 for developer-friendly load testing in CI pipelines; JMeter for complex enterprise scenarios. For AI agent testing: custom pytest frameworks with LLM evaluation harnesses, prompt regression suites, and behavioral constraint validators. CI/CD integration: we wire test suites into GitHub Actions, GitLab CI, Azure DevOps, or Jenkins depending on your existing setup — with test reporting that gates deployments when critical tests fail.

End-to-end QA management means ITMTB acts as your QA function — owning strategy, test design, execution, and reporting — rather than just providing testing resources. It makes sense when: your internal team is small and QA expertise is expensive to hire full-time; you're scaling a product quickly and need QA capacity that can ramp without long hiring cycles; or QA on your current project is an afterthought and you need a structured reset. What's included: test strategy design (what to test, at what coverage level, with what automation ratio), test framework selection and setup, manual and automated test creation for new features, regression suite maintenance, and defect reporting integrated into your team's workflow. We have managed QA for a Series-A delivery startup whose tech debt was causing production failures, clearing the backlog and establishing a release process that allowed regular shipping without incidents.

Performance testing for cloud-native systems is different from traditional load testing because auto-scaling makes the system behave differently under load than it does at rest. We test at multiple layers: application-level load testing (k6, JMeter) to establish latency profiles at 1×, 5×, and 10× expected load; database query profiling under concurrent load to identify slow queries that don't appear in unit tests; cold-start behaviour for serverless and containerised components (latency spike on first request after idle); and AI inference endpoint latency under concurrent requests (p50, p90, p99 matter separately for AI workloads). Outputs: a performance baseline report, identified bottlenecks with proposed fixes, and a load test suite you can re-run in CI to catch regressions before they hit production.

Pricing follows the engagement model. Discovery sprints are fixed-price for a fixed timebox. Build engagements are scoped after discovery — fixed-scope, time-and-materials, or outcome-linked. Managed services are billed monthly with defined SLAs. Total cost depends on scope, integration complexity, QA depth, and post-launch support — we publish a transparent breakdown for buyers evaluating their options.

Every engagement starts with a fixed-scope, fixed-price discovery sprint — typically 2 weeks. You leave with a written roadmap, risk register, cost estimate, and timeline before committing to a build. We then offer four engagement models: fixed-scope builds, time-and-materials, outcome-linked retainers, and managed services post-launch. If discovery doesn't justify proceeding, you can stop there — no obligation.