Quality engineering isn't the last thing you add before a release — it's the first thing you build in. Our engineers embed AI-assisted test automation, performance engineering, chaos engineering, and release assurance directly into your CI/CD pipeline, so quality is a continuous property of how you ship, not a gate at the end of a sprint.
TickingMinds Quality Engineering embeds AI-assisted test automation, performance engineering, chaos engineering, and release assurance directly into your CI/CD pipeline. Delivered by engineers and optionally accelerated by the T-Sigma platform, it treats quality as a continuous property of delivery, not a gate at the end of a sprint.
Traditional QA runs at the end of a sprint: code is written, then someone tests it, then someone signs off. Every step in that chain is a bottleneck, and every defect it catches has already cost more to fix than it would have on day one. Quality engineering inverts that order — automation, performance baselines, and resilience checks run continuously, inside the same pipeline that ships your code, so problems surface while they're still cheap to fix.
Our engineers embed five capabilities into your delivery pipeline, scoped to what your systems and risk profile actually need — not a one-size-fits-all audit.
Self-healing test scripts that use model intelligence to adapt as your UI and APIs change, replacing brittle record-and-playback suites that break every release. Where useful, this is powered by T-Sigma Test Studio; where it isn't, we build directly against the frameworks your team already owns.
Continuous performance baselines — not a one-time load test the week before launch — using k6, Gatling, or JMeter wired directly into CI/CD. Regressions are caught the day they're introduced, before they reach a peak-traffic event.
Deliberate failure injection — a killed pod, a slow dependency, a dropped connection — to test whether your system's resilience assumptions hold under real stress, before an incident tests them for you.
Automated checkpoints that enforce release criteria in the pipeline itself, replacing manual approval bottlenecks with evidence-backed, repeatable gates.
A risk-based view of where your existing coverage actually protects you and where it doesn't — so testing effort goes where a defect would hurt most, not just where it's easiest to automate.
BFSI, insurance, and healthcare engagements carry a sixth requirement: every gate, every test, and every release needs to generate evidence an auditor can check, not just a green checkmark an engineer trusts.
Testing is reactive — it checks work that's already done.
Quality engineering is proactive — it's built into how the work gets done in the first place.
A 2–4 week assessment delivers a DORA metrics baseline and a prioritized roadmap — no commitment required to see where you stand.
Replace manually maintained regression suites with intelligent, self-healing coverage that adapts as your application evolves — without a growing maintenance backlog.
Systematic elimination of entire classes of outage for banking, payments, and clinical systems — found in a controlled experiment, not a 3am incident call.
Continuous performance baselines for retail, ecommerce, and financial services — so the traffic spike that used to cause an incident becomes just another Tuesday.
Start with a 2–4 week assessment — a DORA metrics baseline and a prioritized roadmap, no commitment required.
Get a QE AssessmentCapture regulatory obligations and system knowledge once, so quality engineering decisions reflect what your business and auditors actually care about.
The AI-assisted automation capability of this practice, productized — specialized agents that generate and maintain test coverage across your stack.
If AI agents are part of what you ship, Attest independently certifies them — a natural pairing with a mature quality engineering practice.