AI‑assisted QA for engineering teams

We apply practical ML to testing where it helps: selecting relevant tests, clustering failures, reducing visual noise, and generating safe test data—integrated with your CI/CD and existing frameworks.

Book a 30-min AI QA consult

Business outcomes this enables

For founders, heads of engineering, and delivery leaders who need predictable releases and lower QA cost.

Faster PR merges

Change-aware selection keeps coverage while cutting PR wait time.

Fewer release delays

Flake control and visual/API gates reduce reruns and rollbacks.

Predictable quality metrics

Escape rate, flake rate, and regression duration reported per release.

Lower QA maintenance cost

Stabilized suites and targeted runs cut compute and human time.

Core capabilities

Focused, defensible uses of ML that improve QA outcomes without hype.

Change‑aware test selection
Select and order tests based on code‑change impact (git diff, coverage delta) to keep CI fast without losing signal.
Diff/coverage mapping to affected tests
Risk‑weighted prioritization
Parallelization planning
CI integration (GH Actions, GitLab, Jenkins)

Outcome: shorter regressions with relevant coverage

Failure clustering & triage
Cluster failures by signature (logs, traces, stack) and route to owners with context to reduce time‑to‑fix.
Log/stack similarity grouping
Duplicate detection
Ownership hints (service/module)
Export to Jira/Azure DevOps

Outcome: clearer signals and faster triage

Visual diff noise control
Reduce false positives with baseline normalization and dynamic‑content masks for reliable UI checks.
Baseline normalization across browsers
Layout shift detection
Dynamic element masking
Review queue with approvals

Outcome: actionable visual checks

Synthetic test data
Generate privacy‑safe data for edge cases, locales, and boundary values to increase coverage without touching PII.
PII‑safe personas
Boundary values & adversarial inputs
Locale/timezone variants
Data seeding for CI

Outcome: broader coverage, safer data

Contract & API validation
Derive contract tests from OpenAPI/GraphQL schemas to catch breaking changes early in pipelines.
Schema → contract tests
Backward‑compat checks
gRPC/GraphQL support
Versioning and gates

Outcome: fewer integration regressions

Telemetry & flake analysis
Capture flake rate, escapes, and coverage deltas with dashboards for trend visibility and governance.
Flake/escape metrics
Coverage delta per release
Suite health dashboards
SLO/SLA gates

Outcome: measurable quality signals

Governance & integration

How we keep AI usage practical, safe, and transparent.

Release governance
Define gates and exception policies for AI‑assisted checks; human override always available.
Data privacy & security
No production data leaves your environment. PII is masked; models run in your cloud when required.
Developer workflow integration
Integrates with PR reviews, CI, and issue trackers. Clear artifacts, no opaque decisions.
Operational visibility
Dashboards for coverage, flake, and risk trends to support planning and audits.

Example workflows

Concrete, auditable flows that integrate with your existing tests and pipelines.

Pull‑request test selection
Map git diff → affected modules → relevant tests. Order by risk weight and runtime to keep PR checks fast.
  • Diff & coverage correlation
  • Risk scoring (critical paths first)
  • Shard/parallelize for CI
  • Gate on flake‑adjusted pass rate
Failure clustering & routing
Group failures by signature (logs/trace/stack) to de‑duplicate noise and route with owning hints.
  • Similarity via embeddings & heuristics
  • Merge duplicates across jobs
  • Ownership hints (service/module)
  • Export issues to Jira/Azure
Visual regression hardening
Normalize baselines and mask dynamic regions to reduce false positives while catching real UI breakage.
  • Cross‑browser baseline control
  • Layout shift detection
  • Dynamic content masks
  • Review queue with approvals
Micro-case: faster PRs
A SaaS team cut PR regression time from 90 minutes to 22 minutes by running only change-correlated tests while keeping risk coverage.
  • Mapped git diff to impacted services and tests
  • Risk-weighted ordering + parallel shards
  • Kept critical-path and contract tests always on
  • Maintained coverage signal; reduced queue delays

Integrations & metrics

Works with your stack. Measures what matters for release decisions.

Integrations
We use your frameworks and CI/CD—no rip and replace.
  • Tests: Playwright, Cypress, Selenium, Appium
  • API: Postman, REST/GraphQL, gRPC
  • CI/CD: GitHub Actions, GitLab, Jenkins, CircleCI
  • Cloud: AWS, GCP, Azure
Quality metrics
Operational signals to track suite health and ROI.
  • Escape rate and defect density
  • Flake rate and stability trends
  • Coverage delta per release
  • Median regression duration

Tooling we use

Established ML frameworks and libraries we integrate for QA workflows.

TensorFlow
PyTorch
Scikit‑learn
OpenCV
Hugging Face

Want to see if this fits your stack?

We’ll walk through your CI/CD and test setup and outline where AI adds value—and where it doesn’t.

Book a 30-min AI QA consultCheck ROI first

We don’t replace your test strategy or auto-generate flaky tests. We augment existing suites where signal matters.