AI-Powered Testing Automation

Set priorities with evidence. Use continuous integration automation to pinpoint technical debt, security gaps, and test-suite risks with AI precision that guides the next sprint and quarter.

Legacy bugs stay hidden, and flaky test suites waste valuable time, and root causes often disappear without continuous integration software that links code, data, and infrastructure in real time. You need a single, defensible view of risk that ties failures, performance limits, and quality gaps to business flows and release plans.

• Codebase & architecture scan. Static and semantic analysis to surface hotspots, dependency tangles, and change-prone modules that destabilize tests. Output: risk heatmap and prioritized remediation list tied to user journeys.
• Testability review. Threat modeling of critical flows, API surface review, access controls, and data handling. We align findings with security controls, continuous integration tools, and ISO-grade practices for immediate hardening and measurable auditability.
• Evidence pack. Traceable documentation across eight quality dimensions — architecture, performance, security, business logic, data quality, open-source usage, UI/UX, and source code, so stakeholders can approve with confidence.
• Risk register. Risk log with expected monetary values, mitigation owners, and schedule/cost baselines to keep delivery decisions transparent and defensible.
• Roadmap. A 30-, 60-, 90-day plan that sequences fixes, tunes suites, and removes release blockers — all backed by proven continuous integration solutions and our AI Solution Accelerator™ delivery flow.

Your team leaves the assessment with a single source of truth for quality risk, a pipeline enforced by clear gates, and a remediation roadmap that protects delivery speed without gambling with your product’s reputation.

Turn requirements into runnable proof. Generate generative test cases and executable scripts from user stories and specs with LLMs and semantic analysis, aligned to your delivery plan and integrated into our Accelerator methodology.

Ambiguity in BRDs and scattered acceptance criteria breed gaps. Manual authoring lags behind change, while coverage maps rarely match what users actually do. You need a disciplined path from intent to verification, powered by AI test optimization that captures requirements, ranks risks, and produces tests with clear traceability and ISO-grade quality attributes across the pipeline.

• Requirements-to-test synthesis. Extract test objectives, acceptance rules, boundaries, and equivalence classes from PRDs and user stories to generate structured NLP test scripts automatically.
• Scenario modeling. Model flows with decision tables and state transitions; produce minimal sets that maximize coverage, using pairwise and risk-driven selection.. Trace links back to quality attributes taxonomy (ISO/IEC 25010).
• LLM-to-code generators. Convert approved scenarios into executable tests for Playwright, Cypress, or Selenium — each one optimized as a reliable continuous integration test for fast pipelines. Human-in-the-loop review stays the default for reliability and governance.
• API & contract test derivation. Generate request/response suites from OpenAPI and event contracts, including negative and chaos paths. Ship mocks and stubs ready for pipeline execution.
• Traceability matrix. Auto-link tests to requirements, risks, and quality attributes pulled directly from your continuous integration server; expose coverage gaps against golden paths and performance goals to guide what gets automated next.
• Expected results. Turn requirements and code diffs into executable UI, API, and performance tests in minutes with LLM-driven generation — all inside a developer-friendly CI solution designed for speed and stability.

By turning narrative intent into executable evidence, your teams gain sharper coverage, faster iteration, and release confidence that compounds, protecting your reputation and the trust your customers place in your product.

Ship clean changes at speed with automated suites built for robust continuous integration testing across UI, API, and business logic layers.

Selector drift, outdated cases, and long test runtimes erode confidence. Teams need a signal that mirrors real user paths, enforced by pipeline quality gates and powered by continuous integration services with AI-driven execution and performance awareness. Our Accelerator approach anchors testing in mature delivery processes and CI/CD, giving releases a stable, repeatable rhythm.

• Regression/smoke/sanity orchestration. Risk-ranked suites mapped to golden paths and high-change modules, supporting continuous delivery and continuous integration with incremental runs per commit and full sweeps per release window..
• Ui Automation at scale. Executable tests for Playwright/Cypress/Selenium with resilient waits, stable assertions, and adaptive locator strategies to keep suites current as the UI evolves.
• API & contract automation. Generation and execution of request/response checks from OpenAPI and event contracts, including negative paths and backward-compatibility probes.
• Pipeline-native parallelism. Shard, tag, and prioritize tests across CI stages using a free continuous integration server that supports GitLab CI, custom tags, and parallel execution.
• Performance-aware functional runs. Functional scenarios are seeded with realistic user patterns and prioritized through smart test execution to surface early capacity risks and preempt bottlenecks before dedicated load tests begin.

Your product gains a living safety net that mirrors business behavior through autonomous testing and keeps releases predictable, protecting customer trust and the reputation your company earns with every deployment.

Reveal bottlenecks before launch. Model real user journeys, peak waves, and service contention with AI-driven workloads that expose performance risk early and guide concrete fixes.

Late-stage surprises crush confidence. Synthetic traffic often misses how customers actually move through your product, while capacity limits hide across tiers and environments. You need a pre-release signal that mirrors reality, backed by enforceable gates and clear ownership across the pipeline.

• Workload modeling from real behavior. Synthesize traffic from production telemetry and user paths; capture diurnal patterns, bursts, and long-tail interactions to seed credible load models.
• Capacity baselines. Establish throughput and latency targets with pass criteria wired into quality gates; publish thresholds that the pipeline can verify on every run.
• Peak, burst & seasonal testing. Stress services with controlled spikes, rolling peaks, and queue pressure to validate autoscaling and backpressure strategies before real traffic hits.
• End-to-end latency budgeting. Budget latency across UI, API, services, and data layers inside CI CD continuous integration pipelines; trace timing through deployment and data-model viewpoints for targeted remediation.
• Failure-mode drills. Introduce resource caps, instance kills, and dependency slowness; validate graceful feature degradation and recovery paths with a documented risk log and owners.

You gain predictable launches under real-world load, faster diagnostics when demand surges, and a performance posture that protects customer experience — and the reputation your company earns with every peak.

Keep suites green through change. Machine learning powers test maintenance automation by repairing selectors and stabilizing scenarios as your UI and logic evolve, preserving signal and reducing engineering drain.

UI shifts, attribute churn, and asynchronous behavior turn stable suites into noise. Teams burn cycles fixing brittle locators while real risks slip by. Self-healing automation brings adaptive recognition and intent-aware matching into the pipeline, aligned with our Accelerator methodology for quality gates and CI discipline.

• Adaptive locator intelligence. Multi-signal matching across structure, attributes, text, and visuals. When DOMs change, models rebind elements and keep steps executable without manual edits.
• Intent-centric action repair. Semantic understanding of test steps (“add to cart”, “submit order”) allows fallback strategies when UI layouts shift, preserving business-flow coverage.
• Visual similarity. Computer vision and OCR augment DOM data to find targets across re-skinned interfaces, modals, and canvas-heavy components.
• Auto-quarantine. Statistical scoring separates true defects from timing issues. Unstable tests move to quarantine with the owner and fix hints, ensuring clean signals inside GitLab CI CD pipelines and reducing friction in delivery.
• Change hotspot awareness. Commit metadata and dependency graphs steer healing focus to high-change modules, shortening feedback loops during active sprints.

Your teams gain suites that evolve with the product, keep risk visible, and free engineering hours for work that compounds, strengthening the reputation your company earns with every release.

Catch what humans miss. Self-healing automation complements computer-vision checks and multi-device coverage, helping detect and adapt to layout drift, rendering quirks, and UX regressions before release.

Tiny UI shifts break flows, brand consistency slips between devices, and pixel-perfect pages degrade under real data. You need visual evidence tied to user journeys, enforced by pipeline gates, and aligned with multidimensional architecture views for credible sign-off.

• Visual AI diffing. Visual regression with AI uses semantic image comparison with tolerance windows for fonts, anti-aliasing, and dynamic content, so signals highlight the meaningful UI deltas that matter.
• Layout validation. Grid, flex, and typography checks across breakpoints; viewport sweeps detect overflow, clipping, cumulative layout shift, and modal layering issues.
• Cross-browser matrix. Parallel runs across Chromium, WebKit, and Gecko families with version pinning; results are mapped to quality gates inside CI/CD for predictable releases.
• Mobile & tablet device cloud. Real hardware coverage for iOS and Android with camera, sensors, and network throttling; evidence packaged for stakeholder reviews.
• Localization sweeps. Dynamic copy, RTL layouts, and locale-specific number/date formats validated at scale to protect communication in every market.
• Performance hints. Visual runs instrumented with timing markers; slow paints and long tasks bubble up as early warnings for performance and UX teams.

Your product gains a consistent face on every screen and a defensible trail of proof, protecting brand equity and the customer trust that compounds into your company’s legacy.

Unblock reliable runs. Generate privacy-safe data, spin up clean environments on demand, and keep test beds stable with continuous, AI-driven monitoring.

Manual data prep and drifting environments wreck determinism. Suites fail for reasons unrelated to code. You need IaC-driven environments, consistent datasets, and pipeline gates that enforce quality and security from commit to release — within the Accelerator methodology lifecycle.

• Masked data generation. Produce realistic datasets from requirements and golden paths, with masking for sensitive fields and coverage of edge cases for functional and performance runs.
• Environment as code (EaC). Provision ephemeral test beds through Terraform-based modules and shared templates, fully compatible with continuous delivery continuous integration lifecycles.
• Schema & contract sync. Auto-validate schemas and API contracts across environments; block breaking changes through CI/CD gates and publish diffs for fast remediation.
• Stability & drift monitoring. Continuous checks for config drift, data freshness, and service health. Alerts include root-cause hints tied to architecture and deployment viewpoints for targeted fixes.
• Seed strategies per stage. Calibrated datasets for unit, integration, E2E, and performance stages. Deterministic seeding yields comparable metrics sprint after sprint.
• Parallel-ready environments. Parallel-ready environments integrate seamlessly with continuous build software, reducing wall-clock time while preserving diagnostic fidelity and governance signals.

Your teams gain deterministic tests, cleaner signals, and faster cycles — evidence-backed reliability that strengthens customer trust and the reputation your company earns with every release.

Turn real usage into a feedback engine. Shift-right analytics collect live telemetry, logs, and user signals to spawn new scenarios, predict failures, and keep quality rising between releases.

After launch, issues emerge in the wild: edge paths, peak traffic, and configuration drift. Signals translate into action across security, performance, and reliability — all triggered through smart pipeline automation within the Accelerator methodology.

• Live telemetry harvesting. Stream metrics and logs from production and correlate them with your GitLab CI CD pipeline to highlight high-impact gaps that deserve new tests or hardening.
• New test seeds. Mine clickstreams and session replays to generate high-value scenarios; feed them into design and functional suites for coverage that mirrors reality.
• Proactive anomaly detection. Statistical and ML detections across latency, error rates, and resource profiles; early warnings trigger targeted checks and structured remediation.
• Performance drifts. Track SLOs over time using data collected through your continuous build server; when latency or throughput slips, open performance work items with clear ownership and pipeline-verified fixes.
• Environment drift control. Detect config changes and dependency shifts; publish diffs tied to architecture and deployment viewpoints for precise rollbacks or updates.
• Incident-to-test loop. For every incident class, add a regression guard: synthetic data, reproducible steps, and pipeline gates that prevent recurrence.
• Cost & efficiency telemetry. Measure capacity use and test efficiency; tune concurrency and environments with budget-aware controls from our delivery playbooks.

Your product gets a real-time early warning system and a continuous flow of real-world tests — quiet launches, faster recovery, and a reputation for reliability that compounds into your company’s legacy.