QA Outsourcing and Software Testing Services

Targeted process audits identify architectural gaps early and keep production stable.

Using targeted process analysis, we identify where your QA strategy creates the most risk and the biggest drain on engineering time:

• Maturity Audit. We find what is making releases unreliable before recommending a fix. The audit covers requirements, test coverage, CI failures, and production behavior. You get a clear view of what is broken, what is only creating noise, and what should be addressed first.
• Requirements Readiness Review. We review requirements before development begins to find unclear acceptance criteria, missing edge cases, and risks that have no clear way to be tested. This gives developers a clearer scope and helps prevent avoidable rework later.
• Release-Risk Assessment. We compare current test coverage with business-critical workflows, recent defects, and areas of frequent change. The result shows where the product is protected and where the team is still carrying release risk manually.
• CI Pipeline Health Analysis. We review your CI history to separate real failures from routine noise, then trace recurring issues back to their source. That may be a brittle selector, a race condition, or a test suite that no longer fits its framework. We also measure how much engineering time those failures consume so the team can decide what to fix, replace, or remove.
• Requirements-to-Code Alignment Audit. We compare requirements, Jira tickets, acceptance criteria, tests, documentation, and source code to identify where the implementation no longer matches the intended behavior. Each discrepancy is tied to the affected workflow and converted into a requirements update, regression test, or remediation task. When code and requirements conflict, a senior engineer and the product team agree on the source of truth.

Reliable automation surfaces clear engineering insights and keeps the release pipeline stable. Teams moving away from Selenium and Cypress want one thing: reliable automation that surfaces real issues instead of noise and brittle tests that break every time the UI changes.

Your test suite stays reliable as your application evolves:

• Automation Strategy. We identify which workflows should be automated first and where each test belongs. The plan balances UI, API, integration, and lower-level coverage so the suite catches real risk without becoming expensive to maintain.
• API & Integration Test Automation. We test services below the UI so failures are caught earlier and are easier to diagnose. Coverage can include APIs, service integrations, authentication flows, data validation, and third-party dependencies.
• E2E Test Suite Development. Our engineers build your test suite on Playwright as the default foundation, using its native support for Chromium, Firefox, and WebKit through a single API. We design tests around real user workflows instead of isolated UI interactions.
• CI/CD Integration & Parallel Execution. We focus on wiring your test suite directly into GitHub Actions or GitLab CI, with sharding configured from day one. Playwright’s built-in worker distribution lets your pipeline run tests in parallel through its own architecture, independent of any paid orchestration layer.
• Self-Healing & AI Test Generation. Leveraging advanced AI analysis of your DOM, user flows, and existing coverage, we accelerate test generation. Our engineers review and refine the generated tests before they become part of your suite. Where appropriate, AI-assisted locator updates can reduce maintenance as the UI evolves, while engineer oversight helps keep the suite reliable over time.

Teams usually move to Playwright because they’re tired of paying for Cypress Cloud or maintaining a Selenium Grid. Playwright’s built-in parallelization changes the economics—most teams see the migration pay for itself within just a few CI cycles.

We help you retool your test stack without losing existing coverage:

• Test Suite Migration. Our team converts your existing test suite into Playwright’s syntax and architecture while keeping your existing test coverage intact. A suite of around 300 Cypress tests typically requires approximately 100–150 engineering hours to convert, depending on the complexity of your current implementation, giving you a reasonable baseline for planning.
• CI Pipeline Upgrade. We rebuild your CI configuration to use Playwright’s native sharding, replacing Cypress Cloud’s paid parallelization with worker distribution that runs at the framework level. Your GitHub Actions or GitLab CI setup gets reconfigured to run the full suite in parallel, cutting runtime and licensing costs at the same time.
• Flakiness Reduction. Our team tunes Playwright’s auto-waiting behavior against your application’s real DOM and network conditions, reducing failures caused by timing and rendering issues. By default, we enable rich diagnostic artifacts, so engineers see the evidence to resolve any failure on the first look rather than the third.
• Framework Refactoring. We rebuild the suite around Playwright, using fixtures, shared login states, reusable helpers, and selectors tied to stable application attributes. We cut duplicate setup, simplify page objects that add only extra layers, and move test data into factories the team can reuse across the suite. That gives you a framework that is easier to understand, easier to extend, and less likely to inherit the same maintenance issues as the old one.
• Test Execution Optimization. We group tests by purpose and use Playwright sharding to run them across multiple CI workers. Pull requests trigger a focused smoke suite, while the full regression suite runs before release or on a schedule. We also cut overlapping scenarios, carry authenticated sessions between tests, and move checks to the API when the browser adds no real value. The pipeline runs faster, and engineers find out sooner when a change breaks a critical workflow.

By taking full ownership of your regression suite, you can focus your developers on new features instead of letting flaky tests distract them.

We sustain a reliable regression suite as your product evolves:

• Ongoing Regression Runs. We run full regression cycles on a schedule or before every release. Every report shows critical release blockers.
• Flaky Test Cleanup. We find, fix, and remove flaky tests that pollute your pipeline. Your team spends less time chasing false failures and starts trusting the results again.
• Regression Suite Maintenance. We keep the regression suite aligned with the current product. We add coverage for new user flows, remove tests for retired functionality, and refactor suites when architecture changes. Production signals show us where maintenance effort delivers the most value for each release.
• Risk-Based Regression Planning. We set the regression scope based on what changed, which workflows that change can affect, and what happens if something fails. Critical paths run before every release, while lower-risk scenarios move to scheduled runs. That keeps the suite focused and avoids spending time on a full regression cycle when the change does not call for one.
• Production-Informed Test Coverage. We look at production incidents, support tickets, and monitoring data to find the failures the current suite missed. For each confirmed issue, we add a regression scenario at the test level where it will catch the problem most reliably. That way, a defect found in production does not quietly return in a later release.

Automation never catches everything. Some of the most critical bugs only show up during exploratory testing. We run targeted manual and exploratory testing to find the bugs automation misses, especially around complex application workflows.

Our team validates the user experience beyond what automated tests can verify:

• Exploratory Testing. Experienced testers actively explore the product to uncover hidden edge cases that scripted tests rarely catch.
• Feature Validation. We manually validate new features before release to make sure they meet business requirements.
• Edge Case Testing. We focus on how real users interact with the product and deliberately test tricky scenarios, unusual inputs, and boundary conditions that often break in production.
• Business Context Review. Senior testers review scenarios where the correct outcome depends on product rules, operational constraints, or roadmap decisions that automation cannot infer from the code alone.
• Requirements Conflict Arbitration. When requirements, code, and observed system behavior disagree, our QA engineers reproduce the issue, collect the evidence, and work with product stakeholders to establish the expected behavior.

LLMs rarely produce the same answer twice. Standard testing built around exact string matches meets its limit here, which is precisely why enterprises deploying LLMs and RAG pipelines are turning to a different kind of evaluation.

We tailor validation to your model’s level of risk:

• LLM/RAG Output Evaluation. We score your model’s outputs using frameworks like G-Eval and DeepEval, adding ROUGE and BLEU benchmarks to measure semantic accuracy against ground truth instead of just literal text matches. Hallucinations get flagged and categorized by root cause, whether that traces back to retrieval gaps in your RAG pipeline or drift in the underlying model.
• Prompt Injection Testing. Our team stress-tests your AI agents against real attack patterns by running structured prompt injection attempts that are designed to bypass guardrails or extract data the system holds. Jailbreak attempts get simulated using the same techniques threat actors use in production environments. Every finding includes a recommended guardrail or prompt update.
• AI Compliance Audit. We benchmark your AI systems against frameworks like the EU AI Act and NIST AI RMF, building a documented risk profile your legal teams can present to regulators or enterprise customers. Every finding includes a recommended remediation.
• Structured Schema Validation. We verify that AI outputs conform to required JSON or YAML schemas, field types, business rules, and downstream system constraints. Invalid or incomplete outputs are blocked before they can trigger an automated action.
• Prompt & Guardrail Refinement. We turn recurring hallucinations, prompt injection findings, and context failures into concrete updates to system prompts, retrieval logic, and guardrails. The updated configuration is evaluated again to confirm that the issue has been resolved.

APIs change constantly across different teams and services. Without contract testing, those changes often break production. We catch breaking changes at the contract level before they cause real damage.

We catch integration issues before they become production incidents.

• Contract Testing. Automated contract tests run directly in your CI pipeline on every pull request that changes an API definition.
• Third-Party API Testing. We test integrations against PSD2 and Open Banking standards, validating authentication flows, consent management, and transaction data across the full request lifecycle. Fintech integrations get evaluated for rate-limiting behavior, error handling, and failover conditions under realistic load.
• Semantic Data Mapping. Our engineers map incoming data against your target schema using semantic analysis, reading the business meaning behind fields rather than relying on exact string matches. We transform legacy formats, malformed CSVs, and inconsistent third-party payloads into structured output.
• Cross-System Workflow Validation. We test workflows that convert unstructured inputs from support tickets, Slack messages, emails, or documents into structured actions inside Jira, CRM, ERP, and other business systems. Each step is validated from the source input through the final system update.
• Deterministic API Execution Checks. We validate extracted IDs, parameters, data types, payload structures, and target API calls before automation can create or update production records. Approval rules are applied to high-impact actions.

Staging rarely predicts how your system will behave under real load. Unpredictable traffic and environmental constraints can break performance in ways that only show up in production — often at a high cost.

We uncover performance issues before users do:

• Load & Stress Testing. Our team recreates normal, peak, and beyond-capacity traffic to see where the system starts slowing down or failing. We track throughput, latency, error rates, and infrastructure usage to define the current capacity and identify the exact breaking points.
• Scalability Testing. To see how the product handles growth, we increase user volumes, transaction loads, data volumes, and concurrent processes. Our engineers test both horizontal and vertical scaling to confirm whether added infrastructure improves performance or simply moves the bottleneck elsewhere.
• Soak & Endurance Testing. We keep the system under sustained load for several hours or days to catch problems that short test runs miss. This helps our team uncover memory leaks, exhausted connection pools, growing queues, resource depletion, and gradual performance decline.
• Database Performance Testing. When a workflow runs slowly, our engineers trace it across API calls, database queries, background jobs, and third-party integrations. We isolate the real source of the delay, whether it comes from inefficient queries, oversized payloads, repeated service calls, slow dependencies, or application logic.
• Network & Resilience Testing. Our team simulates high latency, limited bandwidth, dropped connections, and dependent-service failures in a controlled environment. We then verify retries, timeouts, fallback mechanisms, and recovery behavior to make sure one local failure does not spread across the wider system.