As your developers ship code faster with Copilots & AI Agents, we build the automated test layer to match that pace. Expect CI/CD/CT integration, SDET review, and full IP ownership.
CMMI
Level 3
HIPAA
Compliant
AWS
Partner Network
ISO Certified
For Data Quality & Security
SOC 2
Type II Compliant
Most QA teams already have some automation. The problem is usually that it was built for a slower
release rhythm and has not been redesigned since.
Every UI change breaks dozens of scripts, and fixing them crowds out new coverage.
You rely on a legacy or no-code testing platform that costs a lot per seat and is hard to extend.
Chatbots, AI search, and Agents shipped without any way to catch quality regressions.
APIs, data pipelines, and service contracts are tested by hand or not at all.
Releases wait on a manual cycle, or the full suite runs too long to gate every merge.
Pick one test automation service to fill a specific gap, or combine them into a complete automation
program across every layer of your stack.
Decide what to automate, then build it on a base that lasts.
A maturity audit of your current tests, a risk-ranked backlog of what to automate first, and a cost model showing when the investment pays back.
Custom frameworks on Playwright, Selenium, or Cypress, with page objects, reporting, BDD (Cucumber, SpecFlow) where it helps.
Move from Selenium, UFT/QTP, TestComplete, or manual scripts to a modern stack, with AI-assisted conversion and engineer-verified parity.
Our QA experts build complete test coverage that follows the testing pyramid, not just the screen.
We test end-to-end user journeys on Chromium, Firefox, and WebKit, run in parallel on your stack (BrowserStack, Sauce Labs, and LambdaTest).
Test native, hybrid, and cross-platform apps (Flutter, React Native) with Appium, Espresso, XCUITest, Maestro, or Detox on real-device clouds.
Our QA experts test contracts with Pact and real dependencies in Testcontainers, catching issues before deployment.
Automated checks on schemas, transformations, and data quality in pipelines, using dbt tests, Great Expectations, and SQL-based validation.
REST, GraphQL, gRPC, and event-driven services tested with REST Assured, Karate, Postman/Newman or Playwright's API testing.
We test Windows, macOS, and Electron apps, plus legacy and terminal-based systems, using FlaUI, WinAppDriver, and other tooling where needed.
Our test automation services also help you build checks that ride along with every functional run.
We do axe-core and Pa11y checks against WCAG 2.2 on every build, paired with manual audits for issues automation cannot detect.
Automated runs across locales, time zones, screen sizes, and OS versions to catch truncated text and region-specific bugs.
Lightweight latency budgets and baseline security checks inside functional suites, combined with our full performance and security testing.
Our test automation service also validates what keeps automation fast, stable, and useful after launch.
CI/CD/CT suites wired into GitHub Actions, GitLab CI, Jenkins, or Azure DevOps, with parallel runs, test impact analysis, and merge gates.
We automate the groundwork tests depend on: privacy-safe test data, test environments, and stand-ins for external systems that are unavailable or costly to call.
We provide dashboards on coverage, pass trends, run time, and escaped defects, using Allure, ReportPortal, or your observability stack.
A dedicated QA team that keeps suites current as your product changes, on a dedicated or embedded model.
Share your stack and release process. A test automation architect will send back a prioritized roadmap, a recommended framework, and an effort estimate.
No-code and AI-native testing platforms are genuinely useful. The question is what they cover, who maintains
them, and what happens as you scale.
| Feature | Off-the-Shelf Test Automation Tool | Our Test Automation Services |
|---|---|---|
| Time to first tests | Fast. Record or describe a flow, and it runs within days. | Slightly slower start, as the framework is designed around your architecture first. |
| Coverage depth | Strongest at UI flows. API, contract, data, and integration layers are often limited. | Every layer of the pyramid: unit, API, contract, data, UI, visual, and accessibility. |
| Complex logic & edge cases | Not very effective for dynamic data, multi-system workflows, or custom assertions. | Built for your use case, so you can model any scenario, dependency, or assertion. |
| Ownership & lock-in | Tests usually live in the vendor's format and platform, and are hard to export. | Open-source code in your repositories. You own every line, with no platform dependency. |
| Cost as you scale | Licenses per seat, per run, or per test, rising with usage. | Engineering effort up front, then low running cost on open-source tooling you control. |
| How AI is used | Built in, but opaque. Healing and generation decisions are hard to audit. | Chosen per task, with every AI-made change logged and reviewed by an engineer. |
| Testing AI features | Rarely supports evaluation of non-deterministic LLM or agent output. | Dedicated evaluation suites for chatbots, RAG, and agents built alongside functional tests. |
| Maintenance | Your team still triages failures and updates flows. | We maintain, triage, and improve suites as part of the engagement. |
We do not insist on replacing it. Many clients keep a no-code platform for business-user smoke tests while we automate the API, data, and integration layers underneath, all reporting into one pipeline.
Review existing tests, pipeline, defect history, and release pain points.
Choose tools, design the framework, and agree on coverage targets per layer.
Start with the highest-risk flows, with AI drafting and SDETs reviewing every test.
Wire suites into CI/CD as merge and release gates, with live reporting.
Maintain, prune slow or flaky tests, and expand coverage as the product grows.
Solved an acute problem
A commercial lender's underwriters spent 20–30 hours per loan file on manual data entry. We built a custom-trained document processing and workflow platform on AWS.
35%
higher throughput
99.5%
extraction accuracy
Rebuilt a broken core system
A bath & lighting brand in 55+ countries had site search converting just 10–15% of searchers. We trained a transformer ranking model on their own catalog and query logs.
80%
more accurate search
72%
higher conversion
Found the bigger problem
An eCommerce retailer brought us in for nopCommerce upkeep. The real bottleneck was inventory and pricing scattered across five channels — so we built the fix.
60%
increase in sales
~90%
tasks automated
Provided a dedicated engineering pod
A core retail POS platform had to outgrow three technology eras without losing what made it work. The same embedded pod carried it from CGI to PHP to AWS over 15 years, so no knowledge was ever lost in handover.
15+ years
Zero re-tendering and handovers
Start with repetitive, stable, business-critical tests: smoke tests, core user journeys such as login and checkout, and API tests for key services. Exploratory, usability, and rapidly changing features are usually better kept manual.
Playwright is the most common choice for new web projects because it supports Chromium, Firefox, and WebKit, multiple languages, and parallel runs out of the box. Selenium suits large existing suites and broad language needs. Cypress suits JavaScript teams testing single-page apps. Our test automation consultants can help you choose the right tool for your needs. Contact us at info@technoscore.com.
TechnoScore uses AI to generate tests from your requirements and code, repair broken locators, sort test failures by likely cause, and explore apps through browser agents. In our test automation service, SDETs review every AI-made change before it affects a release decision.
A framework with the first critical flows running in CI typically takes a few weeks. Broad coverage across UI, API, and data layers builds up over the following months, depending on application size and existing tests.
Yes. Because LLM output varies from run to run, our test automation experts use evaluation-based testing: golden datasets, a rubric, model-graded scoring, and adversarial prompts. These run automatically whenever a model, prompt, or data source changes.
We compare build and maintenance costs against manual testing hours saved, shorter release cycles, and fewer defects reaching production. Your test automation audit includes a cost model showing the expected payback point for your specific suite.
We use stable locators, proper wait strategies, isolated test data, and independent tests, then track flakiness in the pipeline. Unreliable tests are automatically quarantined while our testing experts fix the root cause rather than retrying.
You do. All tests, frameworks, and pipeline configuration live in your repositories on open-source tooling, with documentation and training so your team can maintain them without us.
Project initiation within 48 hours
Round-the-clock support
Strict NDAs for data security
Share your project requirements.
Slightly slower start, as the framework is designed around your architecture first.
