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About Contact Us
AI-first engineering

AI Test Automation Services.

AI test automation services. Improve UI, API, and regression coverage with reliable CI/CD testing, reviewed AI assistance, and Logiciel.

Get started

See Logiciel in action.

Tell us what you're building and we'll take it from there.

15+
Years building production software
120+
Engineers across delivery pods
75+
Clients served in North America
3K+
Successful product releases
Why Logiciel

Why AI Test Automation Needs More Than Generated Test Scripts.

Why Logiciel · 01

A larger test suite does not guarantee better release confidence when critical workflows remain uncovered.

Why Logiciel · 02

Generated test cases can look comprehensive while missing business rules, edge conditions, and the defects that matter most.

Why Logiciel · 03

AI can propose test cases and maintenance changes, but generated steps and assertions still need engineering review.

Why Logiciel · 04

UI changes, timing dependencies, test-data collisions, and unstable environments turn poorly designed automation into noise.

Why Logiciel · 05

API and end-to-end testing must verify permissions, business rules, integration failures, and expected system states, not only happy paths.

Why Logiciel · 06

CI/CD feedback loses value when flaky tests block releases and teams cannot quickly distinguish product defects from test failures.

Why Logiciel · 07

Teams need a reliable test signal, not more scripts that break whenever the UI, data, or environment changes.

What you get

What You Get From Logiciel AI Test Automation Services.

Our AI test automation services combine practical QA engineering with reviewed AI assistance for software teams shipping frequent releases.

01

Risk-led test coverage

mapping the highest-impact journeys, integrations, roles, failure paths, and change risks before adding automation

02

AI-assisted test design

using requirements, code changes, defect patterns, and workflow context to propose cases that engineers verify and refine

03

Maintainable UI and API automation

building stable selectors, reusable fixtures, deterministic assertions, and modular tests across supported interfaces

04

Faster regression feedback

placing the right smoke, API, integration, and end-to-end suites into pull requests and release pipelines

05

Reliable CI/CD test execution

managing test environments, data setup, parallel runs, retries, reporting, and meaningful release gates

06

Clear failure diagnosis

separating application defects from timing, data, infrastructure, and automation failures with actionable evidence

07

Human-reviewed quality controls

keeping engineers responsible for generated test logic, acceptance criteria, sensitive data, and consequential release decisions

Highlights

AI Test Automation Across Enterprise Workflows.

01

Web Application Regression

What it meansAutomate critical user journeys and use AI-assisted analysis to identify coverage gaps and maintain tests as interfaces change.
02

Mobile Application Testing

What it meansBuild repeatable mobile flows across supported devices with controlled data, stable assertions, and release feedback.
03

API and Integration Testing

What it meansValidate contracts, responses, permissions, failures, and downstream behavior across services and integrations.
04

End-to-End Workflow Testing

What it meansCover business-critical paths that cross authentication, data entry, approvals, payments, or connected systems.
05

AI-Assisted Test Case Design

What it meansUse requirements, defect history, and change context to propose scenarios that engineers review and turn into reliable tests.
06

Continuous Regression in CI/CD

What it meansRun risk-based suites in pull requests and release pipelines with clear pass/fail evidence and failure triage.
07

Flaky Test Reduction

What it meansIdentify unstable selectors, data coupling, timing issues, and environmental failures that erode trust in automation.
Engagement

AI Test Automation Engagement Models Built Around Your Team.

01

Dedicated AI Test Automation Squad

QA automation engineers, SDETs, and test leads work across coverage, frameworks, AI-assisted design, API and UI tests, CI/CD, and release quality.

02

Embedded QA Engineering Team

Automation specialists join your delivery squads to strengthen existing tests, reduce flakiness, improve coverage, and shorten feedback loops.

03

Targeted Automation Acceleration

A focused engagement stabilizes a brittle suite, automates a critical workflow, introduces AI-assisted design, or improves release pipeline quality.

Under the hood

AI Test Automation Services We Deliver.

01

Test Strategy and Risk Mapping

We identify release risks across web, mobile, API, and enterprise application releases, prioritize critical journeys, and define the assertions that prove each workflow works.

Included
02

AI-Assisted Scenario and Test Design

We use AI to suggest cases from requirements, defects, and change context, then review coverage, assumptions, and expected outcomes with engineers.

Included
03

Web, Mobile, and API Automation

We build reusable UI, API, integration, and end-to-end tests with stable selectors, deterministic checks, and maintainable frameworks.

Included
04

Test Data and Environment Engineering

We create approved fixtures, isolated environments, data setup and cleanup, mocks, and controlled integration dependencies.

Included
05

CI/CD and Continuous Regression

We integrate smoke, contract, API, integration, and end-to-end suites into delivery pipelines with risk-based gates and readable results.

Included
06

Flaky Test Diagnosis and Maintenance

We identify timing, selector, data, concurrency, and environment problems and fix their causes rather than hiding failures behind retries.

Included
07

Quality Reporting and Review Controls

We report coverage, defects, failure patterns, test stability, execution time, and review evidence for AI-assisted changes.

Included
Insights

AI Test Automation Insights & Frameworks.

01

Risk-to-Coverage Matrix

A framework that links business-critical workflows, permissions, integrations, failure modes, and recent changes to appropriate automated checks.

Insights
02

Test Signal Quality Model

A method for assessing flakiness, defect detection, runtime, triage effort, and trust in CI/CD results instead of counting scripts alone.

Insights
03

AI-Generated Test Review Gate

A checklist for validating generated scenarios, assertions, test data, sensitive information, false positives, and maintainability before merging.

Insights
How we work

Our AI Test Automation Framework.

01

Map Release Risks and Existing Coverage

We review user journeys, architecture, defects, test suites, release cadence, environments, and the workflows that cannot fail unnoticed.

02

Design the Automation Strategy

We choose test layers, tooling, fixtures, AI-assisted design boundaries, CI/CD gates, and measurable reliability targets.

03

Build and Stabilize the Tests

We implement reviewed scenarios, reusable automation, API checks, data controls, mocks, and integrations with the delivery pipeline.

04

Validate the Test Signal

We run representative failures and regressions, investigate flakiness, confirm assertions, and check that pipeline results are actionable.

05

Maintain Coverage as the Product Changes

We review failures, update tests with releases, track defect escape and suite stability, and use AI suggestions only after engineering validation.

Selected work

Tailored engineering for your industry.

Zeme · Real EstateCut development costs 50% and launched 3× faster with dedicated dev teams.
Real Estate

Cut development costs 50% and launched 3× faster with dedicated dev teams.

Leap · ConstructionScaled to 7-figure ARR with AI-augmented software teams.
Construction

Scaled to 7-figure ARR with AI-augmented software teams.

KW · Real Estate56M+ workflows automated, saving agents 30% time with AI-powered tasks.
Real Estate

56M+ workflows automated, saving agents 30% time with AI-powered tasks.

Resources

Insights for tech leaders.

Whitepaper · PDF

The Model Evaluation Report

Read more
Whitepaper · PDF

The Economics of Model Evaluation

Read more
Whitepaper · PDF

Benchmarking Model Evaluation

Read more
In their words

What our clients say.

Teams that needed to ship fast, and did. Here's what partnering with Logiciel felt like from the inside.

Patrick Fingles

I would highly recommend them to anyone looking to scale quickly or needing support in engineering, product, or QA.

Patrick Fingles
Patrick Fingles
CEO, Leap
Elior Alayev

We don't just call them Logiciel; they're part of the Zeme team. Within the first week they were contributing meaningfully to our codebase.

Elior Alayev
Elior Alayev
Founder & CEO, Zeme
David Buzzelli

The Logiciel team worked tirelessly and built everything we needed, with security and best practices across our entire platform. It allowed us to become #1 in our industry, and we couldn't have done it without them.

David Buzzelli
David Buzzelli
Co-Founder, JobProgress
Questions

Frequently asked questions.

What are AI test automation services?

AI test automation services combine conventional automated software testing with AI-assisted tasks such as scenario discovery, test drafting, maintenance suggestions, and failure analysis. Reliable execution still depends on engineered frameworks and reviewed assertions.

How is AI-powered test automation different from traditional automation?

Traditional automation runs explicit scripted checks. AI can help identify scenarios or suggest changes, but it does not remove the need for deterministic expected outcomes, stable test data, and engineering ownership.

Do AI-generated test cases need human review?

Yes. Engineers should verify business logic, assertions, data access, false positives, and maintainability before AI-generated tests become release gates.

Can you integrate tests with our existing CI/CD and tools?

Yes. We can work with existing repositories, test frameworks, issue tracking, and CI/CD systems where appropriate. Tool selection depends on the application stack, existing investment, and test reliability needs.

How do you reduce flaky automated tests?

We investigate unstable selectors, asynchronous behavior, shared test data, environment differences, timing, and external dependencies. Fixing the cause matters more than repeatedly rerunning failing tests.

Can AI replace software test engineers?

No. AI can assist with scenario discovery, test generation, maintenance suggestions, and failure analysis, but engineers still define risk, verify assertions, review generated tests, and own release decisions.

How do you measure whether AI test automation is working?

Useful measures include critical-path coverage, defect detection, escaped defects, suite stability, execution time, triage effort, and the speed of trustworthy release feedback, not just the number of generated tests.

Let's build

Build Test Automation Your Release Team Can Trust.

Show us your product, release process, or unstable suite. We’ll help prioritize coverage, review automation gaps, and define a practical path to reliable CI/CD feedback.