Should we automate everything?

No. Automate what is repetitive, stable, and high risk; keep exploratory testing human. Suites that chase 100% coverage tend to become the most expensive thing in the pipeline to maintain.

Our automated tests are flaky and ignored. Can that be recovered?

Usually yes. We quarantine and fix the unreliable tests, address the timing and test-data causes underneath, and reintroduce them as blocking only once they are trustworthy again.

How quickly can an engagement start?

Discovery usually begins within one to two weeks of scope agreement, and a delivery team is typically mobilised within three to four weeks depending on the skills involved.

Can you work alongside our in-house team?

Yes — most of our engagements are blended. We work in your repositories, ceremonies, and tooling, and we plan for knowledge transfer from the start so capability stays with you.

How are engagements priced?

Fixed price for well-bounded projects, monthly capacity for dedicated pods, and outcome-linked SLAs for managed services. We are explicit about assumptions and what would change the number.

NeonAITech services — AI and data engineering, product engineering, cloud and DevOps, managed operations, security and quality

Continuous Quality Engineering

Quality as a pipeline property, not a phase before release.

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70%

Shorter regression cycle

80%+

Defects caught before UAT

WCAG 2.2 AA

Accessibility conformance

Overview

What Continuous Quality Engineering means at NeonAITech

We embed quality engineering into delivery — risk-based test strategy, automated functional, performance, accessibility, and security testing, and AI-assisted test generation and maintenance — so releases get faster and safer at the same time rather than trading one for the other.

Tools & Platforms

Playwright
k6
Pact
Axe
Cypress
GitHub Actions

We are not tied to a single vendor — the stack follows the problem, your existing estate, and your team’s skills.

What We Deliver

Test Strategy & Automation Framework

A risk-based strategy and maintainable automation framework across unit, API, contract, and end-to-end layers.

Performance & Resilience Testing

Load, stress, and soak testing that establishes real capacity limits before peak trading proves them for you.

Accessibility & Compliance Testing

WCAG 2.2 conformance testing combining automated checks with assistive-technology validation.

Capabilities

Inside the Engagement

AI-assisted test generation and self-healing suites

Playwright, Cypress, and Selenium automation

Contract testing for services and APIs

Test data management and masking

Quality gates and release-readiness reporting

How We Work

A delivery rhythm you can see into

Every Continuous Quality Engineering engagement runs the same four phases, with AI used wherever it removes effort rather than adds novelty.

01
Discover
We map the current state, agree the outcome, and size the work — so scope is a shared decision, not a surprise.
02
Design
Architecture, delivery plan, and success measures are set before build, with costed options where trade-offs exist.
03
Build
Short increments with working output you can review, steer, and stop — never a black box until go-live.
04
Operate
We measure against the agreed outcomes, hand over documentation, and stay on for support where you want it.

Engagement Models

Buy it the way that fits

Fixed-Scope Project

A defined outcome, timeline, and price. Best when requirements are clear and the deliverable is well bounded.

Dedicated Pod

A cross-functional team working to your backlog and priorities, scaling up or down with a month’s notice.

Managed Service

Ongoing ownership against agreed SLAs, with a share of capacity reserved for continuous improvement.