AI-QA Automation | Bits In Glass
Visual AI-Assisted Test Automation

Ship quality software faster

AI-QA Automation turns a written requirement into a working, self-maintaining test, without needing an army of automation engineers.

Start with a pilot, then own it outright.

Prove value on your own application, then scale on your terms and finish with the full solution and its IP transferred to you.

Phase 01License
Prove it on your app

Results in weeks, not quarters

A focused pilot against a real application of yours, scoped with your team and measured on your own release cycle.

Phase 02Embed
Roll out with your team

Your tools, your workflows

Integrate into your tickets, repos, and CI; upskill your QA analysts as you scale coverage across applications.

Phase 03Transfer
Own it outright

Full IP transfer, no lock-in

The full solution and its IP transferred to you, without permanent dependency on us to keep it running.

From requirement to working test in five AI-assisted stages.

01

Describe

Requirements automatically become structured test cases.

02

Capture

Point and click any element with minimal hand-coding of selectors.

03

Generate

Working test code in your framework and language.

04

Run

Execute suites with live logs and clear reports.

05

Maintain

Existing tests update incrementally, as the app evolves.

Put AI-QA Automation to work where coverage matters most.

Regression suites

Keep growing regression packs current as the product evolves — without the maintenance tax.

Smoke & sanity

Stand up fast confidence checks for every build and deployment.

Release certification

Traceable evidence from requirement to result — ready for sign-off.

Legacy app coverage

Bring untested or under-documented applications under automated coverage.

Automation your existing QA team owns outright.

60–70% faster
Script development
Manual scripting and locator work disappear, so coverage lands in-sprint instead of a release behind.
minimal coding
Built by your analysts
QA analysts author and maintain automation without becoming developers or waiting on engineers.
self-healing
Tests that survive change
Suites resist UI change and update incrementally, so maintenance stops eating your sprint.
fewer flakes
Greater run stability
Consistent, repeatable runs mean less triage noise and less release-day firefighting.
lower TCO
Cost of ownership
Costs scale downward — fewer specialist hires, less maintenance, and zero tool sprawl.
And it stays yours

Runs inside your own cloud, on your own model, with the full solution transferred to you at the end of the engagement.

Fits within the stack you already run

Connects to your tickets, repos, and continuous integration and generates the framework your engineers already use.

Book a 45-minute live walkthrough
JIRA Selenium Playwright Git Java · TypeScript AWS Marketplace

What makes this different from a generic AI assistant?

Generic AI assistant

Built to write code

  • Made to write code, not to test software
  • Regenerates from scratch on every change — nothing persists
  • No awareness of test structure, assertions or reporting
  • You re-prompt, copy, paste and stitch it together yourself
AI-QA Automation

Built for the QA lifecycle

  • Purpose-built for the full QA lifecycle
  • Updates existing tests incrementally — your asset is protected
  • Understands structure, assertions, execution and reporting
  • One connected toolchain — requirement to report

AI-QA Automation is not a coding assistant repurposed for testing.

Full lifecycle, one platform

Requirements to reporting in a single toolchain, not a stack of disconnected point tools.

Built for QA, not generic AI

Understands test structure, assertions and lifecycle, and keeps your test asset alive as the app changes.

Your model, your cloud

LLM-agnostic and bring-your-own-licence; deploys inside your environment and security boundary.

You own it

Full IP transfers to you at the end of the term, with no permanent vendor lock-in.

Use case challenge

Test automation breaks down the moment your app changes.

Most teams hit the same four walls — all at once.

Brittle

Hard-coded locators break

Every UI change snaps the suite. Teams spend their sprint fixing tests instead of writing them.

Slow

QA becomes the bottleneck

Hand-built tests and manual triage lag every sprint, and the release waits on coverage.

Specialist-bound

Automation needs scarce engineers

Coverage stalls behind headcount most QA teams cannot hire fast enough.

Costly

Effort climbs, scale doesn't

Specialist effort keeps rising and still doesn't spread across teams or applications.

Move your team from manual effort to engineered automation.

QA today

Manual effort, every sprint

  • Engineers hand-write and re-fix scripts every sprint
  • A UI change breaks the suite — and the firefighting begins
  • Automation stalls without scarce specialist headcount
  • QA becomes the bottleneck that holds up the release
With AI-QA Automation

Engineered automation you own

  • Analysts turn requirements into running tests with minimal coding
  • Tests self-heal through UI change instead of breaking
  • Your existing team scales coverage without new hires
  • QA keeps pace with development — in-sprint
Why it matters

Slow, fragile testing is a business problem, not just a QA one.

When quality can't keep pace with release, the cost lands across the business — on delivery timelines, IT budgets, the support desk, and the outcomes your sponsors track. AI-QA Automation addresses these concerns:

In IT & delivery

Head of Delivery

"Testing is where my releases slip."

Manual and brittle automation makes QA the critical-path bottleneck — release dates move because coverage can't be trusted in time.

Head of IT

"Quality is costing me more every year."

Scarce automation specialists, rising tooling spend and endless maintenance drive up the cost of quality — without improving it.

Head of Support / ITSM

"Escaped defects become my ticket queue."

Every bug that slips past testing turns into incidents, SLA pressure and lower CSAT — the release problem lands squarely on the service desk.

In the business

Product / Business Owner

"My roadmap keeps waiting on QA."

Revenue-driving features sit in the queue while testing catches up — every delayed release is a delayed business outcome.

Head of Customer Experience

"Defects in production cost us trust."

Bugs that reach customers dent satisfaction, retention and brand — the reputational bill for weak testing lands here.

Finance / Executive Sponsor

"I'm funding effort, not outcomes."

Spend climbs on specialists and rework while time-to-market slips — a poor return on the investment they signed off.

The through-line Faster, self-maintaining tests don't just help QA — they protect release dates, contain the cost of quality, keep defects out of the support queue, and speed the outcomes sponsors track.

Common questions teams ask us.

Which frameworks and languages do you support?

AI-QA Automation generates tests in the frameworks your engineers already use — including Playwright and Selenium, in Java or TypeScript — so the output fits straight into your existing CI.

Where does it run, and is our data safe?

It deploys inside your own cloud and security boundary, using your own model — your code and data stay within your environment.

Do we need automation engineers to use it?

No. It's built so QA analysts can author and maintain automation with minimal coding — your engineers stay free for higher-value work.

Who owns the solution and the tests?

You do. The tests are yours throughout, and the full solution and its IP transfer to you at the end of the engagement.

How do we get started?

With a short pilot on one of your own applications. Book a walkthrough and we'll scope it with you.

How long until we see results?

Most teams see running, self-maintaining tests within the first few weeks of the pilot — measured on a real application of your own.

See it on your own application.

Book a 45-minute live walkthrough — we'll capture an element and generate a running test against your app.

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