Kindlebit
Product Engineering and QAProposed offering

Cut the time spent writing, maintaining and investigating tests, without handing quality to a black box.

We apply AI where it saves testers time: generating test ideas and code from requirements, prioritising by risk, clustering failures and repairing scripts, with human review and measurement of every gain.

Review your QA workflow for AI fitSee the workflow

This is a service Kindlebit proposes to deliver. No named customer project is published for it on this site.

AI assisted QA loop

  1. Requirements
  2. Generate
  3. Review
  4. Execute
  5. Analyse
  6. Learn

Reference design. Components are options, not a statement of what is deployed at any customer.

Problems this service is built to solve

These are situations we expect buyers to recognise. Each one states why it happens, what it costs and how we would approach it.

Slow test creation

A new epic arrives with twenty user stories. Writing test cases and automation for all of them takes two sprints, so testing trails development.

Why it happens
Test design is manual, and each case starts from a blank page.
What it costs
Late testing and coverage gaps on new features.
How we approach it
We use models to propose scenarios, edge cases and draft automation from stories and specifications. Testers review, edit and approve, and we measure acceptance rate and defects found.
What to measure
Time to first test set, share of generated tests accepted and defects found by generated tests.

High automation maintenance

After a redesign, hundreds of UI tests need new locators. The team spends a week repairing them instead of testing.

Why it happens
Selectors and flows are coupled to the interface.
What it costs
Maintenance eats the saving that automation was meant to deliver.
How we approach it
We apply self healing locators and AI assisted repair proposals that go through review, along with design changes that make tests less brittle.
What to measure
Maintenance hours per release, repair acceptance rate and tests broken per release.

Poor risk prioritisation

The full test pack takes eight hours, so only part of it runs before each release, chosen by habit.

Why it happens
There is no data on which tests find defects for which changes.
What it costs
Wasted execution time and gaps in the right places.
How we approach it
We use change data, defect history and test results to rank which tests to run first for a given change, and validate the selection against full runs.
What to measure
Defects caught by the prioritised subset against full runs, and execution time saved.

Lengthy defect investigation

Overnight, ninety tests fail. Engineers spend the morning working out that sixty share one root cause in a login service change.

Why it happens
Failures are reported individually without grouping or hints.
What it costs
Slow diagnosis and delayed fixes.
How we approach it
We cluster failures by signature, correlate with recent changes and logs, and propose likely causes with evidence for an engineer to confirm.
What to measure
Time to triage, cluster accuracy and share of failures with a correct suggested cause.

Solutions we engineer

Concrete capabilities, each with the need it serves, how it integrates, what you receive and the value to expect.

AI assisted test design

Scenario, boundary and negative case suggestions from requirements and specifications, mapped to risk.

Customer need
Faster, broader test ideas.
Integration
Jira and test management.
Deliverable
Draft cases for review.
Business value
Quicker coverage.

Test code generation

Draft Playwright or API tests from flows and specs, following your framework standards and passing review.

Customer need
Speed up automation authoring.
Integration
Your repositories and CI.
Deliverable
Reviewed automation code.
Business value
Less typing, same standards.

Risk based test selection

Models using change impact and history to prioritise suites.

Customer need
Run the right tests first.
Integration
CI and version control.
Deliverable
Selection service.
Business value
Faster feedback.

Failure analysis and triage

Grouping, log and trace summaries and links to recent changes.

Customer need
Diagnose quickly.
Integration
CI logs and observability.
Deliverable
Triage assistant.
Business value
Shorter investigation.

Test maintenance assistance

Locator repair suggestions and flaky test diagnosis with human approval.

Customer need
Reduce brittleness cost.
Integration
Test repositories.
Deliverable
Repair workflow.
Business value
Lower upkeep.

Test data generation

Synthetic data generation respecting formats and constraints, without production personal data.

Customer need
Realistic, safe data.
Integration
Databases and APIs.
Deliverable
Data generators.
Business value
Better coverage and privacy.

Governance and measurement

Policies for code and data sent to models, review requirements and metrics on acceptance and defect yield.

Customer need
Prove AI helps and stays safe.
Integration
Security and engineering leadership.
Deliverable
Policy and dashboard.
Business value
Trust and evidence.

How we solve it

A delivery sequence built around coverage, execution, defect analysis and release criteria.

Baseline current effort

Measure time spent on test design, automation, maintenance and triage, so improvement can be shown.

Select use cases

Choose where AI is most likely to help, and decide what data may be shared with models.

Choose models and tools

Compare options on your artefacts for quality, privacy and cost, including on premises choices where needed.

Build prompts, guardrails and integration

Create templates aligned to your framework, add review steps and integrate with the tracker and repository.

Pilot with a team

Run on one product, record acceptance and defect yield, and collect tester feedback.

Evaluate and adjust

Compare against baseline, tune prompts and remove use cases that do not pay back.

Roll out with governance

Document the policy, train testers and set review requirements.

Monitor

Track acceptance rates, defects found, time saved and model cost, and review quarterly.

Solution in action: Generating and triaging tests for a new epic

Solution Concept An illustrative scenario. It describes how we would structure the work, not a delivered customer project.

Starting problem

A feature epic arrives with detailed stories and a tight sprint.

Existing workflow

Testers write cases manually and triage overnight failures one by one.

Improved workflow

The assistant drafts scenarios and API test code from the stories, flags missing acceptance criteria and edge cases, and testers review and approve them. The next morning, failures are clustered by signature, and a likely cause with log excerpts is attached to each cluster.

A new epic is ready for testing.

Systems involved

Issue tracker, repository, CI, test management, model gateway.

Data movement

Stories, specifications, test results and logs, with sensitive data excluded.

Human decisions

Testers approve generated tests and confirm suggested causes.

Automation opportunities

Draft generation, clustering and notification.

Exception handling

Generated tests that fail review are rejected with feedback. Low confidence clusters stay ungrouped.

Resulting user experience

Testers spend time judging and exploring, not typing and sifting logs.

KPIs to evaluate

  • Time to first test set
  • Generated test acceptance
  • Triage time
  • Maintenance hours

What you receive

Concrete deliverables for this service, written so you can check them against the contract.

  • Baseline measurement of QA effort
  • AI use case selection and policy for data
  • Prompt templates and guardrails aligned to your framework
  • Integration with tracker, repository and CI
  • Pilot results compared with baseline
  • Failure clustering and triage tooling
  • Governance and review guidelines
  • Training for testers and engineers

Technology and engineering

Options we would evaluate for this service. Unless a group is marked as publicly listed on kindlebit.com, treat each tool as a proposed implementation option. Naming a tool does not imply a vendor partnership.

Models (proposed implementation options)

  • OpenAI compatible APIs
  • Anthropic
  • Azure OpenAI
  • Self hosted open models

Testing stack

  • Playwright
  • Cypress
  • Appium
  • Postman
  • k6

Platform

  • GitHub Actions
  • GitLab CI
  • Jira
  • TestRail

Analysis

  • Log and trace aggregation
  • Embeddings based clustering

Relevant Kindlebit work and evidence

We use the strongest evidence available and say which kind it is.

Proposed offering

Evidence status for this service

This is a service Kindlebit proposes to deliver. No named customer project is published for it on this site.

See case study status
Solution ConceptInteractive demo with simulated data

AI assisted test generation and failure clustering in the quality platform

The quality engineering demonstration includes a failure clustering view with simulated results. It does not call a live model.

Open the demonstration

Business outcomes and success criteria

These are the measures we would agree before work starts. They are criteria for success, not results from past engagements.

Time to coverage

Hours from story to reviewed tests.

Maintenance effort

Hours per release spent repairing tests.

Triage time

Minutes from failure to cause identified.

Quality of output

Acceptance rate and defects found by generated tests.

Questions buyers ask

Will AI replace our testers?

No. It reduces typing and sifting so testers can spend time on judgement, exploration and risk. Review by people stays mandatory for what enters the suite.

Is our code safe with AI tools?

We set a policy on what may be sent to models, use providers and settings that match your terms, and can use self hosted models where needed.

How do you know it works?

We measure against a baseline: time to create tests, acceptance rate, defects found, maintenance hours and triage time.

Which parts are most valuable?

Often failure clustering and test maintenance, followed by test idea generation. The pilot shows what works in your setting.

Do generated tests have quality issues?

Sometimes. That is why we review, enforce framework standards, and track the share accepted and the defects they find.

Can this work with our tools?

Usually, through APIs and repository integration. We check your tracker and CI early.

Related services

Industries where this applies

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