How Tezeract Built an AI-Powered Test Case Generation Platform That Automates SQA Workflows and Delivers Accurate Test Cases in Under 5 Minutes

Impact

Project Overview

Software testing has always been one of the most time-intensive phases in the software development lifecycle. QA teams spend hours reading requirement documents, analyzing web applications, and manually writing test cases, work that is repetitive, error-prone, and difficult to scale.

Tshabok was built to eliminate that bottleneck. A Saudi Arabia-based SQA professional approached Tezeract with a clear goal: build an AI-powered test case generation platform that could read a requirements document, parse a Jira ticket, or crawl a live website, and produce structured, meaningful test cases automatically, in minutes instead of hours.

Tezeract designed and built the full platform from scratch, the LLM-based analysis engine, the custom web scraper, the Jira integration, the admin panel, payment gateway, Arabic language support, and the invoice generation system, across three major versions delivered over approximately one year.

What Changed

QA teams no longer spend hours manually writing test cases from requirement documents or analyzing websites feature by feature. Tshabok generates structured, accurate test cases from any input source – a document, a Jira requirement, or a website URL – in under five minutes, with results that surpass manual tester efforts in consistency and coverage.

Customer Profile

Why This Matters for Buyers Like You

If you are running a QA team, managing software releases, or building a testing product, the challenge Maher faced is universal. Manual test case writing does not scale. Requirements change faster than testers can keep up. And the gap between what a document describes and what a tester actually covers is where bugs slip through.

An automated test case generation tool built on AI closes that gap, consistently, at speed, and without adding headcount.

Turning Hours of Manual Test Case Writing Into Minutes of Automated, Accurate Output

Primary Problem

The core issue was speed and accuracy in test case creation. QA teams were reading through requirement documents line by line, manually analyzing web application features screen by screen, and writing test cases by hand. This process was time-draining, inconsistent across testers, and still produced gaps in coverage. A single requirements document could take hours to convert into a complete test suite, and the results varied depending on who wrote them.

What was needed was an AI-powered SQA platform that could understand requirements the way an experienced tester does, but produce structured test cases faster, more consistently, and with broader coverage than any manual effort.

Secondary Challenges

No standardized output

Different testers produced test cases in different formats, making it difficult to maintain consistency across projects and teams.

Website analysis was entirely manual

Evaluating a web application’s features, user flows, and edge cases required a tester to click through every screen, which could take days for complex applications.

Jira workflows were disconnected

Requirements lived in Jira, but test cases were written in separate documents. There was no automated bridge between the two.

Scaling required hiring

Every new project or client meant more manual testers. There was no way to increase output without increasing headcount.

Accuracy under pressure

Tight deadlines led to rushed test cases with missed scenarios, especially for edge cases and negative testing paths.

From Manual QA to AI Test Case Generation

See how Tshabok replaces slow, manual test case writing with fast, structured AI output built for real QA workflows. Explore how your team can reduce effort and improve coverage in minutes.

What Slowed Down Operations and Triggered the Need for Immediate Change

Previous Solutions Tried

Before Tshabok, the team relied entirely on manual testing processes, reading documents, analyzing applications by hand, and writing test cases from scratch. There were no off-the-shelf tools capable of delivering the level of accuracy and contextual understanding required for meaningful, automated test case generation from diverse input sources.

Business Impact

Without automation, the business model had a hard ceiling. Every new client required a proportional amount of manual effort. Test case quality was inconsistent, turnaround times were slow, and the team could not compete on speed with larger QA firms. The inability to scale output without scaling headcount was the primary constraint on growth.

Urgency Factors

Why Tezeract

Maher needed a technical partner with proven expertise in AI and automation, not a generic development shop. The product required custom LLM integration, a purpose-built web scraper, and a deep understanding of how QA workflows actually function. Off-the-shelf solutions could not deliver the contextual accuracy Tshabok demanded.

Before committing, Maher evaluated the landscape and landed on Tezeract because of our AI automation services track record and our ability to handle both the AI engine and the full-stack product build under one roof.

Why Tezeract Won the Evaluation

Tezeract’s expertise in the AI and automation sector was the deciding factor. The team demonstrated a clear understanding of LLM-based software testing, custom scraping pipelines, and how to build a product that a non-technical founder could manage and grow.

A structured delivery process, clear communication, and willingness to iterate based on Maher’s deep SQA domain knowledge made the partnership productive from day one.

Tshabok, An AI-Powered Test Case Generation Platform Built for Speed, Accuracy, and Scale

Tshabok is a fully custom AI-powered SQA platform built around one principle: the fastest path to reliable test cases is to let AI read, analyze, and generate, while the tester reviews, refines, and approves.

The platform accepts three input types:

Requirement documents (RGDs)
Jira tickets
Live website URLs

And produces structured, meaningful test cases that cover functional, edge-case, and negative testing scenarios. The AI does not just extract keywords; it understands the context of each requirement and generates test cases that an experienced QA engineer would recognize as thorough and actionable.

How Nava AI Works

A user uploads a requirements document, connects a Jira project, or enters a website URL. The AI engine processes the input – parsing document structure, extracting Jira requirements, or crawling and analyzing website features using a custom-built scraper powered by Playwright.

The LLM then generates structured test cases mapped to each requirement or feature, displayed on the user’s dashboard, and ready for export.

Using LLM-based software testing and web scraping for QA automation, the platform delivers:

Document analysis: Upload an RGD and receive complete test cases in under a minute
Jira integration: Import requirements directly from Jira, generate test cases, and export them back – all within the platform
Website analysis: Enter a URL and the custom scraper parses the site’s features, user flows, and interface elements, then the LLM generates test cases covering the full application, in under five minutes

Key Capabilities Built

AI test case generation from documents

LLM reads and interprets requirement documents, producing structured test cases with steps, expected results, and priority levels

Website analysis test case generator

Custom Playwright-based scraper crawls any web application, extracts features and UI elements, and feeds them to the AI for comprehensive test case generation

Jira bi-directional sync

Import requirements from Jira, generate test cases within Tshabok, and push completed test cases back to Jira without leaving the platform

Super Admin Panel

Centralized management dashboard for user oversight, system configuration, and activity monitoring

Payment gateway integration

Moyasar integration for secure, streamlined transactions supporting both subscription and pay-as-you-go models

Arabic language support

Full localization including language selection, RTL layout adjustments, and multilingual support across all key modules

Custom invoice generation

Professional, branded invoices generated automatically for every transaction

See How AI Improves QA Coverage

Tshabok helps teams catch more edge cases by generating structured test cases from real inputs like requirements and live websites. Explore how it improves coverage without extra hiring.

Phases wise Deployment

Tezeract delivered Tshabok across three major versions over approximately one year, with each phase building on validated functionality and real user feedback.

Phase 1 – Version 1.0: Core AI Engine and Dual Workflows

Built the two foundational workflows: Document Analysis (extracting requirements from uploaded documents to generate test cases) and Web Application Analysis (automatically crawling websites and generating structured test cases). This phase established the core AI test case generation engine, the custom web scraping pipeline using Playwright, and the user-facing dashboard.

Key milestone: Both document-based and website-based test case generation workflows live and validated with beta users.

Phase 2 – Version 1.2: Monetization and Admin Control

Extended the platform with Payment Gateway Integration (Moyasar) for secure subscription and pay-as-you-go transactions, a Super Admin Panel for centralized user and system management, and Custom Invoice Generation for professional, automated billing.

Key milestone: Platform commercially ready with payment processing, admin oversight, and professional invoicing in place.

Phase 3 – Version 1.3: Localization and Multilingual Support

Added full Arabic language support across the entire platform, language selection, RTL layout adjustments, and multilingual support for all key modules. This was critical for the Saudi Arabia market and regional expansion.

Key milestone: Platform fully localized for Arabic-speaking users. Version 3 delivered on 13th February 2026.

Obstacles Countered and Resolved

Obstacles

Auto-analyzing features of any uploaded website accurately

Generating test cases that surpass manual tester quality

Handling diverse document formats and structures

Scaling for concurrent users without degrading performance

Supporting RTL layouts and Arabic across all modules

Resolution

Built a custom Playwright-based scraper that parses website structure, UI elements, and user flows regardless of the site’s tech stack or complexity

Iteratively tuned the LLM prompts and scoring logic using Maher’s SQA expertise as the benchmark, ensuring generated test cases covered edge cases and negative scenarios consistently

Built flexible document parsing that extracts requirements from varied RGD formats without requiring a fixed template

Implemented Celery-based request queuing to handle larger user bases efficiently and maintain response times under load

Designed the frontend architecture with localization in mind from the start, enabling full Arabic support without a major refactor

The Results

Tshabok launched as a fully functional AI-powered SQA platform, transforming what was once hours of manual work into minutes of automated, accurate output. The impact was measurable across speed, accuracy, and scalability.

Stakeholder Impact

For QA Engineers

Generate complete test cases from a requirements document in under a minute – no manual writing required

Get structured test cases from a live website in under five minutes, covering features a manual review might miss

Export test cases directly to Jira without switching tools or reformatting

Consistent output quality regardless of deadline pressure or project complexity

For QA Team Leads

Standardized test case format across all projects and team members

Faster turnaround on test planning, freeing the team to focus on execution and exploratory testing

Clear visibility into test coverage per requirement or feature

Reduced onboarding time for new testers, the platform handles the heavy lifting

For Software Companies and Development Teams

Scale QA output without scaling headcount

Faster release cycles with comprehensive test coverage built into the workflow

Reduced risk of bugs reaching production due to missed test scenarios

A business process automation layer that integrates directly into existing Jira-based development workflows

For the Business (Tshabok)

A scalable SaaS product with subscription and pay-as-you-go revenue streams

Competitive differentiation through AI accuracy that surpasses manual testing efforts

Regional market advantage with full Arabic language support

A platform that grows with the user base through queue-based architecture

Build a Scalable QA Process With AI

Tshabok is built for teams that want to scale QA without scaling headcount. Learn how AI-driven test case generation fits into modern software delivery workflows.

Building Tshabok With a Purpose-Built AI and Automation Tech Stack

NodeJS

React

Playwright

Celery

Jira API

Moyasar

Google Cloud Platform (GCP)

VS Code

Layer

Tools

Role

Frontend

React

Responsive, user-friendly web application interface with RTL/Arabic support

Backend

Node.js

Scalable server-side architecture handling API logic, user management, and integrations

AI Server

LLM (Large Language Model)

Core AI engine for understanding requirements and generating structured test cases

Web Scraping

Playwright

Custom-built scraper for crawling and analyzing website features, UI elements, and user flows

Task Queue

Celery

Asynchronous request queuing for handling concurrent users and large-scale processing efficiently

Integrations

Jira API

Bi-directional sync for importing requirements and exporting generated test cases

Payment Gateway

Moyasar

Secure payment processing for subscription and pay-as-you-go billing

Cloud Infrastructure

Google Cloud Platform (GCP)

Scalable, reliable cloud deployment and hosting

Development Tools

VS Code

Development, debugging, and version control

Key Features

AI-Powered Document Analysis

Upload any requirements document and receive structured, comprehensive test cases in under a minute. The LLM reads, interprets, and maps each requirement to actionable test scenarios – covering functional, edge-case, and negative testing paths that manual testers often miss under time pressure.

Website Analysis Test Case Generator

Enter any website URL, and the custom Playwright-based scraper crawls the application, extracts features, UI elements, and user flows, then feeds everything to the AI engine. Structured test cases are generated in under five minutes, no manual feature-by-feature analysis required.

Jira Bi-Directional Integration

Import requirements directly from Jira, generate test cases within Tshabok, and push completed test cases back to Jira, all without leaving the platform. This closes the gap between requirements management and test planning, which slows down most QA teams.

Super Admin Panel and Billing

Centralized management dashboard for user oversight, system configuration, and activity monitoring. Integrated Moyasar payment gateway supports both subscription and pay-as-you-go models, with custom professional invoices generated automatically.

How automated test case generation tool Helps

Accelerating QA for Software Development Agencies

Development agencies managing multiple client projects can use Tshabok to generate test cases across all active projects simultaneously, reducing QA bottleneck and delivering faster releases without compromising test coverage.

Automating Regression Test Case Updates

When requirements change, Tshabok can regenerate test cases from updated documents or Jira tickets, ensuring regression suites stay current without manual rework, a critical capability for teams practicing continuous delivery.

Website QA Audits for New Client Onboarding

QA consultancies can use the website analysis test case generator to rapidly audit a new client’s web application, producing a comprehensive test suite as part of the initial engagement, demonstrating value from day one.

Scaling QA for Startups Without Scaling Teams

Early-stage companies with small teams can leverage Tshabok to maintain enterprise-grade test coverage without hiring dedicated QA staff, keeping quality high while controlling costs during growth phases.

Bridging the Gap Between Business Analysts and QA Teams

Business analysts write requirements; QA teams write test cases. Tshabok automates the translation, ensuring nothing gets lost between the requirement document and the test plan – and freeing both teams to focus on higher-value work. This is where AI agent development meets practical QA workflow automation.

Build Your Own AI-Powered SQA Platform With Tezeract

Tshabok proves what becomes possible when AI test case generation is built around real QA workflows, not retrofitted from a generic tool. Accurate test cases from documents in under a minute. Full website analysis in under five minutes. Jira integration that keeps your workflow intact.

Whether you are building a QA automation product, looking to embed AI into your testing pipeline, or need a custom AI development partner who understands both the AI and the SQA domain, Tezeract can design and build it. We do not adapt templates. We build for your workflows, your users, and your scale targets.

Ready to automate your test case generation with AI? Let’s talk.

Frequently Asked Questions

What is AI-powered test case generation and how does it work?

AI-powered test case generation uses large language models to read requirement documents, Jira tickets, or live websites and automatically produce structured test cases. The AI understands the context of each requirement – not just keywords – and generates test scenarios covering functional, edge-case, and negative paths. The result is faster, more consistent test coverage than manual writing.

Can an automated test case generation tool replace manual QA testers?

No. An automated test case generation tool handles the repetitive, time-intensive work of writing test cases from requirements. This frees QA engineers to focus on exploratory testing, edge-case investigation, and test execution, the work that requires human judgment and creativity.

How does a website analysis test case generator work?

A website analysis test case generator uses a custom web scraper to crawl a web application, extract its features, UI elements, and user flows, then feeds that data to an AI engine that generates structured test cases. Tshabok uses Playwright for scraping and an LLM for test case generation, delivering results in under five minutes for most websites.

What is LLM-based software testing?

LLM-based software testing refers to using large language models – like GPT, to understand software requirements and generate test artifacts. Unlike rule-based tools, LLMs can interpret natural language requirements, understand context, and produce test cases that reflect how an experienced tester would approach the same document.

How accurate are AI-generated test cases compared to manually written ones?

When properly tuned, AI-generated test cases match or exceed manual quality in coverage and consistency. Tshabok was iteratively refined using feedback from an experienced SQA professional, ensuring the output covers scenarios that manual testers often miss under deadline pressure – particularly edge cases and negative testing paths.

Can Tshabok integrate with Jira for test case management?

Yes. Tshabok offers bi-directional Jira integration. You can import requirements directly from Jira, generate test cases within the platform, and export completed test cases back to Jira, keeping your existing workflow intact without switching tools or reformatting output.

What types of input does Tshabok accept for generating test cases?

Tshabok accepts three input types: requirement documents (RGDs) for document-based analysis, Jira tickets for requirement-to-test-case automation, and live website URLs for automated web application analysis. Each input path produces structured, exporable test cases.

How is web scraping used in QA automation?

Web scraping for QA automation involves programmatically crawling a web application to extract its structure, features, and UI elements. This data is then used to generate test cases automatically, eliminating the need for a tester to manually click through every screen. Tshabok uses a custom Playwright-based scraper purpose-built for this workflow.

How Tezeract Built an AI-Powered Test Case Generation Platform That Automates SQA Workflows and Delivers Accurate Test Cases in Under 5 Minutes

Impact

85%

70%

5 Min

Project Overview

What Changed

Customer Profile

Why This Matters for Buyers Like You

Turning Hours of Manual Test Case Writing Into Minutes of Automated, Accurate Output

Primary Problem

Secondary Challenges

No standardized output

Website analysis was entirely manual

Jira workflows were disconnected

Scaling required hiring

Accuracy under pressure

From Manual QA to AI Test Case Generation

What Slowed Down Operations and Triggered the Need for Immediate Change

Previous Solutions Tried

Business Impact

Urgency Factors

Why Tezeract

Why Tezeract Won the Evaluation

Tshabok, An AI-Powered Test Case Generation Platform Built for Speed, Accuracy, and Scale

How Nava AI Works

Key Capabilities Built

AI test case generation from documents

Website analysis test case generator

Jira bi-directional sync

Super Admin Panel

Payment gateway integration

Arabic language support

Custom invoice generation

See How AI Improves QA Coverage

Phases wise Deployment

Phase 1 – Version 1.0: Core AI Engine and Dual Workflows

Phase 2 – Version 1.2: Monetization and Admin Control

Phase 3 – Version 1.3: Localization and Multilingual Support

Obstacles Countered and Resolved

Obstacles

Resolution

The Results

85%

70%

Under 5 Min

Stakeholder Impact

For QA Engineers

For QA Team Leads

For Software Companies and Development Teams

For the Business (Tshabok)

Build a Scalable QA Process With AI

Building Tshabok With a Purpose-Built AI and Automation Tech Stack

Layer

Tools

Role

Development Tools

VS Code

Key Features

AI-Powered Document Analysis

Website Analysis Test Case Generator

Jira Bi-Directional Integration

Super Admin Panel and Billing

How automated test case generation tool Helps

Accelerating QA for Software Development Agencies

Automating Regression Test Case Updates

Website QA Audits for New Client Onboarding

Scaling QA for Startups Without Scaling Teams

Bridging the Gap Between Business Analysts and QA Teams

Build Your Own AI-Powered SQA Platform With Tezeract

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