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AI Functional Testing

Application discovery, requirements, test scenarios, execution, and evidence

Your application already has functionality — often more than anyone has fully documented. We use AI to discover it, turn it into requirements, and validate it with evidence.

QualiMatrix-Inspired Continuous Architecture

The 4-Stage Predictive AI-QA Cycle

How our AI QA Tool engine transforms application interactions into predictive risk scoring and self-healing test automation. Click each stage to inspect.

Stage 01 • Telemetry, DOM & Execution Logs

Data Processing

Collected runtime logs, telemetry, DOM mutations, and API traffic are sanitized, structured, and vectorized. Well-organized test data lays the technical groundwork for reliable model predictions and actionable QA decisions.

Capabilities & Logic

DOM structure and element state extraction
Network API request/response payloads sanitization
Historical execution telemetry & error stack indexing
Business entity lifecycle tracking across states
Data Ingestion Speed
< 120ms

Validated across continuous regression runs and multi-platform pipelines.

Execution Workflow

End-to-End Discovery & Execution

  1. Application URL + Objective

    You provide the application and the business objective for testing.

  2. AI Application Explorer

    AI walks through pages, forms, and workflows like a skilled tester.

  3. Functional Requirements

    Business rules, validations, and workflows are extracted and documented.

  4. AI Test Scenario Generator

    Risk-based scenarios are generated from what the application actually does.

  5. Playwright / API / SQL Execution

    Scenarios execute across the browser, API, and database layers.

  6. Evidence Collection

    Screenshots, traces, responses, and query results are captured.

  7. Defect Analysis

    Findings are compared against expected behavior and classified.

  8. AI QA Report

    A clear, evidence-backed report of coverage and defects is delivered.

Why it's different

Not generic AI testing

Understands behavior, not just markup

The AI explorer follows workflows the way a user would — filling forms, submitting, and observing outcomes — rather than only crawling links.

Business rules become documented requirements

Validation rules, calculations, and permissions discovered during exploration are written down, so your team gets a living functional baseline, not just test results.

Risk-based, not exhaustive-for-its-own-sake

Scenarios are prioritized by business risk and likelihood of defect, so effort goes where it matters most.

Evidence on every finding

Every reported defect ships with the screenshot, trace, API response, or database result that supports it.

See what AI-driven discovery finds in your application

Book a free QA assessment and get a first look at your functional coverage.