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Multi-Engine AI Synthesis & Autonomous QA Platform
Autonomous synthesis engine tailored for enterprise domains requiring zero flaky assertions and high regulatory rigor.
Validates transactional invariants, SEPA instant payments, ISO 20022 message schemas, two-factor OTP verification, balance conservation rules, and ledger idempotency.
Dynamic synthetic data generation with zero Personally Identifiable Information (PII) exposure. Guaranteed HIPAA & GDPR compliance during test pipeline execution.
K6 load modeling with parameterized virtual users (VUs), ramp-up stages, chaos fault injection (latency, 500 status rates), and checkout conversion resilience.
Automated WCAG 2.2 Level AA accessibility test generator with interactive locators, contrast ratios, sovereign air-gapped execution, and local GGUF models.
Our Model-as-a-Service architecture ensures customer source code, business schemas, and credentials never persist on AI infrastructure.
All model inferences execute strictly within transient, ephemeral session memory. No request payload, OpenAPI schema, or test plan is ever written to disk on AI inference nodes.
Models (Granite-8B LoRA, Phi-4, local Ollama endpoints) are strictly frozen. We never train, fine-tune, or adapt foundational weights using user data, API payloads, or telemetry.
Upon pipeline completion or session termination, all context tokens are immediately purged. The platform retains only artifacts explicitly exported or saved locally by the user.
Seamless integrations extending test synthesis into your daily IDE workflow and browser inspection.
Direct IDE companion extension for bidirectional schema sync, AST diffing, and inline Playwright execution.
Chrome Manifest V3 extension for one-click live DOM capture, network recording, and instant test suite synthesis.
Architecture walkthroughs, multi-framework test generation masterclasses, and chaos testing workshops.
Adhering to international cybersecurity, privacy, and accessibility standards for enterprise deployments.
Full data sovereignty. Synthetic payloads mask real user identities; zero telemetry or PII leaks across third-party networks.
Automated accessibility validation including color contrast ratios, minimum target touch sizes (24x24px), focus states, and WAI-ARIA role hierarchy.
Built-in provenance and transparency watermarking for AI-synthesized test files, ensuring full auditability and regulatory compliance.
The Brain model has analyzed your endpoints and proposed the following scenarios. Deselect any scenarios you don't want to generate code for.
The AI Validator detected a critical issue and the Code Healer has automatically applied a fix. Review the diff below.
Provide instructions for the Code Healer to fix or modify the generated code.
Upload a custom accessibility policy definition (.json, .yaml, .txt) defining required WCAG tags, color contrast ratios, or excluded locators.
Select any global language or enter a custom target dialect. LLM will synthesize localized aria-label, aria-describedby, and semantic announcements respecting WCAG 2.5.3 (Label in Name).
Select any global language or enter a custom target dialect. Cognitive AST router will synthesize localized test step annotations, assertions, and BDD phrases.
Push the generated test suite directly to a repository branch and open a PR.
File an automated defect ticket with recorded DOM events & visual screenshots attached.
Bidirectional synchronization with Confluence Cloud: ingest specs & test data tables, sync corporate policies, and persist crawler discovery & WCAG audits as long-term memory.