Catch chatbot regressions before release
Catch chatbot regressions before release with Argus AI: a practical, on-prem approach built for Azerbaijani teams.
Prevent Chatbot Regressions Before Release
Ensure your AI assistants remain reliable, safe, and performant with a specialized self-hosted testing platform. As one of the three core engines of Argus, this system shares its runtime, model layer, credential store, and cost ledger with the QA and pentest engines to provide a unified testing ecosystem. By simulating thousands of diverse Azerbaijani synthetic users and adversarial personas, the platform identifies critical regressions and vulnerabilities before they ever reach your end customers. The engine operates on a black-box principle, interacting with your assistant through the same channels your users do—whether via REST, Dify, Kommunicate, or browser automation. By deriving expected behaviors from your uploaded knowledge and policy documents, the system generates a comprehensive readiness score and write-once assurance records. This allows teams to move from guesswork to data-driven validation, ensuring that every update maintains the high standards of accuracy and safety required for production.
Advantages of Argus Chatbot Testing
Eliminate recurring bugs using dedicated regression suites that confirm past issues stay permanently fixed.
Scale quality assurance by simulating thousands of realistic Azerbaijani users with distinct roles, goals, and behaviors.
Stress-test resilience using adversarial personas designed to trigger failures through frustration, manipulation, and prompt-injection.
Ensure linguistic accuracy with a native Azerbaijani LLM judge that evaluates tone, formality, and compliance.
Maintain total data sovereignty and security through a fully self-hosted, on-premises deployment architecture.
Generate immutable, write-once assurance records and readiness scores for every testing cycle.
Core Testing Capabilities
Synthetic User Generation
Generates thousands of users with specific roles, goals, language styles, knowledge levels, and behaviors to mirror real-world usage.
Adversarial Personas
Tests resilience against frustration, contradiction, manipulation, and AZ↔RU code-switching to find edge cases that break assistants.
Native LLM Judge
An Azerbaijani-native LLM scores the assistant on accuracy, tone, formality, compliance, and safety.
Black-Box Testing
Connects via REST, Dify, Kommunicate, or browser automation to test exactly what the end customer experiences.
Policy-Driven Expectations
Derives expected behaviors from uploaded knowledge and policy documents, allowing human overrides for final verdicts.
The Testing Workflow
Frequently Asked Questions
How does the platform handle system load during testing?
Per-assistant concurrency is strictly bounded to ensure that the testing process does not inadvertently become a denial-of-service attack on the assistant under test.
Can I change the evaluator model after a test run is finished?
No. Each run snapshots its evaluator configuration at launch, ensuring that a finished run is never re-scored against a model chosen after the fact, preserving the integrity of the results.
Is the readiness score a definitive release gate?
The readiness score serves as a signal rather than a definitive release gate, as the LLM judge does not yet have a published agreement measurement against human reviewers.
How are 'expected behaviors' determined during a test?
Expected behaviors are derived from your uploaded knowledge and policy documents. These derived expectations are treated as proposals rather than final verdicts and remain human-overridable.
What makes the adversarial personas different from standard users?
Unlike polite users, adversarial personas are first-class citizens designed to break the assistant using contradiction, prompt-injection, and AZ↔RU code-switching.
Secure Your AI Deployment
Start catching regressions and improving assistant safety with Argus AI today.
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