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 maintain peak quality and safety through a robust, self-hosted testing platform. Argus AI empowers teams to simulate thousands of realistic Azerbaijani synthetic users, each defined by specific roles, goals, and behavioral patterns. By identifying regressions and vulnerabilities before they reach your customers, you can deploy with confidence knowing your assistant can handle the complexities of real-world interaction. As one of the three core engines of the Argus ecosystem, this testing suite is deployed alongside QA and pentest engines, all managed through a single console and sign-in. The platform utilizes a black-box testing approach, connecting via REST, Dify, Kommunicate, or browser automation to evaluate the assistant exactly as a customer would. By combining native LLM judging with adversarial persona simulation, Argus provides a comprehensive safety net for Azerbaijani-native AI deployments.
The Advantages of Argus AI Testing
Eliminate recurring bugs using dedicated regression suites that confirm past issues stay fixed.
Stress-test resilience against adversarial personas, including prompt-injection, manipulation, and contradiction.
Ensure total data privacy and control through a fully self-hosted, on-premise deployment.
Capture linguistic nuances of the Azerbaijani market, including complex AZ-RU code-switching.
Generate immutable, write-once assurance records and readiness scores for every testing cycle.
Maintain system stability with bounded per-assistant concurrency to prevent testing from becoming a DoS attack.
Core Testing Capabilities
Synthetic User Generation
Generates thousands of diverse users, each carrying a unique role, goal, language style, knowledge level, and behavior.
Adversarial Personas
Prioritizes high-risk interactions—such as frustration and manipulation—since polite users are the least likely to break an assistant.
Native LLM Judge
An Azerbaijani-native model provides objective scoring across accuracy, tone, formality, compliance, and safety.
Black-Box Testing
Integrates via REST, Dify, Kommunicate, or browser automation to test the actual customer-facing interface.
Knowledge-Driven Expectations
Derives expected behaviors from uploaded policy documents; these remain human-overridable proposals rather than final verdicts.
The Testing Workflow
Frequently Asked Questions
Is the readiness score a definitive release gate?
The readiness score serves as a critical signal rather than a strict release gate, as there is currently no published agreement measurement against human reviewers.
How does Argus AI prevent overloading the assistant under test?
To ensure testing does not inadvertently become a denial-of-service attack, per-assistant concurrency is strictly bounded.
What happens if I change the judge model after a test run is complete?
Each run snapshots its evaluator configuration at launch. This ensures that finished runs are never re-scored against a model chosen after the fact.
How is the platform deployed and managed?
Argus is a self-hosted platform. The testing engine is deployed alongside the QA and pentest engines, all accessible via one console and a single sign-in.
How are 'expected behaviors' determined during testing?
Expected behaviors are derived from your uploaded knowledge and policy documents. These are treated as proposals that can be overridden by human operators.
Secure Your AI Release
Start catching regressions and vulnerabilities with Argus AI's self-hosted testing platform.
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