Test your chatbot before launch
Test your chatbot before launch with Argus AI: a practical, on-prem approach built for Azerbaijani teams.
Validate Your Chatbot Performance Before Public Launch
Ensure your AI assistant is reliable, safe, and culturally aligned with Argus, a sophisticated self-hosted AI testing platform. As one of the three core engines of the Argus ecosystem, it leverages a shared runtime, model layer, credential store, and cost ledger alongside QA and pentest engines to provide a comprehensive validation environment. By simulating thousands of realistic Azerbaijani synthetic users, Argus identifies critical vulnerabilities and performance gaps in your chatbot before it ever reaches your customers. The platform specializes in high-fidelity simulation, where each synthetic user is assigned a specific role, goal, language style, knowledge level, and behavior. Rather than testing against ideal scenarios, Argus prioritizes adversarial personas—including frustrated, contradictory, or manipulative users—to stress-test the assistant's boundaries. By combining black-box connectivity with an Azerbaijani-native LLM judge, Argus transforms qualitative AI interactions into quantitative readiness scores and permanent assurance records.
The Advantages of Argus Testing
Stress-test resilience using adversarial personas designed to simulate frustration, prompt-injection, and manipulation.
Maintain complete data sovereignty and security through a self-hosted, on-premise architecture.
Ensure linguistic and cultural precision with an Azerbaijani-native LLM judge scoring accuracy, tone, and formality.
Eliminate recurring bugs using dedicated regression suites that confirm past issues remain fixed.
Validate the actual end-user experience via black-box connectors including REST, Dify, Kommunicate, and browser automation.
Generate audit-ready documentation with write-once assurance records and objective readiness signals.
Core Testing Capabilities
Synthetic User Generation
Creates thousands of users with specific roles, goals, language styles, knowledge levels, and behaviors to mirror real-world diversity.
Adversarial Personas
Simulates challenging interactions including frustration, contradiction, prompt-injection, manipulation, and AZ-RU code-switching.
Native LLM Judging
An Azerbaijani-native model scores the assistant on accuracy, tone, formality, compliance, and safety.
Black-Box Connectivity
Tests the assistant exactly as a customer would via REST, Dify, Kommunicate, or browser automation.
Policy-Driven Expectations
Derives expected behaviors from your uploaded knowledge and policy documents, while remaining human-overridable.
The Testing Process
Frequently Asked Questions
How does Argus prevent testing from disrupting my live assistant?
Per-assistant concurrency is strictly bounded to ensure that the testing process does not inadvertently become a denial-of-service attack on your system.
Can I change the evaluation criteria after a test run is finished?
No. Each run snapshots its evaluator configuration at launch, ensuring that finished runs are never re-scored against models chosen after the fact.
Is the readiness score a definitive release gate?
The readiness score serves as a signal rather than a strict release gate, as there is currently no published agreement measurement against human reviewers.
What makes the synthetic users realistic for the Azerbaijani market?
The engine generates users capable of AZ-RU code-switching and applies local linguistic styles, roles, and behaviors to mirror actual user diversity.
How are the 'expected behaviors' of the chatbot determined?
Expected behaviors are derived from your uploaded knowledge and policy documents. These derived expectations act as proposals and remain human-overridable rather than final verdicts.
Ready to secure your AI assistant?
Contact Allmaz to implement Argus and start testing your chatbot with professional-grade synthetic users.
Request a demo