AI assistant testing for Government
AI assistant testing for government. Public bodies require on-premise systems so citizen data never leaves the country.
Secure AI Validation for Public Sector Services
Allmaz provides a self-hosted AI testing framework specifically engineered for government bodies and public institutions that prioritize absolute data sovereignty. By deploying the platform on-premise, public sector organizations ensure that sensitive citizen data and internal policy documents never leave the national borders, maintaining a secure environment while rigorously validating the reliability, safety, and performance of their AI assistants. As a core engine of the Argus platform, this solution integrates QA and pentesting capabilities behind a single unified console and sign-in. It allows administrators to select a specific judge model within the settings to evaluate assistants as black boxes. By simulating thousands of diverse interactions and measuring them against native linguistic standards, the framework transforms AI validation from a manual guessing game into a scalable, audit-ready process.
Solving Government AI Challenges
Guarantees total data sovereignty through complete on-premise deployment and local processing.
Protects citizen privacy by ensuring all AI validation occurs within national borders.
Mitigates security risks by testing resilience against prompt-injection, manipulation, and adversarial personas.
Increases audit transparency and accountability using write-once assurance records for every test run.
Streamlines policy alignment by deriving expected assistant behaviors directly from uploaded knowledge documents.
Ensures consistent service quality through regression suites that confirm past issues remain permanently fixed.
Enterprise-Grade Testing Capabilities
Synthetic Citizen Simulation
Generates thousands of realistic Azerbaijani synthetic users, each assigned a specific role, goal, language style, knowledge level, and behavior to mirror actual public interactions.
Adversarial Persona Testing
Prioritizes 'first-class' adversarial personas—including frustration, contradiction, and AZ↔RU code-switching—to break the assistant where polite users would not.
Native LLM Judging
Utilizes an Azerbaijani-native LLM judge to provide precise scoring on accuracy, tone, formality, compliance, and safety.
Black-Box Connectivity
Interfaces via REST, Dify, Kommunicate, or browser automation, testing the assistant exactly as a citizen would experience it.
Policy-Driven Expectations
Derives expected behaviors from uploaded policy files; these remain human-overridable proposals rather than final verdicts.
The Validation Process
Frequently Asked Questions
How is citizen data protected during testing?
The system is entirely self-hosted and deployed on-premise. This ensures that all processing and data storage remain within the government's controlled infrastructure and never leave the country.
Can we customize the criteria used to judge the AI?
Yes. While the system derives expected behaviors from your uploaded policy documents, these are treated as proposals. Human reviewers can override these expectations to ensure the final verdict aligns with institutional standards.
Does the testing process risk crashing our public assistant?
No. The platform implements bounded per-assistant concurrency, ensuring that the volume of synthetic tests does not inadvertently become a denial-of-service attack on your production system.
What does the readiness score represent and how should it be used?
The readiness score is a quality signal rather than a definitive release gate. Because there is currently no published agreement measurement against human reviewers, it should be used as a guide for improvement.
How does the system handle changes to the judge model over time?
Each test run snapshots its evaluator configuration at launch. This ensures that a finished run is never re-scored against a different model chosen after the test was completed, preserving the integrity of the results.
Secure Your Public AI Infrastructure
Contact Allmaz to implement a self-hosted testing environment that guarantees data sovereignty and service reliability.
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