Argus for Government
Runs thousands of realistic synthetic users against your chatbot, scores every conversation and gives each release a readiness score.
Argus for Government: Rigorous Chatbot Testing Built for Public Sector Standards
Government agencies deploying citizen-facing chatbots operate under obligations that have no equivalent in the private sector: responses must be accurate, formally appropriate, and fully auditable; citizen data must remain within national borders at every stage; and every release must be defensible to procurement committees, oversight bodies, and regulators alike. Argus is an on-premise AI testing platform purpose-built for exactly this environment. It generates thousands of realistic synthetic Azerbaijani users, routes them through your chatbot under adversarial conditions — including frustration patterns, contradictory inputs, prompt-injection attempts, and Azerbaijani–Russian code-switching — and scores every conversation using an Azerbaijani-native LLM judge that understands the language, tone, and formality standards expected in public-sector communication. The result is a clear, explainable release readiness score that gives your team the documented evidence it needs to approve and deploy with confidence.
Why Government Teams Choose Argus
Runs entirely on-premise so citizen data never crosses a national border, satisfying data sovereignty requirements by architecture from day one rather than relying on contractual assurances alone.
Generates thousands of realistic synthetic Azerbaijani user profiles — covering diverse literacy levels, query styles, and service scenarios — to surface edge cases that small manual test sets consistently miss.
Applies adversarial test patterns including frustration, contradiction, prompt-injection, and Azerbaijani–Russian code-switching, validating chatbot behaviour against the full complexity of real citizen interactions rather than sanitised demos.
Scores every conversation on accuracy, tone, formality, and regulatory compliance using an Azerbaijani-native LLM judge aligned to public-sector communication standards, producing language-appropriate assessments rather than generic quality metrics.
Produces a transparent, explainable readiness score for every release, accompanied by full conversation-level logs that give procurement committees and auditors the documented evidence they require before a public-facing system is approved.
Maintains a persistent regression library so that previously identified defects are automatically retested against every new build, confirming fixes are stable and that new development has not reintroduced old failures.
Core Capabilities Designed for Public Bodies
Sovereign On-Premise Deployment
Argus installs within your own infrastructure. No citizen data, query logs, or conversation records are transmitted to external servers, meeting the strict data-protection obligations of public institutions by design rather than by policy.
Thousands of Realistic Synthetic Citizens
The platform generates a large, diverse population of synthetic Azerbaijani users that reflect the range of literacy levels, query styles, and service needs found across the public. Testing at this scale surfaces edge cases that small manual test sets routinely miss.
Adversarial and Code-Switching Test Scenarios
Synthetic users apply frustration, contradiction, prompt-injection attempts, and Azerbaijani–Russian code-switching — the real patterns citizens use — so your chatbot is validated against genuine interaction complexity, not sanitised demos.
Azerbaijani-Native LLM Judge
Every conversation is scored by a judge model built for the Azerbaijani language and public-sector context. It evaluates accuracy, tone, formality, and regulatory compliance, producing consistent, language-appropriate assessments rather than generic quality metrics.
Release Readiness Score
Each test run concludes with a single, explainable readiness score that summarises overall chatbot quality. Teams can set pass thresholds aligned to their own governance policies, making go/no-go decisions objective, consistent, and fully defensible.
Regression Suites for Continuous Assurance
Argus maintains a library of previously discovered issues and reruns them automatically against every new release, confirming that fixes hold and that new development does not reintroduce old failures across successive build cycles.
From Integration to Approved Release in Four Steps
Frequently Asked Questions
Does Argus require real citizen data to generate its test users?
No. Argus generates fully synthetic Azerbaijani user profiles from the ground up. No real citizen records, personal data, or live query logs are needed or used at any stage of testing, eliminating privacy risk entirely.
Can Argus be deployed inside our agency's own data centre?
Yes. Argus is designed exclusively for on-premise installation. All processing, conversation generation, and scoring happen within your own infrastructure, so data sovereignty obligations are met by architecture rather than by contractual assurances that depend on third-party compliance.
How does the readiness score support our procurement and audit processes?
The readiness score is accompanied by full conversation-level logs and per-dimension scores covering accuracy, tone, formality, and compliance. These records provide the structured, documented evidence that procurement committees and auditors typically require before a public-facing system is approved for release, and they can be retained as a permanent audit trail across release cycles.
What happens if our chatbot handles both Azerbaijani and Russian language input?
Argus includes Azerbaijani–Russian code-switching as a standard adversarial test pattern, directly reflecting how citizens naturally mix languages in real interactions. The Azerbaijani-native LLM judge is equipped to evaluate chatbot responses in this mixed-language context, ensuring quality assessments remain accurate and meaningful rather than defaulting to a single-language baseline.
How do regression suites protect quality across multiple release cycles?
When a defect is identified and fixed, Argus stores the corresponding test scenario in a persistent regression library. Every subsequent release automatically reruns those stored scenarios, confirming that the fix remains stable and that no new code changes have reintroduced the problem — giving teams continuous assurance rather than point-in-time snapshots.
Ready to Release with Confidence?
Contact the Allmaz team to arrange a demonstration of Argus running against your chatbot in a sovereign, on-premise environment — and see what a readiness score looks like before your next public launch.
Request a demo