Argus for Insurance

Insurance operations are built on trust — with policyholders, claimants, and regulators who expect consistent, accurate, and compliant responses at every interaction. A single poorly handled claims query or a tone-deaf response to a dispute can erode that trust and invite regulatory scrutiny. Argus addresses this risk before it reaches real customers by generating thousands of realistic synthetic Azerbaijani users who probe your chatbot across the full spectrum of insurance conversations: claim status enquiries, decision disputes, policy term questions, and adversarial inputs including prompt-injection attempts and AZ↔RU code-switching. Every conversation is then scored by an Azerbaijani-native LLM judge across accuracy, tone, formality, and compliance, producing a body of auditable evidence that your team can review and act on before any release goes live. What sets Argus apart for insurance teams is the combination of linguistic precision and operational integration. Because the LLM judge is native to the Azerbaijani language and regulatory context, nuanced phrasing, formality expectations, and market-specific compliance signals are evaluated correctly rather than approximated through translation. Each test run concludes with a single release readiness score that gives product owners, compliance officers, and QA teams a shared, objective signal — removing ambiguity from the go or no-go decision. Automated regression suites ensure that issues identified in previous cycles, whether surfaced by an internal audit, a regulator finding, or a customer complaint, remain resolved in every subsequent build, creating a traceable record of continuous improvement that regulators and internal governance teams can rely on.