AI is redefining customer interactions, but its rapid rise has brought an equally rapid emergence of risk.
From hallucinations to compliance breaches, the pressure to deploy customer-facing AI agents at scale has collided with a lack of control, transparency, and testing.
Cyara’s AI Trust suite was born from this friction, and it's quickly becoming essential in turning generative AI (GenAI) promise into production-grade reality.
Origins Rooted in Risk
The AI Trust suite emerged in response to a now-familiar pain point: bots veering off-script with misleading or unsafe responses.
Infamous gaffes that include virtual agents swearing at customers, taking offence, and even telling people to break the law exemplify the risk within the service space.
To address this, Cyara developed the AI Trust testing suite, an AI testing solution with modules designed to expose the unique risks of generative AI. The latest module, AI Trust Misuse, detects and flags inappropriate or off-brand bot behavior in the development stage.
Complementing this is the AI Trust FactCheck module, which identifies factual inaccuracies and hallucinations that LLMs are known to produce.
Speaking to CX Today, Christoph Börner, VP of Engineering at Cyara, explained: "Trust is the main currency for AI-driven customer engagements or experiences.
As AI continues to reshape, the contact center landscape will also reshape. We know that new challenges will keep emerging, and we will evolve our approach to these new challenges.
FactCheck: Validating AI Responses Against Real Data
FactCheck is one of the suite’s powerful modules, a reality check for LLM outputs.
The concept is simple but critical: validate AI-generated responses against a “source of truth,” whether it's a product knowledge base, policy library, or technical manual. Responses are audited with color-coded feedback to flag factual errors and partial matches, which teams can use to QA and refine their models.
FactCheck most frequently finds issues involving fabricated product specifications, outdated policy terms, and incorrect procedural guidance.
