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Generative AI and Large Language Models (LLMs) is revolutionizing business automation and data-driven decision-making. But while LLMs promises significant benefits for business, there’s an Achilles hell: It often produce “riskly answers” aka hallucinations, generating incorrect or nonsensical answers that can undermine businesses, introducing serious risks for business operations, customer trust and company reputation.

Our product works by monitoring and evaluating every LLM output to detect potential wrong and riskly content in realtime, then proactively correct them following each application policy rules.

There are three main components, Guard Evaluation, Auto Correction and Application Policies.

• User input and LLM output: Usually are the raw prompt, user question and the output from your LLM providers or model.

• Input and output rails: User inputs and LLM outputs are evaluated to ensure they align with predefined application policies.

• Guard realtime evaluation: This step checks the content against application policy rules to detect and prevent any violations.

• Auto correction: If any issues are identified, the system automatically corrects the output to meet the required standards.

• Application policies rules: Usually are guidelines to help on Guard evaluation step to monitor and identify riskly content.