According to a recent LinkedIn post from Lorikeet, the company is drawing attention to data-quality risks that can emerge when enterprises deploy AI agents on top of existing knowledge bases. The post describes a fintech implementation where customers reportedly received conflicting answers because legacy and updated documentation coexisted with inconsistent policy details.
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The company’s LinkedIn post highlights that, in its view, human agents naturally compensate for poor documentation, while AI systems treat all content as equally authoritative. The post suggests that firms often discover that 30–40% of internal articles may have structural issues such as conflicting duplicates, thin process descriptions, or problematic internal language that can degrade AI performance.
As shared in the LinkedIn content, Lorikeet is promoting a free Knowledge Base Evaluator tool that scans uploaded CSV exports locally in the browser to flag these patterns in roughly two minutes. For investors, this focus indicates a product-led strategy targeting enterprises investing in AI-driven customer support, with an emphasis on pre-deployment data hygiene as a potential revenue and adoption driver.
If the tool gains traction, it could position Lorikeet as an upstream gatekeeper in the AI customer-service stack, potentially improving cross-sell opportunities for more advanced solutions. The post also underscores growing enterprise concern over governance and reliability of AI outputs, suggesting that Lorikeet is aligning its offerings with a broader market trend toward trust, accuracy, and risk mitigation in AI deployments.