Lorikeet Emphasizes Risks in AI-Driven Customer Support QA

A LinkedIn post from Lorikeet highlights concerns about the limitations of traditional customer support quality assurance, particularly the common practice of sampling only 2–5% of tickets for review. The post argues that such sampling can delay detection of issues that are already depressing customer satisfaction scores across a broader base.

The post suggests that while QA tools have improved in measurement over the past decade, diagnosis of root causes and implementation of fixes often remain manual and slow. This critique is positioned as increasingly relevant in an era of AI agents, which can propagate systematic errors across large volumes of customer interactions in a short timeframe.

Lorikeet’s LinkedIn content references a mapping of the QA tooling landscape, contrasting established players with newer automated scoring tools and highlighting perceived gaps in their capabilities. The post also points to a set of due‑diligence questions buyers should ask QA vendors, including scenarios where a single provider both supplies an AI agent and evaluates its performance.

For investors, the post may indicate Lorikeet’s strategic focus on AI‑driven support quality and vendor accountability, suggesting a potential product or advisory angle in QA tooling and oversight. If Lorikeet can position itself as a solution to emerging risks in AI‑enabled support operations, it could benefit from increasing enterprise demand for tools that reduce quality blind spots and protect customer satisfaction metrics.