According to a recent LinkedIn post from Lorikeet, the company is drawing attention to limitations in traditional customer support quality assurance, where most teams review only 2–5% of tickets. The post argues that such sampling can delay the detection of issues driving CSAT declines, especially as QA tools have emphasized measurement over root-cause diagnosis and remediation.
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The post further suggests that this gap becomes more acute with AI-based support agents, which can scale systematic errors across large volumes of conversations in short timeframes. Lorikeet indicates it has mapped the QA tooling landscape, contrasting established and newer automated scoring tools and proposing due-diligence questions buyers should ask vendors, including those that both supply AI agents and grade their performance.
For investors, this content points to a growing pain point in AI-enabled customer support operations and implies a potential market for solutions that go beyond sampling and scoring to faster incident detection and root-cause analysis. If Lorikeet’s offering effectively addresses these concerns, the company could benefit from increased demand as enterprises adopt AI agents at scale and seek to mitigate associated quality and reputational risks.
The emphasis on vendor-evaluation questions and on conflicts when a single provider both runs and evaluates AI agents hints at an opportunity for differentiated, independent QA platforms. This positioning, if supported by robust technology and go-to-market execution, may strengthen Lorikeet’s competitive stance in the customer experience and support-automation ecosystem and support long-term monetization potential.