Lorikeet Launches Resolution Loop and Scales Claude Code Training to Deepen AI Role in Customer Operations

Lorikeet spent the week advancing its positioning at the intersection of AI-enabled customer support and operations, combining a major product release with an expanded education push for non-technical leaders. The company introduced Resolution Loop, a native escalation-management capability in its AI support platform designed to keep AI-to-human handoffs within Lorikeet rather than external ticketing tools.

Resolution Loop supports two modes: Take Over, where human agents resolve issues while the AI observes, and Steer, where the AI remains primary but consults experts and stores their guidance for future use. By preserving full conversation context across channels such as WhatsApp, SMS, email, and messaging, the feature aims to shorten resolution times, reduce stack fragmentation, and improve automation quality through closed-loop learning.

Lorikeet says the feature was developed in response to demand from subscribers in sectors like fintech, trading, and gifting, where governance, observability, and compliance guardrails are particularly important. If enterprises in these regulated, time-sensitive verticals adopt Resolution Loop at scale, the platform could see deeper workflow ownership, higher usage, and stronger retention, though actual financial outcomes will depend on customer uptake and competitive dynamics.

In parallel, Lorikeet highlighted growing interest in Claude Code and related AI tools among operations and customer experience teams, following a well-attended London event. The company is rolling out a global series of 45-minute virtual sessions on March 10 and 11, led by practitioners Tom Wing-Evans, Nathaniel Houghton, and Robbie Tilleard, aimed specifically at non-technical CX and operations leaders.

These sessions will showcase practical use cases, live builds, and step-by-step guidance on getting started with Claude Code and tools such as Cursor, while emphasizing the continued role of human judgment in overseeing AI outputs. By lowering the skills barrier and focusing on applied workflows rather than coding expertise, Lorikeet is seeking to embed its Coach features and broader AI stack more deeply across enterprise functions.

From a strategic perspective, the combination of product innovation in escalation management and education-led demand generation in CX and operations points to a coherent go-to-market focus on AI-enabled customer operations. Overall, the week underscored Lorikeet’s efforts to strengthen its competitive position in AI support and productivity solutions, while expanding its addressable market among non-technical enterprise decision-makers.