Lorikeet

Lorikeet positions itself as an AI customer concierge for complex support, targeting teams that have outgrown basic FAQ chatbots and need AI agents capable of triaging, routing, and resolving nuanced customer issues. The platform combines ticket triage, multi-channel coordination, and CRM-grounded responses into a single workflow engine.

The core differentiator is intelligent ticket routing. Instead of relying on static rules, Lorikeet's AI analyzes incoming support requests and assigns them to the appropriate team based on issue type, urgency, and available agent expertise. According to a vendor-published customer story, Amber, an energy retailer, used Lorikeet's triage workflow to solve a persistent misrouting problem — "the wrong teams were getting assigned issues that they couldn't solve, causing delays and frustration" for both staff and customers. The company built a custom workflow using existing logic, then refined it over time with improvements surfaced by Lorikeet's AI.

Integration depth is another pillar of the platform. Lorikeet connects with Salesforce and HubSpot to read customer context and execute workflows inside the CRM, with Slack integration for team notifications and in-channel support handling. The platform also supports call forwarding from major telephony providers including Amazon Connect, Twilio, and Genesys Cloud. For knowledge grounding, Lorikeet integrates with Notion and Google Workspace, letting teams designate approved documentation as reference material so AI responses reflect actual policies rather than generic knowledge.

On the extensibility front, Lorikeet can connect to external systems through APIs, webhooks, and MCP (Model Context Protocol) servers, enabling custom tool integrations within workflows. The vendor describes a "team of agents" model where specialized AI agents coordinate across phone, email, SMS, and chat, keeping customers informed throughout multi-step resolutions.

A customer story from Flex, a rent-payment platform, illustrates the platform's ability to handle high-stakes support. Flex's use case, according to the vendor, involves high urgency, high emotion, and high ticket volume — rent is tied to housing security. Lorikeet reportedly plugged into Flex's existing API and authentication flow, tapping into over 40 customer attributes spanning accounts, transactions, and property integrations.

However, the available evidence is limited to vendor-authored sources: customer stories, integration documentation, and marketing content. Independent third-party benchmarks, performance metrics, and pricing transparency are not present in the source materials. Prospective buyers should validate claims through their own evaluation processes, particularly around integration setup effort and AI accuracy in their specific domain.

The platform is best suited for mid-to-large support operations that have outgrown rule-based routing and need AI that can coordinate across channels while staying grounded in CRM and knowledge base data. Organizations considering Lorikeet should evaluate it alongside the broader Conversational AI landscape to ensure fit for their specific stack and complexity requirements.