The way companies serve customers is being rewritten.

Customers expect personalized, context-aware experiences at every touchpoint. They expect companies to know their history, anticipate their needs, and resolve issues before they escalate.

The companies that win will build and ship these experiences at light speed. The ones that lose will be stuck in silo’d legacy systems: CX teams clicking through complex UIs to debug workflows they can barely see inside, marketing and growth teams pushing out offers with rule-based logic, and reliant on engineering or vendors for configuration.

Lorikeet changes this. While others build tools to help agents respond faster, Lorikeet has built itself to be self configuring and agent-first. All you have to do, as a Lorikeet user, is supervise through a conversational UI.

Lorikeet is an AI concierge for the entire customer lifecycle: proactive, personalized, conversational. Under the hood, it balances natural language flexibility with deterministic logic for accuracy and compliance. The command center is a conversational layer via Lorikeet Coach and Lorikeet MCP.

You describe what you want. It gets built. From Claude Code, Codex, ChatGPT, wherever you already work.

Agent-First, Not Agent-Assisted

Most AI support tools help human agents respond faster. Draft suggestions, auto-complete, copilot-style assistance.

Lorikeet is different. We're agent-first: you build autonomous agents that handle tickets end-to-end. Humans configure, test, and audit, but they don't need to touch every conversation.

This is why self-configuring matters. When you're building autonomous agents (not helping humans respond), you need to move fast: test scenarios, adjust behavior, deploy changes. Lorikeet MCP enables exactly this: building the agent itself, through conversation.

From Lorikeet Coach to Lorikeet Everywhere

We introduced a conversation-first approach to building with Lorikeet Coach, which allows subscribers to configure workflows, test automations, and audit decisions through conversation. The response was immediate:

"Game changing. Before, I was making individual nodes, adding tools one by one, testing each iteration. That would take a week or two. Now I just explain the scenario: 'here are the 13 paths a customer can go through.' It starts creating the nodes and paths for me, and I fine-tune it. It made me feel like a conductor." Joel Sheehan, Customer Service AI Lead, Mosh

Today, we're taking it further. Everything you could do in Coach is now available wherever you already work: Claude Code, OpenAI Codex, ChatGPT, and any MCP-compatible tool.

Lorikeet is now self-configuring. You investigate data, test and build yourself, through conversation.

In Practice: Proactive Customer Re-Engagement

Here's what using Lorikeet MCP looks like in the real world.

A specialty lending company wants to recover lapsed customers. Traditional lifecycle marketing wasn't working - static outbound messages with low conversion.

With Lorikeet, they combined internal data from their data warehouse with ticket signals: payment history, past support interactions, account activity, behavioral patterns. Then they built a proactive outbound workflow that didn't just send a templated message - it started a conversation.

When a customer responded, Lorikeet understood their full context. It could reference their specific situation, answer questions about their account, and offer personalized options based on their history.

The result: 60% improvement in recovery rates compared to their prior outbound engagement.

This is the difference between lifecycle marketing and an AI concierge. You're not limited to static offers triggered by simple rules. You act on rich signals and deliver fully personalized experiences through conversation at scale.

Configure, Audit, and Build Without Vendor Dependency

With Lorikeet Coach and Lorikeet's MCP integration, you can:

• Build workflows conversationally: Describe what you want automated, and Lorikeet constructs it step by step

• Test Simulations: Run quality testing and simulations prior to launch

• Audit everything: Ask why a ticket was handled a certain way and get a traceable answer

• Self-heal: Get recommendations on how to improve and implement those recommendations

• Iterate instantly: Test changes against historical tickets, see results, adjust, deploy

The people closest to your customers, your CX team, marketing team, growth teams—can now own campaign set up and automation directly.

“A great use case is that any user of Lorikeet is able to describe the behaviour they want from their customer Concierge agent, and have Coach and MCP create simulations of prompts and how conversations might behave. Lorikeet can autonomously experiment with prompt changes to improve the performance.” - Jamie Hall, Lorikeet Co-Founder

Supercharge Customer Engagement: Combine Lorikeet With Your Existing Data and Tools

Lorikeet doesn't operate in isolation. MCP connects multiple tools in a single conversation, so you can work with Lorikeet alongside everything you already use: databases, CRMs, analytics, internal APIs, Slack, and more.

In a single conversation, you can:

• Pull ticket patterns from Lorikeet, cross-reference with your product database, identify what's driving support volume

• Query your CRM, check Lorikeet's handling history, draft a response, without switching tabs

• Debug end-to-end: from Lorikeet's decision logs through your application logs to root cause

• Update a workflow based on analytics insights, test it, deploy, all conversationally

Your AI assistant becomes a unified interface across your entire stack.

Built-In Guardrails

Conversational configuration doesn't mean uncontrolled. Lorikeet's MCP integration includes granular role-based access with separate toggles for read and write permissions, so teams can audit decisions without risking configuration changes. Every workflow can be tested against historical tickets in simulation mode before touching a customer. You can generate test suites from real ticket patterns, run them on every change, and roll back instantly if something isn't right. Every decision is explainable and traceable.

The Future We're Building Toward

The traditional model (navigating complex UIs, submitting requirements to vendors, waiting for changes) doesn't scale. It separates the people who understand your customers from the people who control the tooling.

We're building something different: an AI concierge that delivers proactive, personalized customer experiences, powered by the complexity necessary for real-world accuracy, but accessible through natural conversation.

Coach was the first step. MCP integration is the next.

Ready to try it? Connect to Lorikeet from Claude Code, Codex, or any MCP-compatible tool and start building through conversation.