Lorikeet

What is Lorikeet?

Lorikeet is an AI-powered customer experience platform that specializes in building intelligent agents capable of handling complex, multi-step customer service workflows autonomously. Unlike traditional chatbots that can only manage simple question-and-answer interactions, Lorikeet's AI agents are designed to execute entire operational processes from start to finish, including tasks that require accessing multiple systems, making decisions based on business rules, and taking actions on behalf of customers. The platform represents a new generation of customer service AI that goes beyond conversation to deliver genuine resolution.

The company was founded with the insight that most customer service interactions are not just about providing information but about completing tasks. When a customer contacts support to change a shipping address, process a return, or upgrade their subscription, they need an agent who can not only understand the request but also navigate the necessary systems and complete the action. Lorikeet's AI agents are built to handle these end-to-end workflows, integrating with backend systems to read data, make API calls, update records, and confirm actions, all while maintaining a natural conversation with the customer.

Lorikeet positions itself at the intersection of conversational AI and process automation, combining the natural language understanding capabilities of modern large language models with the structured execution capabilities of robotic process automation. This hybrid approach allows the platform to handle the kind of nuanced, multi-step interactions that typically require experienced human agents, achieving automation rates that were previously impossible with simpler chatbot technologies. The platform is particularly well-suited for companies that want to automate their customer service operations without sacrificing the quality and completeness of resolution that customers expect.

Key Features

• Multi-Step Workflow Execution: Lorikeet's AI agents can execute complex workflows that involve multiple sequential and conditional steps, such as verifying customer identity, checking order status across systems, applying business rules to determine eligibility for a return, processing the return in the e-commerce platform, and sending a confirmation email. These workflows are defined through a visual builder and can incorporate decision points, conditional logic, and error handling to ensure reliable execution.

• Deep System Integration: The platform connects with a wide range of backend systems including CRMs, e-commerce platforms, order management systems, billing platforms, and custom APIs. These integrations go beyond simple data retrieval, allowing the AI agent to take actions within connected systems such as creating tickets, updating records, processing transactions, and triggering downstream workflows. This action-oriented integration is what enables Lorikeet to resolve issues rather than just discuss them.

• Policy-Aware Decision Making: Lorikeet's AI agents can be configured with detailed business policies and rules that govern how they handle different scenarios. For example, the agent can understand that returns are accepted within 30 days of delivery for unused items, that VIP customers receive extended return windows, and that certain product categories have different return policies. This policy awareness ensures consistent, compliant decision-making across all automated interactions.

• Human Escalation with Context: When the AI agent encounters situations that exceed its capabilities or require human judgment, it seamlessly transfers the conversation to a human agent along with a comprehensive summary of the interaction, the steps already completed, and the remaining actions needed. This intelligent handoff prevents customers from having to repeat information and allows human agents to pick up exactly where the AI left off.

• Continuous Learning and Optimization: The platform includes analytics and feedback mechanisms that track resolution rates, customer satisfaction, workflow completion rates, and escalation patterns. This data is used to continuously improve the AI agent's performance, identify new automation opportunities, and refine existing workflows. Teams can review conversation logs, identify failure patterns, and update workflows and policies to address gaps in the AI's capabilities.

How It Works

Implementing Lorikeet begins with mapping out the customer service workflows that the organization wants to automate. The platform's team works with clients to document the steps involved in handling common request types, including the systems accessed, decisions made, actions taken, and exceptions handled. These workflows are then translated into Lorikeet's workflow definition language, which specifies the sequence of operations, conditional logic, and integration points for each process.

Once workflows are defined, the platform's AI agents are configured with the organization's knowledge base, policies, and brand voice guidelines. The natural language understanding layer is trained to recognize the intents and entities relevant to the defined workflows, ensuring that the AI can accurately identify what a customer is asking for and extract the necessary information to execute the appropriate process. Integration connections are established with the organization's backend systems, with appropriate authentication and permission controls in place.

In operation, when a customer initiates a support interaction, Lorikeet's AI agent engages in conversation to understand the request, identifies the relevant workflow, gathers any additional information needed, and executes the multi-step process to resolve the issue. Throughout the interaction, the agent provides status updates to the customer, confirms actions before executing them when appropriate, and handles exceptions gracefully. The entire interaction is logged for quality assurance, analytics, and continuous improvement purposes. Human supervisors can monitor active conversations in real-time and intervene if needed, though the goal is for the AI to handle the vast majority of interactions independently.

Use Cases

• E-Commerce Order Management: Lorikeet excels at handling the full spectrum of order-related requests including order modifications, address changes, cancellations, returns processing, refund issuance, and exchange coordination. The AI agent can access order management systems to verify order details, check return eligibility based on policies, initiate return shipping labels, process refunds to the original payment method, and confirm the completed actions to the customer, all within a single conversation.

• Subscription Management: For subscription-based businesses, Lorikeet automates complex subscription workflows including plan upgrades and downgrades, billing cycle changes, payment method updates, pause and resume requests, and cancellation flows with retention offers. The AI can access subscription management platforms, apply promotional offers, prorate charges, and update billing records without human intervention.

• Insurance Claims Processing: Insurance companies use Lorikeet to automate the initial stages of claims processing, including claim intake, documentation collection, coverage verification, and status updates. The AI agent guides customers through the claims process step by step, collects required information and documentation, checks coverage details against policy records, and creates the claim in the claims management system.

• Travel and Hospitality Booking Changes: Travel companies leverage Lorikeet to handle booking modifications, cancellations, rebooking requests, and loyalty program inquiries. The AI can access reservation systems to check availability, calculate change fees based on fare rules, process modifications, and issue updated confirmation documents, handling the complexity of travel industry workflows that typically require specialized knowledge.

Pricing

Lorikeet operates on a custom pricing model tailored to each organization's specific needs and scale. Pricing is typically based on factors including the number of automated resolutions, the complexity of workflows being automated, the number of system integrations required, and the volume of customer interactions processed. As a relatively newer entrant in the market focused on enterprise and mid-market customers, Lorikeet does not publish standard pricing tiers on its website. Prospective customers are encouraged to engage with the sales team for a demonstration and customized quote. The company emphasizes the return on investment from automation, with customers typically seeing significant cost savings from reduced agent handling time and increased first-contact resolution rates. Implementation costs may also apply depending on the complexity of the workflow design and integration requirements.

Pros and Cons

Pros:

• Unique ability to execute complex, multi-step workflows autonomously sets Lorikeet apart from simple chatbots and FAQ-style AI assistants, delivering genuine issue resolution rather than just information retrieval.

• Policy-aware decision making ensures that automated resolutions are consistent with business rules and compliance requirements, reducing the risk of errors that could result from agents misinterpreting or overlooking policies.

• Deep system integration capabilities allow the AI to take real actions in backend systems, going beyond conversation to actually resolve customer issues end-to-end within a single interaction.

Cons:

• Implementation requires significant upfront investment in workflow mapping, system integration, and policy documentation, which can be time-consuming and resource-intensive for organizations with complex operational environments.

• As a newer platform, Lorikeet may have a smaller ecosystem of pre-built integrations and templates compared to more established customer service platforms, potentially requiring custom development for specialized use cases.

Who Is It Best For?

Lorikeet is best suited for mid-market to enterprise organizations that have well-defined customer service processes involving multiple steps and system interactions, and want to automate these workflows at a level that goes beyond simple conversation. E-commerce companies, subscription businesses, insurance providers, and travel companies with complex operational workflows benefit most from the platform's multi-step execution capabilities. Organizations that have already tried simpler chatbot solutions and found them insufficient for handling the complexity of their customer service processes will find Lorikeet's approach particularly compelling. The platform is ideal for teams that want to achieve high automation rates without compromising on resolution quality.

Why Choose Lorikeet?

Lorikeet represents the next evolution in customer service AI, moving beyond conversational assistance to genuine operational automation. By combining advanced natural language understanding with multi-step workflow execution and deep system integration, the platform enables a level of autonomous resolution that was previously only achievable through human agents. For organizations that are serious about transforming their customer service operations through AI and want a solution that can handle the full complexity of real-world support workflows, Lorikeet offers a uniquely capable platform that delivers measurable improvements in resolution rates, customer satisfaction, and operational efficiency.