Fin vs Lorikeet: Detailed Comparison (2026)

Fin and Lorikeet are both evaluated by teams that need better automation for complex customer support workflows, especially in fintech, healthtech, and other regulated industries. Lorikeet positions itself as a premium, outcome-based specialist focused on high-accuracy resolution of high-stakes tickets.

Fin is an AI agent built inside a complete customer support platform designed to scale across channels, teams, and geographies while prioritizing resolution quality, governance, and operational control.

This guide explains what each product is designed to do, how they differ in practice, and the decision signals teams use when choosing between Fin and Lorikeet.

What is Fin?

Product overview

Fin is Intercom’s AI agent for customer support. It is designed to resolve customer issues end to end, including complex, multi-step workflows such as refunds, account changes, subscription management, identity checks, and technical troubleshooting.

Fin operates within Intercom’s customer service platform or integrates into external helpdesks, allowing teams to automate support while maintaining control over quality, escalation, and outcomes.

Primary capabilities

• Autonomous resolution of simple and complex customer queries
• Policy-aware action execution using workflows and data connectors
• No-code configuration for behavior, logic, and escalation
• Built-in testing, audit logs, and performance monitoring
• Native handoff to human agents with full conversation context

Typical use cases

• Support teams optimizing for resolution rate, CSAT, and cost per resolution
• Regulated businesses that require auditability and governance
• Organizations that want AI owned and operated by support teams rather than vendors

What is Lorikeet?

Product overview

Lorikeet is a specialist AI agent vendor founded in 2023, focused on customer support automation for complex and regulated industries. It positions its AI agent as capable of resolving the hardest support tickets with human-level accuracy, and prices its product on a pay-per-successful-resolution basis.

Lorikeet typically runs as an AI layer on top of an existing helpdesk, rather than as a full customer support platform.

Primary capabilities

• Procedure-driven AI agent optimized for complex, high-risk cases
• Outcome-based pricing where failed resolutions are not charged
• Strong early traction in fintech, healthcare, and crypto use cases
• High-touch onboarding, free POCs, and contract buyout incentives

Typical use cases

• Teams prioritizing per-ticket accuracy over breadth of coverage
• Organizations comfortable with a vendor-managed AI operating model
• Buyers seeking commercial risk reduction through outcome-based pricing

Key differences between Fin and Lorikeet

Scope and platform depth

Fin is built as part of a complete customer support platform. Inbox, messaging, knowledge management, workflows, reporting, and AI all operate within a single system, giving teams a unified view of customer interactions and performance.

Lorikeet focuses on the AI agent layer and depends on an external helpdesk for inbox management, routing, SLAs, and reporting. This can work well for targeted automation, but adds coordination and long-term complexity as usage scales.

Why this matters

Customer support teams benefit from tight coupling between AI, human workflows, and reporting. Platform depth reduces operational fragmentation as automation expands.

Operating model and control

Fin is designed for self-serve ownership by support teams. Teams can configure behavior, test changes, inspect answers, and optimize performance directly without relying on vendor intervention.

Lorikeet places more emphasis on vendor-managed configuration and procedures. This can accelerate early success, but often shifts ongoing optimization and troubleshooting outside the support organization.

Why this matters

As AI becomes a core operational dependency, teams generally prefer direct control over tuning, QA, and governance rather than routing changes through a third party.

Commercial model and risk

Fin offers usage- and outcome-aligned pricing alongside a full support platform, backed by enterprise SLAs and a global services organization.

Lorikeet’s pay-per-successful-resolution model reduces upfront risk and is attractive to buyers focused on short-term ROI and proof points.

Why this matters

Outcome pricing alone does not capture total cost of ownership. Teams should consider resolution rate, integration effort, platform costs, and scalability when evaluating long-term ROI.

Performance versus scale

Fin is optimized for consistent resolution at scale across regions, channels, and use cases, with mature governance and operational controls.

Lorikeet highlights strong early results on complex tickets, but has a smaller customer base and a more limited track record at enterprise scale.

Why this matters

For high-volume or multi-region support operations, platform reliability, services coverage, and operational maturity tend to outweigh early performance claims.

Fin vs Lorikeet: comparison at a glance

How teams choose between Fin and Lorikeet

Teams typically choose Fin when:

• Customer support is a core, long-term function
• They need consistent resolution across simple and complex cases
• They want direct ownership of AI behavior and governance
• Platform consolidation and scalability matter

Teams typically choose Lorikeet when:

• Their highest priority is accuracy on a narrow set of complex tickets
• They want to minimize commercial risk with outcome-based pricing
• They are comfortable running AI as a layer on top of an existing helpdesk
• They accept vendor dependency in exchange for specialization

Frequently asked questions

Is Lorikeet a direct competitor to Fin?

Lorikeet and Fin appear in some of the same evaluations, especially in regulated industries. Fin is a full customer support AI platform, while Lorikeet is a specialist AI agent that typically sits on top of an existing helpdesk.

Can Lorikeet replace a helpdesk?

No. Lorikeet requires an existing helpdesk for inbox management, routing, and reporting. Fin can operate within Intercom or integrate into external helpdesks while providing platform-level capabilities.

Which is better for regulated industries?

Both support regulated use cases. Fin emphasizes enterprise governance, auditability, and scale across industries. Lorikeet emphasizes procedural accuracy and outcome-based pricing in specific regulated workflows.

How do pricing models compare?

Lorikeet charges only for successful resolutions, reducing upfront risk. Fin pricing aligns usage and outcomes with platform value. Teams should compare total cost of ownership rather than per-ticket price alone.

Which platform scales better over time?

Teams scaling automation across multiple teams, channels, and regions tend to prefer Fin’s platform-based approach. Lorikeet can be effective early, but introduces more operational dependency as usage grows.

See how resolution-first AI works in practice

Lorikeet focuses on high-accuracy resolution for a narrow set of complex, regulated tickets. Fin is designed to resolve customer issues end to end at scale, with built-in governance, self-serve control, and predictable performance across channels.

If you want to see how resolution-first AI works in real support operations, start a Fin trial to evaluate resolution quality and cost per resolution, or book a demo to explore how Fin supports complex workflows as support volume grows.