Fin vs Gradient Labs (Otto): Detailed Comparison (2026)

Fin and Gradient Labs are both evaluated by teams looking to automate customer support in high-risk and regulated environments, particularly financial services.

While both platforms position themselves as AI agents capable of handling complex workflows, they differ in scope, operating model, and long-term scalability.

This guide explains what each platform is designed to do, how they differ in practice, and how teams typically choose between Fin and Gradient Labs.

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 workflows such as transaction disputes, failed payments, account changes, and technical troubleshooting.

Fin operates within Intercom’s customer service platform or integrates into existing 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, monitoring, and performance optimization
• Native handoff to human agents with full context

Typical use cases

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

What is Gradient Labs (Otto)?

Product overview: Gradient Labs is a startup focused on AI agents for financial services and FinTech support teams. Its AI agent, Otto, is positioned as a “procedure-following AI agent” built specifically for regulated environments.

Gradient Labs markets Otto as a single AI agent that spans:

• Frontline support
• Proactive outbound support
• Back-office operations
• Voice support for financial services

Otto is typically deployed as an AI overlay on top of Intercom, Zendesk, Freshworks, or in-house helpdesks via API integrations.

Primary capabilities

• Procedure-driven AI agent defined in plain-language “live documents”
• Built-in financial services guardrails applied on every turn
• Proactive outreach for issues like payment failures, fraud alerts, and document collection
• Back-office task handling triggered by system events
• Voice AI agent designed for compliant, end-to-end resolution over the phone

Typical use cases

• FinTech and financial services teams keeping their existing helpdesk
• Organizations prioritizing compliance, procedural accuracy, and voice
• Teams comfortable with a vendor-managed AI operating model

Key differences between Fin and Gradient Labs

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, reducing integration overhead and giving teams a unified view of performance and customer experience.
• Gradient Labs focuses on the AI agent layer and relies on external tools for inbox management, routing, and reporting. This can work well when teams want to preserve an existing stack, but it also introduces additional coordination and long-term complexity as usage scales.

Why this matters: Teams running AI at scale tend to benefit from tighter coupling between AI, human workflows, and reporting. Fin’s 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 waiting on vendor involvement. This enables faster iteration and clearer accountability.
• Gradient Labs exposes fewer self-serve controls and places more emphasis on vendor-managed procedures and configuration. This can accelerate early setup, but often shifts ongoing optimization and troubleshooting outside the support team.

Why this matters: As AI becomes a core operational dependency, teams typically prefer direct control over tuning, QA, and performance management rather than routing changes through a third party.

Proactive and back-office workflows

• Fin supports proactive messaging and automation as part of a broader support platform, allowing teams to prevent issues, guide customers in-product, and automate follow-ups within the same system that handles inbound support.
• Gradient Labs positions proactive outreach and back-office operations as first-class use cases, particularly in financial services, where system-triggered actions and investigations are common.

Why this matters: Both platforms support proactive workflows, but Fin’s advantage is consistency: proactive, reactive, and human-assisted support all share the same operating surface, reducing context loss and handoff friction.

Voice support

• Fin supports voice through integrations and an expanding voice roadmap, enabling teams to layer AI into existing telephony and contact center setups while keeping support operations centralized.
• Gradient Labs positions itself around a dedicated voice AI agent built specifically for financial services, with compliance guardrails applied on every call.

Why this matters Dedicated voice agents can be valuable in finance-specific scenarios, but many teams prioritize flexibility and integration with existing voice infrastructure. Fin’s approach allows teams to extend AI into voice without committing to a separate, voice-specific system.

Fin vs Gradient Labs: comparison at a glance

How teams choose between Fin and Gradient Labs

Teams typically choose Fin when:

• Customer support is a core, long-term function
• Resolution quality and repeat-contact reduction matter
• They want direct control over AI behavior and performance
• They prefer consolidating tools into a single platform

Teams typically choose Gradient Labs when:

• They operate in FinTech or financial services
• They want a procedure-driven AI agent layered onto an existing helpdesk
• Voice, proactive outreach, and back-office automation are immediate priorities
• They accept vendor dependency in exchange for domain-specific focus

Frequently asked questions

Is Gradient Labs a direct competitor to Fin?

Gradient Labs appears in many FinTech and regulated-industry evaluations. However, Fin is a full AI agent platform for customer support, while Gradient Labs is a narrower AI overlay focused on financial services workflows.

Can Gradient Labs replace a helpdesk?

No. Gradient Labs requires an existing helpdesk for inbox management, routing, and reporting. Fin can operate with or without Intercom’s helpdesk.

Which platform is better for regulated industries?

Both support regulated use cases. Fin emphasizes broader governance, reporting, and compliance across industries. Gradient Labs emphasizes financial-services-specific guardrails and procedures.

Which platform scales better over time?

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

Do both platforms support proactive and voice use cases?

Yes, but in different ways. Gradient Labs positions proactive outreach and voice as core capabilities. Fin supports proactive messaging and voice through its platform and integrations.