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Fin vs Dust
Fin vs Dust: Detailed Comparison (2026)
Fin and Dust are both evaluated by teams looking to apply AI to customer-facing workflows. While both platforms use AI agents, they are designed for very different purposes and operating models.
Fin is built specifically for customer support resolution. Dust is built as a general-purpose platform for creating custom AI agents across many business functions, including support, sales, engineering, and operations.
This guide explains what each platform is designed to do, how they differ in practice, and how teams typically choose between Fin and Dust.
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, troubleshooting, and policy-driven escalations.
Fin operates within Intercom’s customer service platform and can also integrate into existing helpdesk environments, 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
- Companies where customer service is brand-critical
- Organizations that want AI owned and operated by support teams
What is Dust?
Product overview
Dust is a platform for building and deploying custom AI agents across an organization. It enables teams to create multiple specialized agents connected to company knowledge and internal tools, rather than shipping a single opinionated agent.
Dust positions itself around:
- No-code agent creation using templates and visual building blocks
- Multi-agent orchestration across departments
- Model flexibility, allowing teams to choose from OpenAI, Anthropic, Gemini, Mistral, and others
- Strong enterprise security and data control
Customer support is one of many use cases Dust supports, alongside sales, marketing, engineering, data, IT, HR, and legal workflows.
Primary capabilities
- Self-serve agent creation with templates and customization
- Multi-agent deployment for different roles and tasks
- Connections to internal tools such as Slack, Notion, GitHub, Google Drive, and APIs
- Model-agnostic architecture with support for multiple LLMs
- Enterprise-grade security controls (SOC 2, HIPAA, GDPR)
Typical use cases
- Organizations centralizing AI across multiple departments
- Teams automating internal workflows and knowledge work
- Companies willing to design, configure, and maintain custom agents per use case
Key differences between Fin and Dust
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.
- Dust focuses on the agent-building layer itself and relies on external systems for inbox management, routing, SLAs, and customer support reporting. This works well for internal automation, but adds coordination when used for customer-facing support.
Why this matters Customer support teams benefit from tight coupling between AI, human workflows, and reporting. Fin’s platform depth reduces fragmentation as automation scales.
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 building agents from scratch.
- Dust requires teams to design, configure, and maintain agents for each use case. While templates reduce initial effort, ongoing ownership sits with internal teams rather than being abstracted away.
Why this matters As AI becomes part of frontline support, many teams prefer faster iteration and clearer accountability over maintaining custom agent logic.
Customer support specialization
- Fin is optimized specifically for customer support outcomes: resolution rate, repeat-contact reduction, escalation quality, and CSAT.
- Dust is intentionally general-purpose. Customer support is one of many workflows it can support, rather than the primary design constraint.
Why this matters General-purpose agents often require additional configuration to meet the reliability, consistency, and safety standards expected in customer support.
Multi-agent vs single-agent approach
Fin uses a single AI agent optimized to handle a wide range of customer issues consistently.
Dust encourages building many specialized agents for different functions and tasks.
Why this matters Multiple agents can be powerful internally, but in customer support they can introduce inconsistent tone, fragmented context, and uneven escalation behavior.
Fin vs Dust: comparison at a glance
| Category | Fin | Dust |
|---|---|---|
| Product scope | Full support platform with AI agent | General-purpose AI agent builder |
| Primary focus | Customer support resolution | Cross-functional knowledge work |
| Best fit | Resolution-first support teams at scale | Ops and business teams building custom agents |
| Operating model | Self-serve, support-owned | Design-and-maintain by internal teams |
| Time to value | Fast, out of the box | Slower for customer support use cases |
| Agent strategy | Single optimized support agent | Multiple specialized agents |
| Reporting and CX metrics | End-to-end AI and human visibility | Relies on external support systems |
| Security posture | Enterprise-grade, support-focused | Enterprise-grade, organization-wide |
How teams choose between Fin and Dust
Teams typically choose Fin when:
- Customer support is a core business function
- Resolution quality and consistency matter more than flexibility
- They want measurable improvements in CSAT and cost per resolution
- They prefer an AI agent that works out of the box
Teams typically choose Dust when:
- They want to centralize AI agent creation across departments
- Internal automation and knowledge work are primary goals
- They are comfortable designing and maintaining custom agents
- Customer support is one of many secondary use cases
Frequently asked questions
Is Dust a direct competitor to Fin?
Dust and Fin are sometimes evaluated together, but they are built for different purposes. Fin is a customer support AI agent, while Dust is a general AI agent platform used across many business functions.
Can Dust replace a customer support platform?
No. Dust does not provide a native inbox, routing, SLAs, or customer support reporting. It relies on external systems for customer-facing operations.
Which platform is better for customer support?
Teams focused on customer support outcomes typically choose Fin because it is purpose-built for resolution quality, escalation safety, and support operations.
Does Dust support customer support workflows?
Yes, but customer support is one of many use cases. Dust is most commonly used for internal automation and knowledge work rather than as a primary support system.
Which platform scales better for customer support teams?
Fin scales more predictably for customer support because AI, human workflows, and reporting are unified in one platform. Dust scales well for internal AI use cases across teams.