Fin
Fin, formerly Intercom, is an autonomous support agent that resolves customer conversations end to end: grounded in your help content, working across chat and email, and priced by outcome rather than seat, so you pay per resolution it actually completes. It layers over existing helpdesks rather than demanding a migration.
The operating loop is the product: simulate answer quality before customers see it, deploy on routine volume, escalate the rest to humans with full context attached, and mine unresolved questions for the content gaps behind them.
Fin publishes an average resolution rate of 76 per cent across its customers; definitions of resolution vary across the market, so the honest baseline for staffing and economics is a trial on your own volume rather than any vendor's headline figure.
- Cost
- Paid only
- Ease
- Model
- Hosted service
- Checked
- August 2026
Prices, plans and model versions change fast: this is a mid-2026 snapshot; check the tool's official site for the latest.
Best for
- Autonomous end-to-end resolution of routine conversations
- Outcome pricing: pay per resolution, not per seat
- Layering over an existing helpdesk without migration
- Escalation to humans with full context attached
- Mining unresolved cases for missing help content
Less suited to
Outcome pricing defies flat-rate budgeting: costs scale with volume resolved, and forecasting takes modelling rather than a price list. Fin's own reporting puts average resolution at 76 per cent, but resolution definitions vary by vendor and results track content quality, so plan staffing from a trial on your own volume rather than from any published rate.
Answer quality also rides entirely on help-content quality: thin documentation produces a confidently unhelpful agent.
Costs & data, in short
Fin charges per resolved conversation, so the bill scales with how well it performs rather than with seats. Used inside Intercom it also needs at least one paid seat; used on another helpdesk there is a small monthly minimum. Model your real ticket volume before committing.
Fin answers from your help content and past conversations under Fin's commercial terms. One strategic note: Salesforce has agreed to acquire the company, with Fin expected to fold into its Agentforce line, which is worth weighing in any long-term platform decision.
Plans
| Fin with your current helpdesk | Price on application |
|---|---|
| Fin with the Intercom helpdesk | Price on application |
Prices as of August 2026. Prices and plans change regularly. Check with the provider before you buy.
In practice
How Fin is used, area by area.
Jobs
Customer support
Fin is the support agent to beat on autonomous resolution
Fin is the support agent to beat on autonomous resolution. It answers from your help content and past conversations, resolves a large share of routine volume end to end across chat and email, and charges only when it succeeds, with the operating loop as the real product: simulate answer quality before customers see it, deploy on routine volume, escalate the rest with full context, and mine unresolved questions for the content gaps behind them. It also layers over a helpdesk you already have, including Zendesk, so the bot decouples from the platform decision. It belongs with teams whose goal is reducing ticket volume, paying for outcomes rather than seats. Bills scale with success and resist forecasting, and a resolution is counted when the customer stops replying, not only when they confirm the fix, so audit resolved conversations. The pending Salesforce acquisition belongs in any long-term platform decision.
Example tasks
- Resolve routine questions end to end across chat and email
- Run Fin on top of your existing helpdesk without a migration
- Escalate unresolved cases to humans with the full conversation attached
- Use its reporting to find the content gaps behind failed answers
- Simulate answer quality on real past conversations before launch
Limits
Bills scale with success and can be hard to forecast, since a resolution is counted when the customer stops replying, not only when they confirm the fix. Budget-sensitive teams should model real volume first.
Compares
| vs | Pick Fin when | Pick the other when |
|---|---|---|
| Zendesk AIFull comparison → | Fin runs on top of the helpdesk already in place, so the bot decision does not drag a migration behind it, and resolves routine questions end to end across chat and email | the helpdesk is already Zendesk and you want automation and agent assistance without changing systems |
| Tidio LyroFull comparison → | Fin is the stronger and more expensive engine | the budget and the ticket volume are both small |
| SierraFull comparison → | Fin makes the operating loop the product: simulate, deploy on routine volume, escalate the rest with the full conversation attached, then mine the unresolved questions for the content gaps behind them | the deployment is enterprise-scale and one agent should cover chat, voice, SMS, WhatsApp and email under guardrails you define |
| HubSpot AI (Breeze)Full comparison → | Fin charges only when it succeeds, though a resolution counts when the customer stops replying rather than only when they confirm the fix, so bills scale with success and are hard to forecast | escalations should carry full CRM context into the platform marketing and sales data already lives in |
| DecagonFull comparison → | Fin lets a budget-sensitive team model real volume before committing, since the mechanics of what gets charged are visible up front | the operation is large enough to buy an AI-native platform as infrastructure, with the trust boundaries agreed before any conversation reaches it |
| AdaFull comparison → | Fin's pending Salesforce acquisition belongs in any long-term platform decision taken about it | the team wants proven service automation with operational tooling, bought as an enterprise platform |
| Salesforce Einstein / AgentforceFull comparison → | answer quality is tried against real past conversations before a single customer meets the bot, so the content gaps behind a failed answer surface in a simulation rather than in a live chat | service runs on Salesforce at a scale where the governance review decides the shortlist |
Founders & entrepreneurs
Fin lets a founder ship real customer support without a support hire
Fin lets a founder ship real customer support without a support hire. It answers from your docs and past conversations, resolves the majority of routine volume end to end, and charges only when it succeeds, which fits founder economics precisely: cost scales with customers rather than headcount, and the first support hire gets deferred until volume genuinely justifies one. It meets founders at a specific moment: the support inbox outgrowing personal reply capacity. The preconditions matter: with no help content it has nothing to answer from, so write the docs first, and pre-launch products with ten users need a founder replying personally more than they need deflection. Outcome pricing means success costs money, so model resolution volume against runway, and note the pending Salesforce acquisition before signing anything long-term.
Example tasks
- Stand up an agent that answers from your help docs from day one
- Resolve routine billing, how-to and account questions end to end
- Escalate the conversations that genuinely need the founder, with context
- Mine unresolved questions for the docs you have not written yet
- Scale support volume without the first support hire
Limits
With no help content it has nothing to answer from, so write the docs first; and pre-launch products with ten users need a founder replying personally more than they need deflection.
Compares
| vs | Pick Fin when | Pick the other when |
|---|---|---|
| Zendesk AIFull comparison → | Fin runs standalone or atop whatever helpdesk you adopt later | you are committing to its full platform early |
| Tidio LyroFull comparison → | Fin is the stronger engine at higher cost per resolution | budget rules and volume is small |
Tasks
Customer support & chatbots
Fin is the closest thing this category has to a benchmark
Fin is the closest thing this category has to a benchmark. Its published resolution rates lead the mainstream field, it deploys across chat, email and voice, and unlike most rivals it does not force a platform migration: it operates as the AI layer on whatever helpdesk you run, which decouples the bot decision from the helpdesk decision entirely. Teams choosing on resolution performance start here, with the caveat stated plainly: the 76 per cent average resolution rate is the vendor's own figure, definitions of resolution vary across the market, and the number that matters is the one a trial produces on your volume. Outcome pricing defies flat budgeting, since a good month costs more, and what counts as resolved deserves auditing, because abandoned is not answered. The pending Salesforce acquisition makes the long-term roadmap a fair pre-signature question, and cost per resolution deserves monthly tracking against a human-handled baseline.
Example tasks
- Deploy an agent that resolves the majority of routine conversations
- Layer Fin over Zendesk, Salesforce or HubSpot without moving platforms
- Test answer quality in simulation before customers see it
- Extend the same agent from chat into email and voice channels
- Track resolution quality, not just deflection volume
Limits
Outcome pricing means a fixed budget is impossible to guarantee; a good month costs more. Teams wanting a flat, predictable line item should weigh seat-priced or platform-included alternatives.
Compares
| vs | Pick Fin when | Pick the other when |
|---|---|---|
| Zendesk AIFull comparison → | Fin is built to resolve the majority of routine conversations outright rather than route them onward, which is a different target from deflection and is measured differently | you want the bot, the human queue and the reporting in one system rather than a standalone bot bolted on |
| Freshdesk FreddyFull comparison → | Fin leads on resolution capability | you are already on Freshworks and want good-enough AI at lower complexity |
| SierraFull comparison → | Fin decouples the bot decision from the helpdesk decision, running as the AI layer over Zendesk, Salesforce or HubSpot with no migration, and it simulates answer quality on real past conversations before a customer sees one | the requirement is one governed agent carrying the same guardrails into phone-based voice |
| HubSpot AI (Breeze)Full comparison → | Fin extends one agent from chat into email and voice and prices on outcomes, which means a good month costs more and a fixed budget cannot be guaranteed | the bot should feed the same CRM marketing and sales run on, and a bought bot is preferable to a built one |
| DecagonFull comparison → | Fin asks to be judged on resolution quality rather than deflection volume, with published rates that lead the mainstream field | the purchase is enterprise infrastructure scoped through sales, and a large support operation is ready to hand real volume to an AI-native agent platform under supervision |
| Salesforce Einstein / AgentforceFull comparison → | Fin's pending Salesforce acquisition makes its long-term roadmap a fair question to ask before signing anything | support runs on Salesforce already and the agent should act on records it can see natively |
| AdaFull comparison → | Fin states its baseline plainly: real-world resolution settles well under half of volume for most teams, so humans remain the majority channel | mature, channel-spanning service automation matters more than either helpdesk nativeness or novelty |
Where to start
Not sure what to adopt first?
Five quick questions about your job, task and constraints. We'll suggest your top three tools, plus the one to try first.
Alternatives
Same category, different strengths.
Common questions
Is Fin free?
No: Fin is a paid product, with plans for individuals and teams.
Where does Fin fit best?
Fin fits best in Customer support and Founders & entrepreneurs; see its practice notes for how.
Before sharing confidential or personal data, check this tool's data-governance and training policies. They differ between providers and can change.
Last checked: August 2026