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Sharing how we're using Slack side conversations, plus one problem I'm hoping someone can confirm or work around.What we've builtOur technical support team has a private internal Slack channel with an always-on AI agent sitting in it (built on Claude, running in our own agent framework). There's no trigger word or slash command — you just start a new thread in the channel and the agent replies in that thread. It has read access to our internal systems (Jira/Confluence, GitHub, Sentry, our own product data), so answers come back grounded in real context rather than generic. It's become the fastest way for the team to get a sanity check mid-ticket.From Intercom, we can open a Slack side conversation on a ticket and post a question straight into that channel. That half works perfectly — the question lands, and the agent answers in the thread.The gapOnly human replies flow back into the side conversation in Intercom. The agent's replies never appear. I'm assuming that's because they're pos

We've all spent time improving Fin's knowledge, testing answers and looking at resolution rates.But I'm curious about what happens after Fin actually sends the answer.Say Fin tells a customer:“Yes, you can get a full refund.”…but your actual policy doesn't allow it.Or it gives an answer that sounds completely normal, but contains something your team shouldn't be promising.How are you catching those cases today?Manual QA? Monitors? Sampling conversations? Customer complaints? Or do they mostly get missed?I'm particularly interested in teams running Fin at meaningful volume. How are you handling this in practice?

We connected a POST API to SendGrid within our email workflow to capture FIN's response to users and store it as audit evidence in forwarded emails. However, we haven't found any parameter or attribute that exposes FIN's response text.Is there a way to create a custom attribute to pass this value, or do you recommend an alternative method to capture it? ​@Nathan Sudds 

Hey everyone 👋As Fin handles more customer conversations, I'm curious how teams are checking the actual responses being sent to customers.Not just whether Fin resolved the conversation but whether the response itself could create a problem.For example:promising a refund or exception that isn't actually allowedgiving incorrect pricing or policy informationmaking a commitment the business can't honorexposing sensitive customer informationgiving inappropriate financial or medical advicemissing an important AI disclosureThe difficult cases aren't necessarily obvious. A response can look perfectly reasonable on its own but become risky when you look at the customer's situation and the company's policies.How are you handling this today?Manual sampling? QA? Fin's monitoring tools? Internal rules? Something else?I'm working on this problem myself and would genuinely like to understand how other Intercom teams are approaching it.

Fin doesn't read your knowledge base the way a person does. It reads structure, tags, and outcomes. Get those wrong during migration, and resolution rate suffers from day one — even if every article technically "moved."Across Intercom's customer base, Fin averages a 67% resolution rate. Top-performing teams hit 80% or higher. The gap between those two numbers usually comes down to migration quality, not the model.How Fin actually reads your knowledge baseFin doesn't treat your help center as a flat pile of articles. It uses hierarchy — Collection, Section, Article — to understand scope and context before it answers.Most help desks only support a three-level structure. Intercom allows a simplified Collection > Article path with no Section in between. That's fine natively. It's a problem in migration: if your source platform's categories don't map cleanly onto that structure, a migration tool has to guess, usually by dropping orphaned articles into a default folder. That default bucke

I just published a new Support Stack Solo episode showing how I built my first Fin Procedure inside Intercom.Instead of just answering questions, this procedure turns Fin into a qualification concierge for my website. It asks a few questions, stores attributes, scores the lead, and routes them to the right next step.If you're experimenting with Fin Procedures or AI workflows, this is a practical walkthrough of the full setup (including what broke during testing).Watch it here: Support Stack Solo E03: How to Build a Fin Procedure in Intercom (Real Qualification Concierge)

Sonalika Worldtrac 90 is a 90 HP tractor designed for demanding agricultural applications and multipurpose farm operations. This 2WD tractor is equipped with a powerful engine and a 4-cylinder configuration as listed on the KhetiGaadi model page. It can be used for activities such as ploughing, harvesting, rotavation, trench digging, and other agricultural operations. The model page lists a 4087 cc engine, 2200 RPM engine-rated speed, power steering, oil-immersed brakes, and 76.5 PTO HP. It comes with 12 forward and 12 reverse gears, providing multiple speed options for different operating requirements. The tractor also has a hydraulic lifting capacity of 2500 kg and a 540 RPM PTO. The Sonalika Worldtrac 90 price is listed at approximately ₹11.5 lakh to ₹13 lakh ex-showroom on KhetiGaadi. Prices may vary depending on location and applicable charges. Farmers looking for a high-power tractor can explore its engine performance, transmission, hydraulics, PTO, steering, braking system, and

Sonalika GT-26 4WD is a 26 HP compact tractor designed for different farming applications. This 4WD model comes with a 3-cylinder engine and is suitable for activities such as ploughing, spraying, cultivation, rotation, and threshing. It is also positioned as a mini tractor option for farmers looking for a compact machine for specific agricultural requirements. The Sonalika GT-26 4WD features a 1318 cc engine with a rated speed of 2700 RPM. It has a 13.4 HP PTO, power steering, oil-immersed brakes, and a sliding mesh transmission system. The tractor also comes with 6 forward and 2 reverse gears and a multi-speed PTO, providing flexibility for different implement-based operations. The Sonalika GT-26 4WD price is listed at approximately ₹4.95 lakh to ₹5.7 lakh ex-showroom on KhetiGaadi. The final price can vary depending on the state and applicable charges. Farmers can check the complete specifications, horsepower, engine capacity, transmission, PTO, steering, brakes, and other features

The Sonalika range includes tractors designed for different agricultural applications, from regular fieldwork to specialised farming requirements. Sonalika Tiger models are among the options available for farmers looking for suitable power and performance for different operations. The brand also offers mini tractor models for compact farming requirements. The Sonalika mini tractor price depends on the model, specifications, engine power, features, and location. Mini tractors can be useful for farmers working on smaller landholdings or requiring a compact machine for specific applications. Other popular Sonalika models include Sonalika DI 35, Sonalika 745, Sonalika 750, Sonalika DI 50, Sonalika DI 55, and Sonalika DI 60. Farmers can compare these models according to horsepower, transmission, hydraulic capacity, PTO, mileage, and other specifications. Sonalika tractors can support various operations such as ploughing, cultivation, sowing, transportation, and implement-based farming. Sele

I've been killing my brain thinking about a workaround for this, so maybe someone can help me: My agents are dedicating much time to create Jira issues, so I've been trying to find a way to automate that proccess. The Intercom for Jira or Jira for Intercom integration doesn't work for us because every new issue associates to the person who connected the integration, and not to the agent who registered. Also we can't fill every custom field from Intercom in that way. I tried using Zapier but I can't get all the information I need to create a new issue:People dataCompany dataConversation data  

Hi everyone,We’re using Fin in Hebrew, but we keep seeing cases where Fin adds random English words inside Hebrew replies, even when those words do not appear in our Help Center articles or snippets.We also had a more serious case where Fin inserted Japanese/Chinese-looking characters into a Hebrew customer-facing reply.We already added clear Guidance telling Fin to reply fully in Hebrew and avoid English unless it is a brand name, URL, product name, or a required term. The issue still happens.Intercom Support suggested using Guidance and mentioned the multilingual glossary, but also clarified that the glossary does not apply to AI-generated replies.Has anyone using Fin in Hebrew or another non-English language found a reliable way to prevent this?Is this something Guidance can actually solve, or is this a current limitation of Fin?This is affecting customer trust, so any practical advice would be appreciated.