Fin is an AI Agent for customer service. It automates complex queries, improves resolution times, and delivers consistently high-quality support at scale.
$0.99
one-time fee per outcome
IBM watsonx.ai
Score 8.7 out of 10
N/A
Watsonx.ai is part of the IBM watsonx platform that brings together new generative AI capabilities, powered by foundation models, and traditional machine learning into a studio spanning the AI lifecycle. Watsonx.ai can be used to train, validate, tune, and deploy generative AI, foundation models, and machine learning capabilities, and build AI applications with less time and data.
Fin comes with a 90-day money-back guarantee. Here's how it works:
Intercom states that users who sign up for the Fin Guarantee Success Program and do not achieve at least a resolution rate of 65% will be paid $1M. This program is designed for high volume customers.
Eligibility criteria:
High volume customers (over 250k monthly conversions) in North America and Europe. Intercom states that phase one of this program will admit customers on Intercom Helpdesk or Zendesk.
Pricing for watsonx.ai includes: model inference per 1000 tokens and ML tools and ML runtimes based on capacity unit hours.
Fin is well-suited to most use cases as long as it is trained accordingly. With a robust, well-designed knowledge base, Fin can extrapolate information from help articles and provide detailed instructions to an end user without simply sending a link to the article without context. In my experience, Fin is best suited to standard support requests. As the product becomes more complex and product types change, Fin seems less successful at accurately supporting those requests. It is best to maintain Fin's knowledge sources continuously.
I have built a code accelerator tool for one of the IBM product implementation. Although there was a heavy lifting at the start to train the model on specifics of the packaged solution library and ways of working; the efficacy of the model is astounding. Having said that, watsonx.ai is very well suited for customer service automation, healthcare data analytics, financial fraud detection, and sentiment analysis kind of projects. The Watsonx.ai look and feel is little confusing but I understand over a period of time , it will improve dramatically as well. I do feel that Watsonx.ai has certain limitations from cross-platform deployment flexibility. If an organization is deeply invested in a multi-cloud environment, Watson's integration on other cloud platforms may not be seamless comported to other AI platforms.
It seems some users really struggle to figure out how to escalate to a human (especially through email).
Not excited about how "soft" resolutions still count as resolutions and are paid for. Though some abandoned cases appear to be able to be concluded as "the user got the answer they needed", there are others where they clearly didn't, because they just open up another chat (or even more), trying to get more info. This pads the resolution stats and makes it seem more effective than it actually is.
Cost -- Fin is quite expensive. It helps us with scaling coverage, but we're not really saving money.
We have been and will be continuing our journey with Intercom and nothing too concerning has happened that I have experienced or heard of that has us on the edge yet. If it ever happens it will be something along the lines of "Outgrowing" the use of need of the platform.
I still don't have enough experience, but i have seen a lot of demos and i have made some real world scenarios and so far so long every thing looks fine. I was at IBM Think 2025 and IBM TechXchange 2025 and the labs were really usefull and simple to understand.
Much improved and improving feature set, but overall navigation of the interface and platform is cumbersome. While it's worthwhile to continue building out new functionality and features, there should be some effort to make overall usage more intuitive. Becoming an admin requires either months of daily usage or more complex training.
I needed some time to understand the different parts of the web UI. It was slightly overwhelming in the beginning. However, after some time, it made sense, and I like the UI now. In terms of functionality, there are many useful features that make your life easy, like jumping to a section and giving me a deployment space to deploy my models easily.
I can get help by asking Fin questions about itself. It answers accurately, citing its own Help Center resources with visuals. It can reason and dialogue well. But when it comes to getting human support for Fin, it is not as quick. It can sometimes take a few days. They are polite and well-meaning. Some things aren't their fault (product limitations), but there was one occasion where something took a long time to resolve with lots of back and forth but it was I who found out the error in the end that they missed, so they didn't really help resolve it.
I still don't have enough experience, but i have seen a lot of demos and i have made some real world scenarios and so far so long every thing looks fine. I was at IBM Think 2025 and IBM TechXchange 2025 and the labs were really usefull and simple to understand.
Our previous support platform didn't offer an AI agent (they used it internally for their own tickets, but there were delays with shipping it to their users, i.e., us, so we migrated to Intercom). Our colleagues from different brands within our own company demoed a different chat-only tool, but it looked much less impressive than what Fin offered, so we haven't considered it either.
IBM watsonx.ai has been far superior to that of Chat GPT AI. the UI elements prompt responses and overall execution of the AI was much better and more accurate compared to the competition. I can not recommend using this platform enough. Great job IBM. I hope the team behind this project continues to grow and prosper.
I still don't have enough experience, but i have seen a lot of demos and i have made some real world scenarios and so far so long every thing looks fine. I was at IBM Think 2025 and IBM TechXchange 2025 and the labs were really usefull and simple to understand.
New role opportunities — Using the “Fin-first” approach has reduced the workload for our Tier 1 team, giving them more time to focus on their own career growth. It’s also opened the door to a dedicated, AI-focused role, where a team member regularly reviews Fin’s answers and makes updates to help it perform even better.
Enabling Fin has also reduced our response time and allowed us to meet SLA's.
