cmercury vs. Fin

Fin is fantastic at answering simpler inquiries, where the range and types of questions are easier to categorize. Thereby reducing the subset of possible answers. Where it has shown great improvement - but still needs more improvement - is by becoming a true Agentic AI support engineer that is capable of answering more technically nuanced questions. Our product has a lot of variables used for troubleshooting that cannot be adequately captured in documentation. Even though we provide thousands of pages of spec docs, each issue is unique. Training and empowering Fin to be as good as a Level 1 support engineer is still very challenging.

• It is very easy to train
• It does a great job of recommending answers when little context is given.
• I love the ability to create a snippet to address issues in the moment or in the long term, based on FAQs.

Incentivized

• 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.

Incentivized

The platform is overall clear and intuitive. As with any new platform, there's a learning curve, but that wasn't an issue for our team (and it shouldn't be an issue for others). Fin options are scattered across several submenus, and I'd like them grouped together, but I also like having all those training-related tabs open at all times, so it's not much of a real issue for me.

always there

Incentivized

works perfect

Incentivized

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.

Incentivized

Easy to know the learning path

Incentivized

The implementation was surprisingly easy but we are having to adapt from our old process to work well with Intercom.

Incentivized

There are so many AI platforms available, and you could theoretically build a system using the available AI API's from any of the big platforms. However, I dont think it's as easy as this. Intercom is deliberately built for customer service, the features they are releasing a based on providing the best customer experience. If we were to build this ourselves or to use another platform we would be taking on the upkeep, using Fin is just much simpler as it's also our chosen ticketing platform so anything that Fin is not able to answer yet and escalated directly to our team with no extra effort required from our side.

Incentivized

• 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.

Incentivized