IBM watsonx™ Code Assistant for Red Hat® Ansible® Lightspeed demystifies the process of Ansible Playbook creation through generative AI-powered content recommendations. Purpose-built to accelerate IT Automation, the product is designed to deliver automation content recommendations for an enhanced Ansible experience.
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Fin by Intercom
Score 8.7 out of 10
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Fin is Intercom’s AI Agent for customer service, designed to deliver high-quality answers, even for complex queries. It works with any helpdesk, or it can be paired with Intercom’s next-generation Helpdesk to get the full Intercom Customer Service Suite.
$0.99
one-time fee per outcome
Pricing
IBM watsonx Code Assistant Portfolio
Fin by Intercom
Editions & Modules
No answers on this topic
Fin with your current helpdesk
$0.99
one-time fee per outcome
Copilot add-on
$35
per month per user
Pro
$99
per month For analysis of 1,000 conversations
Fin with Intercom’s Helpdesk
from $39 + $0.99 per Fin outcome
per month per seat
Offerings
Pricing Offerings
IBM watsonx Code Assistant Portfolio
Fin by Intercom
Free Trial
Yes
Yes
Free/Freemium Version
No
No
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
No setup fee
No setup fee
Additional Details
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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.
I would recommend for understanding your Mainframe components not for the GenAI piece involved from just my experience. The explanations were not up to the quality we wanted but its deterministic side provided a lot of value for different members of my team. The visuals would be great. I am not sure where it currently stands
Fin is great for using for first line support. We use Fin for conversations where customers have a standard question, and Fin is able to pull from our content to answer this accurately and go above and beyond to include some basic problem solving. This really helps us free up our Support Team's time for more complex queries. Fin isn't appropriate for us on technical issues or conversations which require human support. We've had to remove Fin from interacting on these conversations as customers were becoming frustrated with speaking to AI, or having Fin be unable to problem solve. However, this was easy to set up through Intercom, and now customers with complex questions/situations or bugs/technical issues do not engage with Fin and Fin only handles suitable conversations.
It can automatically revamp specific parts of the COBOL code and very useful when we want to maintain the existing codebase but improve its structure. I can highlight a block of COBOL code and use Watsonx Assistant to suggest ways to simplify and optimize it.
Legacy codes, mostly written in COBOL, are cryptic and difficult to understand. Watsonx Assistant analyzes the code and provides insights into its functionalities and dependencies. A great help when working on older applications where understanding the codebase is crucial.
A step-by-step approach to modernize our applications slowly and steadily, so that we can control the process better. I don't have to change everything at once. Instead, I can focus on specific COBOL modules and automatically convert them to Java.
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.
From an administration standpoint, Fin is very easy to set up, train, and test. Having the ability to impersonate a user in our system to see how Fin responds is huge. It lets you test several situations and throw curveballs at it (as customers will) without the risk of setting Fin live and wondering what will happen. It's also easy to fine-tune. Some chatbots you can never quite get right without spending hours on, but Fin usually takes a few minutes to dial it in. From a customer standpoint, Fin couldn't be easier to engage with. We tell customers up front it's an AI bot and they're wow'd with the experience
Intercom is the premier customer support/engagement model and it definitely has one of the top tier customer support teams as well. I don't think I have ever waited more than 5 minutes to get the information I need or get help with an issue. They are incredible and I aim to model our customer service department after them.
Security is very important in the mainframe world. At Watsonx, we work in the trusted Z environment, which has strong security rules, stricter than those of other cloud-based solutions. My domain is primarily mainframe modernization and Watsonx Code Assistant for Z is specifically used to understand and work with COBOL, the language used majorly in mainframe environments, not any general-purpose language that used in various platforms. It understands the nuances of COBOL and Assembler specific to the Z environment, something crucial for my work.
We only used the free live chat version from HubSpot, so Intercom is yards better! If I were just comparing an actual live chat between the two tools, HubSpot was often clunky and delayed, and it was hard to find past conversation information
While manual review and adjustments are still needed, it's a 50-70% reduction in manual coding. Think about it - a project estimated to take a year is done in 4-6 months.
We've been able to introduce new features and improvements more quickly by updating our technology faster. One relevant example is we recently released an important update to our main product 45 days earlier than planned.
It has been a smart move and it's really paid off for our company. We've cut down a lot of time we used to spend doing things manually. We now spend our resources more wisely, work faster and finish projects sooner and as a result, we've reduced our development costs by 25%.
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.
