IBM® watsonx™ Orchestrate® leverages AI to automate complex workflows. The solution helps build, deploy, and manage AI assistants and agents. It offers a catalogue of pre-built agents and tools, low-code agent builder, multi-agent collaboration capabilities, and integrations with enterprise apps.
$530
per month
Fin by Intercom
Score 8.7 out of 10
N/A
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
OpenAI API Platform
Score 9.1 out of 10
N/A
The OpenAI API platform provides a simple interface to AI models for text generation, natural language processing, computer vision, and other purposes.
$0
per 1K tokens
Pricing
IBM watsonx Orchestrate
Fin by Intercom
OpenAI API Platform
Editions & Modules
Essential
$500
per month per subscription
Essentials
$500
per month Per subscription
Standard
Enterprise
Standard
Enterprise
per month Per subscription
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
Ada
$0.0008
per 1K tokens
Babbage
$0.0012
per 1K tokens
Curie
$0.0060
per 1K tokens
Davinci
$0.0600
per 1K tokens
Offerings
Pricing Offerings
IBM watsonx Orchestrate
Fin by Intercom
OpenAI API Platform
Free Trial
Yes
Yes
No
Free/Freemium Version
No
No
No
Premium Consulting/Integration Services
Yes
No
No
Entry-level Setup Fee
Optional
No setup fee
No setup fee
Additional Details
IBM watsonx Orchestrate can be deployed and run on IBM Cloud, AWS, or on-premises. Prices shown are indicative, may vary by country, exclude any applicable taxes and duties, and are subject to product offering availability in a locale.
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.
It focus on enterprise level flexibility and it also provides us cloud and on prem both support which helps in integrating with legacy and modern systems. It has better compliance standards than other competitors. Better data governance and cost effective AI model which is very …
The only reason is that we have been using some other products of IBM, and the trust on their company, so we chose to use IBM Watsonx Assistant, that's it.
Verified User
Project Manager
Chose IBM watsonx Orchestrate
IBM Watson Assistant has a competitive cost structure, is easy to integrate with Intercom, and is more scalable as we look to future enhancements such as speech-to-text, document upload/analytics, and natural language processing across multiple languages.
All tools that we used in the past, have a lot of problems on the chat app, our the syncing data between integrations, Intercom has too, but is very less than others.
In our case, it is well-suited for workday integration, which allows us to automate the entire workflow. However, we are still working on the O9 platform integration, which we feel is less appropriate, and integrating the workflow into the platform.
FIN is great if you need someone to direct the customers based on their problems. You also have the option to use multiple languages if you have a worldwide customer base, so that's great. If you provide enough documentation to feed it, FIN can also solve tickets on its own, which enables your team to focus on other tasks. You can also have him handle conversations in other applications, such as Discord or Slack, and have them create ticket issues in JIRA if your team uses it.
For smaller organizations that run lean and would like to get to deploy a solution quickly. This is a solution that is easy and quick to develop. It has a good amount of customization. However, for advanced customization this might not be a good solution. I suggest experimenting with OpenAI API and then if the experimentation is successful then it is a good idea to optimize and try other LLM models.
New and improved natural language processing yielding better results helps the assistants understand the intention behind the query.
Preserves context of communication, allowing the customers to establish inquiries on the website and continue on the mobile app without having extra informational input.
Intelligent conversations mean that complex paths that are branched based on the user's inputs allow for a much more natural flow of the conversation than fixed scripts.
I think that it needs to be able to integrate better with the knowledge catalogs. It currently provides a default database, which isn't quite large enough for enterprise use. We can connect that then to an external source, but it'd be nice if we could able just to instantiate one straight away.
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.
Currently we are using to develop chatbots based on client provided flow what kind chatbot required for client either button or free text chatbots. we will decided accordingly flow and develop chatbot using IBM Watson. We will integrated custom components if required which is not present in library. Action flow and dialog flow we are currently in chatbot.
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.
With the growing use of AI and chatbots, it's very easy to use, and the conversational language makes it easier than keyword searches in a document. The contextual language processing is impressive. It's easy to integrate into our internal portal. The use of this tool would depend on each company's security and data sensitivity.
The core experience is solid but the key friction across our team is that there are so many new features for improving Fin are being launched. Content improvements, guidance settings, recommendations, trends, and monitors are each useful in isolation, but they sit across separate areas of the platform with no clear starting point. The recurring feedback from my team is that it is hard to know where to focus. A consolidated "Improve Fin" section could really improve the experience, the ideal option would be a training page where our team could improve Fin in one place, ideally by answering questions and Fin would then be able to add those details in the right place, where it's creating new guidance or building procedures. I feel that would make uptake a lot quicker.
Easy to setup, develop and deploy. The payload for the API is simple and has all the inputs required for simple projects. There are a good number of options of LLM models to optimize for speed, cost or quality of the answers. A larger token input might improve the overall usability.
To develop chatbots based on client provided flow what kind chatbot required for client either button or free text chatbots. we will decided accordingly flow and develop chatbot using IBM Watson. We will integrated custom components if required which is not present in library. IBM Watson library anyone can easily learn and develop chatbots.
We've rarely had to engage support, but they've always been prompt in responding and very attentive. Support experiences have been extremely positive (but we're mostly happy that we just don't have any cause to routinely need support in the first place!).
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.
Make has more community of workflows to follow that have been redeveloped and are available for download. Selecting WxO is based on our trust level with IBM and the propositions of the Granite model being less biased, more business trained, and the ecosystem allowing for expansion with Assistant and Discovery.
Although we have not utilised a specific chat box like Fin before in other websites, we have used ChatGPT and Claude within our general work. Our Product and Engineering team make use of Devin within Azure Dev Ops to support with their work. However, Fin is the most suitable for what our Support Team requires as this can be integrated into our chat with customers.
Anthropic is only the best for coding and its really really expensive. So, if you're not making a coding app, I would stay away from it. On the other hand, Gemini models are dirt cheap but come with a bit of performance limitations, so i would use it for big volume non sofisticated use cases. The OpenAI API platform excels at providing best in class performance models, at not outrageous anthropic-like pricing.
From past 3+ years I am using IBM Watson in our current project easily can implement and manage and monitor user how their using. Is there and update also just update dialog is just enough to change no need to touch any other templates. Multiple language will support, and action and dialog speak recognize chatbot we can create as per client requirement. Overall, as of now good experience with IBM Watson.
By automating tasks that would otherwise require human intervention, organizations may achieve cost savings in terms of labor, especially for handling large volumes of routine inquiries.
Virtual assistants can handle a large number of simultaneous interactions, making them scalable to accommodate growing customer bases and increasing workloads without a linear increase in staffing.
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.
