IBM watsonx Orchestrate vs. Fin by Intercom vs. Rasa

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.

A prospect lands on my site to ask about building profile sizes, wind/snow ratings, installation timelines, or warranty coverage.
What Fin does well is deliver instant, consistent answers, pull from approved specs and positioning, and keep the conversation moving without human involvement.

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Rasa Pro is well suited for corporate use and for chatbots which require backend connections. Smaller chatbots with a few flows might be better served with a simple dialogue engine and custom AI agents, or Rasa Open Source. Rasa does not come with its own complex vector database, just in-memory FAISS and connectors to external vector DB's such as Milvus and Qdrant. It provides only a basic document parser and embedder for FAISS. If you need to build a RAG focused chatbot around a large knowledge base with complex documents, e.g. lots of MS Word or PDF files, you'll have to build a separate document parser and embedder, as well as your own semantic search engine

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• 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.

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• 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.

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• Rasa team has Top notch AI knowledge
• Greate customer support, by listening towards the clients needs.
• And building future proof solutions around client Business Requirements within dazzling timeframes

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• 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.

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• 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.

• No-code apps could be improved
• Online docs can be messy
• Steep learning curve

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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.

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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.

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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.

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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.

With the help of dedicated team - documentation and video resources it is relatively easier to build. We prioritized pro-code usage to begin with launch.

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always there

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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.

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works perfect

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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!).

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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.

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Rasa support has been very responsive, trying to fix any reported issues ASAP. They've also listened to many requests for improvement. The Rasa features and changelog are well documented

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Excellent course material.

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Easy to know the learning path

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Overall the implementation was simple.

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

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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.

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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.

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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.

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• 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.

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• 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.

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• Cost Savings & Efficiency
• Increased Conversion Rates
• Improved Customer Satisfaction
• Operational ROI

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