IBM Watson Explorer vs. Perplexity

The Watson Explorer is great because it potentially replaces a meriad of other low-level analytics products that we would need to use for data analytics and data mining. WEX isn't really suitable much beyond doing text and data analytics and performing machine learning, so if your team doesn't really have a use-case that fits all of these categories, it is worth looking at an alternative.

Incentivized

Perplexity is helpful when you want auto-code generation for day-to-day problem scenarios such as Powershell script to accomplish a task, Code to invoke a REST API, Class generation from JSON/XML data, etc. It is also helpful when you want to correct or optimize code that you have self-written. Perplexity might not be best suited for scenarios when you need 100% accuracy without your self-verification of correctness.

Incentivized

• Free to try - It's possible to use most of the useful features of Watson Explore on their trial/demo accounts.
• Super well-designed data analytics tool - Most of the tools and features of the explorer are really useful, and truly help you fully understand the depth of any format of textual data.
• Extensive sources compatibility - WEX can retrieve data from a large range of sources, and the compatibility there is quite good as well.

Incentivized

• Searching across the web
• Programming
• Diversity of answers

Incentivized

• Support is just OK, like most of the other IBM Watson products. The setup/integration is really hands-on, but it's also problematic because support later may take a considerable amount of time.
• UI could still use a little more improvement - part of the administration and sources dashboards are hard to navigate.
• The Application Builder is a great part of the product, but hard to learn/understand - this is where we needed the most support from IBM and tutorials/documentation.

Incentivized

• Pictures generation could be better, other AI tools are better
• Creation of presentations
• Enterprise version for handling confidential (company) data

Incentivized

I think it is very good; some minor tweaks might be helpful.

Incentivized

Google Cloud offers a Natural Language product, but it is just an API. This API doesn't offer the useful visualizations of relations, analytics, and graphs that IBM Watson Explorer offers on their interface. For this reason, we chose to go with IBM WEX. For later stages of our production, we decided to use Google's NLP API because we found that it was quick to integrate into production after studying data and developing models using IBM WEX.

Incentivized

I think they both do well. I use ChatGPT for some pre-created GPTs. So because I use them for different functions, it's hard to compare them head-to-head. I also use an Abacus, which I like a lot. Overall, they are all good, but I have different use cases for each one.

Incentivized

• Positive - Trial/demo period. This was really useful for us to figure out what features of WEX we liked most and how difficult it would be to integrate WEX into our workflow.
• Negative - On-boarding was long and almost always requires support from IBM support, unlike most other products this advanced.
• Positive - WEX replaced a large selection of alternative products we would have to use for the same functionality, and having all of that function in one place was definitely helpful.

Incentivized

• Faster content generation
• Brainstorming
• coming up with new ideas
• solving numerical problems
• Analytic and deep thinking skills

Incentivized