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Our use case involves leveraging it for tasks such as generating document summaries with keyword detection after scanning. Additionally, we employ image augmentation based on image descriptions. For advanced language analytics, we analyze the frequency of specific keywords and business terms within documents, ultimately ranking the documents based on these criteria.

It provides the most valuable external analytics. Whether I'm searching for patterns or conducting keyword-based analytics, I consistently achieve better accuracy with Vertex compared to OpenAI from Azure.

While it employs chat activity for answering queries, the available options might be somewhat limited. I've noticed that using chat activity often presents a broader range of options and insights for a well-constructed question. Improving the knowledge base could be a key aspect for enhancement—expanding the information sources to enhance the generation process.

We have been using it for the past three months.

I find it easy to both scale out and scale up. I don't foresee any issues when the user volumes increase; the Google Platform automatically handles it, which is helpful.

Google's support is impressive. They are highly responsive and go the extra mile to assist users in trying out and ensuring they stand out in the market. I would rate it ten out of ten.

Positive

Previously we were using Azure OpenAI. In certain aspects, Azure outshines Vertex, particularly in total text analytics and, more notably, in multi-language capabilities. However, when it comes to generating content from a robust knowledge base, OpenAI is my go-to because of its extensive knowledge repository. The choice between platforms depends on the specific use case at hand.

The initial setup was easy, especially for someone with a technical background like myself. It didn't require much effort, and the abundance of documentation made the process smooth.

Our current activities involve proof of concept and piloting. Currently, our pilot phase appears to be successful, and we are examining volume metrics to determine the next steps for transitioning into actual production deployment.

As a product company with implementations spanning eight different platforms, we plan to offer this specific feature selectively to enterprise-grade customers based on their needs. I'm confident that there's substantial value addition for end customers using it, leading to a significant return on investment.

Overall, I would rate it eight out of ten.

We are looking to develop HR agents on it, HR-based bots. We integrated it with our HR system, HRIS.

The development toolkit itself and the engine that supported the agent development was flexible. The features include support for RAG and support for generative AI. What we're looking to do is provide a human-like interface where natural language comes into play to serve HR data. Our users interact in a narrative fashion and get their queries answered. It has increased our serving of HR requests by 30%. It is user friendly, and our user base was able to work with it quite conveniently.

Improving on the development toolkit would help. The platform and toolkit that we use to develop agents could benefit from improvements from a user-friendliness perspective. Making it more user-friendly would be beneficial.

I have been using watsonx.ai in the last year. I have done some training on it and completed some POCs and demos. I have worked on watsonx.ai for development purposes.

The platform was easy to adopt, though it took some time, but we did not hit any major roadblocks. Our user base found it friendly to work with. Our internal customers were happy with the deployment and the interfacing they had to do with the experience. From a development perspective, our developers were comfortable developing on the platform. They did not raise any significant concerns.

It has been stable so far.

From a scalability perspective, it is median.

We had to acquire some training on it. We needed to build skills before using it.

It was relatively straightforward. We haven't extensively tested it compared to other platforms, but for now it has helped serve our purpose.

I have recently started working with HubSpot. We were expected to work with ServiceNow and Salesforce, but that did not come through. I work on Office 365, but I have a bit of exposure to Azure.

This solution is highly recommended. On a scale of 1-10, I rate watsonx.ai a seven out of ten.