My main use case for DataRobot is to give an agentic AI flavor to my different customers because many of my customers are looking for a consumption tool when they are looking to implement GenAI in their premises. DataRobot actually helps to create agents directly, both on-premises as well as on a cloud. We are an on-premises company, so I propose DataRobot solutions when customers are looking to actually integrate AI agents with their infrastructure which they have recently procured from Dell.
We were working with a very large bank and they wanted to have an AI consumption tool where they can build AI/ML pipelines and they needed to have a graphical user interface where they can actually chat with the models which they have imported directly from outside as well as create agents which they can interface with their models as well as their commands. Based on their requirement, we zeroed in on a DataRobot solution because that actually helped them achieve all of their outcomes.
We understood the use case, what the customer is looking to implement and we got up with the DataRobot team. We understood that they could actually cater to all of the requirements of the customer, then we went ahead with the deployment of DataRobot. DataRobot actually helped set up a multi-agent scenario for the customer and one agent talking to the other agent has automated the complete sequence of events of fraud monitoring where if one particular fraud is reported, the second agent can actually log it into the ledger books and that can be reported into the chief manager who can actually take it up where the exact issue is happening. The whole process gets automated.
Previously my customer used to do everything manually, but now they are using agents to actually talk to their models as well as to their financial repository of information which they have brought into the vector database which comes along with DataRobot. They have actually automated several procedures such as updating the ledgers, updating the bank account information, generating feedback about their customer service. Everything is being automated. DataRobot is one of the major platforms being used, which actually interfaces with the primary bank application which they have in the particular bank. Model benchmarking actually helps to make sure that the results which are being provided by the model are correct. They can continuously review whether it has the right results which are being shown to the bank application and that will help them automate all of their remaining use cases which they are currently looking to deploy.
Previously we had five or six processes which used to be done manually by different people and that has been transformed using DataRobot because agents now are doing the same thing. There is a lot of money saved. The manager mentioned that they have redirected the employee base to other tasks and they are incurring a cost savings of around $1,000 per employee and that has actually boosted the share of the company by a lot. Since it is a government PSU bank, we cannot share the financials, but they have actually achieved a lot of cost savings, around $2 million they have saved by implementing DataRobot.
DataRobot's one of the major features is model evaluation and model performance. One can actually evaluate whether the model is performing correctly and can actually benchmark it against the correct results. That will actually help to fine-tune models to give the right results. Additionally, DataRobot has a very good agentic interface where one can actually spin up multiple agents at the click of a mouse and can have multi-agent protocol where that will help automate all use cases. That is something which is a differentiator with respect to other platforms.
DataRobot can actually be improved by having access to multiple data repositories. It is lacking in the ways in which it ingests data, in which it transforms the data because we need a separate data manipulation tool for which we need to have somebody else. It is primarily for agentic AI governance and creating agents on the data or on the models which we have already brought into the platform. If DataRobot also works in that space, we can actually improve their uptake to a lot of customers.
Currently we have an ETL tool which actually does the ingestion, transformation, and then that data is fed into the AI model. The AI model which is connected to DataRobot then gives us the GUI where we can create AI/ML pipelines. However, if DataRobot also adds those data transformation capabilities, then it will be an end-to-end tool and the customer will not have to procure many tools for doing the ingestion and transformation process. It will have a single tool. That is where DataRobot can actually improve itself.
I have been using DataRobot for one year.
DataRobot is very scalable because the customer initially started with two licenses, and now they have around 20 licenses which are working across different deployments across their entire ecosystem and that helps them to achieve their outcomes pretty easily.
Customer support is very good. For many of the issues, we went back to the DataRobot team and they were very helpful in answering the questions which the customer had. There is no issue with the customer support.
Previously we did not have any solution. It was a greenfield setup, so we did not have any solution. Everything was being done manually.
We worked directly with the DataRobot team. We got the licensing worked out for the respective instances which the customer was looking to purchase. The whole process was very seamless and there were no issues in calculating the licensing costs and that is what we conveyed to the customer. It is pretty transparent and we had no issues.
Around $2 million have been saved for the particular company in a particular financial year. We have automated a lot of processes. Previously we had five employees doing the entire workflow, and now we can do it with two employees because agents are being used to do the same which was previously being done by the employees.
We previously also evaluated Dataiku, but we found that DataRobot has a better interface and it has a lot of other skills such as model performance, model evaluation and benchmarking, and agentic AI platform which is better than Dataiku.
DataRobot is primarily an agentic AI tool and it does that pretty well. Based on that, I have given it a rating of eight. DataRobot has not promised to be a data ingestion tool, otherwise I would have given a lower number. It is good in what it promises to be.
Customer support is very good. For many of the issues, we went back to the DataRobot team and they were very helpful in answering the questions which the customer had. There is no issue with the customer support.
If one is looking for an agentic AI governance tool, then DataRobot is the place to be because it has a very good interface and one can create multiple agents on the fly. One can have an agent-to-agent protocol and that will automate processes in a single click. That is where DataRobot scores.
