What is our primary use case?
My main use case for Gretel.ai is data generation and dataset generation.
Additionally, my main use case with Gretel.ai involves a production database that contains millions of data points. We need that data for testing but must hide the original data because it has compliance issues. The traditional approach involves getting the data from the database and manually updating it. This is why we use Gretel.ai. It generates synthetic data and tests data settings, providing faster testing and better security with no exposure of customer information to vendors. The business impact includes faster development because we need realistic data for AI training, resulting in faster releases and reduced waiting time along with better compliance.
What is most valuable?
In my opinion, the best features that Gretel.ai offers are faster performance, better accuracy, and lower computing costs.
When I mention better accuracy and performance, I refer to improved model accuracy. Whatever data we are getting has rare scenarios. Gretel.ai can detect those scenarios and generate more examples for training, which reduces manual effort from that perspective, meaning it has better model accuracy and performance.
Besides that, I want to add that it has better testing and is faster. Compliance with regulatory actions is also a benefit because data sharing is advantageous.
Since using Gretel.ai, the positive impact on my organization includes a reduction in manual effort and infrastructural cost in training the AI model due to compliance issues, resulting in faster development and reduced waiting time.
What needs improvement?
Gretel.ai might need to enhance security purposes and provide better explainability on the use cases we are developing, including the addition of domain-specific templates and risk metrics.
Regarding needed improvements, I believe that addressing privacy risks or implementing anything that can deliver faster results is essential.
For how long have I used the solution?
I have been using Gretel.ai for four months.
What was our ROI?
The cost reduction amounts to daily savings, as two to three people sit on modifying the data to hide the original details of customers or employees. Two to three employees' full-time equivalents have been reduced, which results in a save of around $3,000 per day.
What other advice do I have?
Gretel.ai is not for everyone. If you are handling sensitive data that can be used for AI and ML models, that is where you can use it. QA teams struggle with frequent data sharing with external vendors, which Gretel.ai reduces.
Before concluding, I believe it is not for every organization due to costs. Small startups with limited data cannot use it. Organizations without AI also cannot apply it. For web applications or larger data, they can adopt Gretel.ai.
Since using Gretel.ai, the positive impact on my organization includes a reduction in manual effort and infrastructural cost in training the AI model due to compliance issues, resulting in faster development and reduced waiting time.
I would rate my overall experience with Gretel.ai at an eight out of ten.
Which deployment model are you using for this solution?
Public Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
