We build AI infrastructure. Our platform processes customer ML workloads, training datasets, and model artifacts. Knowing where sensitive data ends up across our environment is not optional for us. It is a business requirement. Matters.AI gave us that answer fast. I check the dashboard weekly and the posture score tells me immediately if anything needs attention. The scans pick up new data stores on their own, classify what is in them, and flag anything sensitive. I do not need to remind anyone to audit anything. It just runs. The classification is sharp. It correctly distinguishes between a model configuration file and a file that contains actual customer data. That distinction matters when you are processing thousands of files across cloud storage. The accuracy has been consistent since day one. The interface is clean. My team did not need a walkthrough to start using it. Findings come with a clear priority and a recommended next step. I hand it off, it gets fixed.Support has been responsive. Setup was done in a day. No disruption to our production workloads.

Andromeda uses its DSPM capabilities to analyze the different data stores used in our product. The data insights and overall UX have been neat and intuitive. Its AI-powered classification and discovery feel like a clear differentiator in the market. The tool helps us identify and classify data, and it also recommends remediation in a way that feels genuinely innovative. Overall, it works as a complementary solution alongside our other security tools and practices.

I handle project delivery for our security and data protection workstreams, and most tools in this space are built for full-time security engineers. They assume you live in the product eight hours a day. Matters.AI is different. It gives me what I need without demanding that I become a security specialist. The first thing I noticed was the dashboard. I land on it and immediately see our posture score, open findings by severity, and what has changed since my last login. No clicking through five tabs to piece together the picture. The layout is intuitive enough that I walked two non-technical team members through it in under fifteen minutes and they were comfortable navigating independently. The intelligence behind the scans is what keeps me confident in the output. The platform figured out which files contained personal identifiers, financial records, and access credentials across our cloud storage and connected applications. I did not have to set up detection rules or feed it sample data. It worked out of the box and the accuracy has been consistent enough that we stopped doing manual spot checks after the first few weeks. Connecting our cloud environment and productivity tools took less than a day. I had budgeted a week for integration based on past experience with other platforms. That time went back into actual project work instead. I specifically tracked system performance during the first month because our engineering team was cautious about anything touching live infrastructure. Zero impact. No latency, no resource spikes, no complaints from the engineering side. From a cost perspective, we replaced a patchwork of manual processes and partial tooling with one platform. The return was clear within the first quarter, both in time saved and in the confidence we now have when answering questions about our data security practices. The team behind the product has been available and responsive throughout. Setup was guided without being hand-holding. Post-deployment, they have flagged configuration opportunities before we asked, which tells me they actually pay attention to how customers are using the product.