Tonic.ai team cares about their customer and wants to see them suceed

Tonic.ai for us, enables us to test on anonymized data with reference data architecture challenges. I think while the concept is simple, the support is what makes our engagement successful.

Before Tonic, we needed an effective process in place for data provisioning to our dev and staging environments. Now, it's a straightforward process, taking about five minutes for jobs to run. For local development, our developers pull from a container image that's a de-identified production version and refreshed after every release. For staging, our QA team can refresh data on demand whenever they need an update. It gives our QA team and our engineers confidence in testing features. Before Tonic, we would see critical issues at least once a week that were tied to being unable to test our features accurately under real-world scenarios. We haven't had a critical issue since we fully operationalized Tonic into our software development life cycle. That was ten months ago. The ease of setting up Tonic and managing the service ourselves is great. We knew we could plug this into our infrastructure and set it up way easier than writing our own scripts. In terms of security and compliance, the ease of being able to identify sensitive fields and apply the appropriate generators to those fields so that we aren't missing anything was a big motivator for us. In terms of accelerating our workflows, Tonic drastically reduces the time it takes for a full regression test for all of our core features. Before, it was within two weeks for QA to get the data set up; now, they are ready to go and have tested all of the core features manually within half a day. It's been such a great product to use. It's been huge for us, from getting it set up and the initial configuration to the support that we get to operationalizing. I have yet to find any other platforms that do what Tonic does.