Credence: a data model built for the age of AI
When we started Credence, we did so to solve our own struggles with a messy data pipeline. It was meant to be an internal tool, but our peers quickly informed us that they too were searching for the solution we envisioned. As we spoke with more and more folks around the industry, we continued to hear some version of the same story:
I cannot get the answers I need when I need them.
The problem we saw was systemic. CRE data is moored in a handful of legacy tools, each with their own incomprehensible, and incompatible, data structures. One tool did not talk to another. Even if the intent was there, the data was often so out of sync that integration was untenable. The industry has been working for decades under the same rules: walled gardens with access to your own data stuck behind a paywall, overly complex systems requiring a team of specialists to implement and maintain, periodic goose chases to pull together meaningful reports.
Our initial product scope broadened when we asked the question:
Does it have to be this way?
Our team knew the answer to be an emphatic "no." We foresaw a different world. One without decades of burdensome tech debt and mountains of unnecessary complexity. One where data access is open, real-time, and interactive. One enabling people to be decision makers instead of doers.
Thus, Credence was born. Designed from Day 1 with the future in mind. Read more about our philosophy here.