Spotlight: FinQore

We use MotherDuck for everything on the front end, and we’re pulling it into more parts of our app as we move forward to systematically replace some of the datasets we're pulling from Postgres. The power of what we're doing is about how we extract data from multiple source systems, bring it together, transform it based on business-approved logic, and automate continuous data orchestration. This enables us to create highly accurate and deeply segmented data sets.

When working with revenue data and finance data at large, you want to be able to analyze, explore, and operationalize it instantly. To do any of those things, you need to have clean data that's actually usable. When they come to us, our customers are pulling information from multiple systems and then hashing it together in Excel (even established and fairly sophisticated businesses). The lack of automation results in a challenging manual process, especially for finance teams at businesses with highly complex revenue models that encompass a variety of revenue streams, products, channels, and business units.

To help automate the process, we establish connections to our customer's multiple source systems, funneling data into our private data lake. From there, using MotherDuck, we process and unify the data within a bespoke, code-generated pipeline. MotherDuck helps supercharge data processing, enabling the creation of a single, daily-updated, reliable source of financial data for the entire organization. This eliminates reliance on manual processes and ensures everyone has access to accurate data–going beyond just assisting finance teams with end-of-month analysis and reporting.

With MotherDuck and DuckDB, we don't have to worry about performance. This flexibility enables us to deliver new functionality, such as our FinQore metrics explorer. It’s nice and snappy, but more importantly – due to our semantic data layer built on top of MotherDuck – we were able to launch specially trained AI Agents. These agents use real-time Retrieval Augmented Generation (RAG), leveraging the performance of MotherDuck and DuckDB. We’re so excited about upcoming MotherDuck features, such as database versioning. Looking ahead, we plan to operate all of our analytical workloads using MotherDuck. We made this bet because it accelerated our vision years into the future.

Build insanely interactive data apps

If you’re interested in building applications with the MotherDuck WebAssembly (Wasm) SDK, please connect with us or join the community - we’d love to learn more about your use case and what you’re building!