How Finqore Transformed 8-Hour Financial Data Pipelines into 8-Minute Automated Workflows
Our data pipelines used to take eight hours. Now they’re taking eight minutes, and I see a world where they take eight seconds. This is why we made the big bet on DuckDB and MotherDuck. It’s only possible with DuckDB and MotherDuck.
Jim O'Neill
Co-founder and CTO
FinQore provides CFOs and the teams that support them with clean, organized, explorable data that is AI-ready to free them from the chaos of monthly and quarterly reporting cycles.
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.
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FAQS
What is a revenue intelligence platform?
Revenue intelligence software hooks into your billing platforms, ERPs, and CRMs and pulls financial, customer, and product data into one structured layer. Finance teams get to see what’s actually happening with revenue instead of exporting CSVs and fighting with spreadsheet reconciliation every month.
FinQore does this by building what they call a "revenue cube" — a segmented financial data structure organized around each customer’s specific business logic. The cube refreshes daily and feeds into their reporting, analysis tools, and AI agents that handle forecasting and KPI monitoring. If you’re building something similar, the customer-facing analytics guide covers how teams are embedding live analytics into their products.
How does FinQore’s revenue intelligence platform work?
FinQore’s platform connects to your billing, ERP, CRM, and product systems, pulls that data together, transforms it, and slices it however your business needs. They call their core data structure a "revenue cube" — a single layer combining financial, customer, and product data so everything is accurate and segmented in one place. They run AI agents on top for forecasting, board reporting, KPI tracking, and letting people query the data directly. Their pipelines run on MotherDuck, which cut execution time from 8 hours to about 8 minutes. FinQore built their front-end data experience using MotherDuck’s Wasm SDK for app developers.
Why do finance teams still reconcile revenue data in spreadsheets?
Most finance teams end up pulling data from multiple systems and reconciling it in spreadsheets, even at fairly sophisticated businesses. Without automation, teams are stuck doing manual work, and it gets worse when revenue comes from multiple products, channels, and business units. FinQore connects to each source system, collects the data in one place, and runs it through an automated pipeline. The result is one source of financial data for the whole organization, updated daily. For teams starting to feel the limits of their current setup, this guide on outgrowing Postgres for analytics walks through the signs and what comes next.
Can AI agents work directly with revenue data?
FinQore built AI agents on top of their revenue cube. The main one, Qori, acts as an AI financial analyst — it generates forecasts, assembles board-ready reports, tracks KPIs, and answers questions about revenue data through a conversational interface. FinQore runs retrieval augmented generation on MotherDuck and DuckDB, so Qori’s responses draw from actual, current numbers rather than generic model outputs. The team calls these "specially trained AI Agents" using "real-time RAG, leveraging the performance of MotherDuck and DuckDB." For more on how this architecture works, see structured memory management for AI agents with DuckDB and how MotherDuck’s MCP server connects AI agents directly to your data warehouse.
