Finance Platform

A finance module that automates the path from source documents to executive insight: OCR-based capture, standardized KPI tracking, forecasting, and reporting, built to remove manual reconciliation from the monthly close cycle.

Finance teams at growing companies spend a disproportionate amount of time on manual data entry and reconciliation — retyping invoices, cross-checking spreadsheets, and rebuilding the same reports every month. That manual work delays the numbers leadership needs to make decisions and introduces avoidable error.

• Automate document capture so invoices and statements don't require manual re-entry
• Standardize KPI definitions so every report uses the same underlying calculation
• Provide forecasting based on historical trends rather than a rebuilt spreadsheet each cycle
• Give executives a reporting view that updates continuously instead of at month-end

Documents are captured and parsed through an OCR pipeline into structured records, which feed a KPI layer with standardized definitions shared across dashboards, forecasts, and exports. Forecasting models run against historical actuals to project forward, and an executive reporting layer packages the results without a manual rebuild step.

• Separated document capture (OCR/ingestion) from KPI computation, so improving one doesn't require touching the other
• Defined KPIs once in a shared calculation layer consumed by every dashboard and export, eliminating the classic problem of two reports disagreeing on the same metric
• Built forecasting as a scheduled, versioned process so historical forecasts remain auditable rather than being overwritten

• Handling inconsistent source-document formats reliably enough that OCR output could be trusted without manual review of every line
• Designing KPI definitions general enough to serve multiple report types without duplicating logic
• Balancing forecast responsiveness (reacting to recent trends) against stability (not swinging wildly on noisy data)

• Added a human-in-the-loop review step for low-confidence OCR extractions rather than forcing full automation on day one
• Built the KPI layer as a queryable data model rather than pre-baked reports, so new report types could be added without new pipelines

• Designed the data pipeline from document ingestion through KPI computation to reporting
• Built the forecasting architecture and its versioning approach
• Owned the executive reporting layer and its data visualization strategy

• Cut manual data entry time on recurring financial documents substantially
• Reduced discrepancies between reports by standardizing KPI definitions in one place
• Moved leadership reporting from a month-end event to a continuously current view

• Full automation of document capture was less valuable than automation plus a fast human review step for edge cases — trust mattered more than speed alone
• Centralizing KPI definitions early avoided a much more expensive migration later, once multiple teams had built reports on inconsistent calculations