Mid-market finance teams — companies ranging from $15M to $1B in revenue — are buried in disconnected spreadsheets and manual reporting cycles. Without integrated data or forward-looking analytics tools, they can’t afford enterprise planning platforms or a dedicated data science team, yet they still need strategic forecasting and board-ready reporting. Without change, they remain reactive scorekeepers, unable to anticipate cash risk or give leadership the real-time insight needed to make confident decisions.
Eventus Advisory Group built the Finance Intelligence Accelerator — a modular, AI-powered FP&A platform for mid-market companies ($15M–$1B revenue). The system centralizes data from QuickBooks, HubSpot, and Excel into a PostgreSQL database, applies ML-based time-series forecasting for cash flow and revenue, and layers a GPT-4o chat interface so any finance team member can query data without SQL.
Eventus began by mapping the client's existing data landscape — QuickBooks for financials, HubSpot for pipeline, and Excel for supplemental planning data. A PostgreSQL database was established as the central warehouse, with ETL pipelines pulling from each source on a defined cadence.
The forecasting layer applied ML-based time-series models to cash flow and revenue data, calibrated against the client's historical actuals. Forecast outputs were surfaced through Power BI and Tableau dashboards pre-configured for the CFO and board reporting cadence.
A GPT-4o chat interface was layered on top, allowing finance team members to ask plain-language questions — cash position by entity, variance to forecast, scenario comparisons — without needing SQL or BI tool training. The interface was scoped to read-only queries against the warehouse to prevent data mutation.
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