The Challenge
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.
What They Built
Eventus Advisory Group built the Finance Intelligence Accelerator — a modular, tiered AI-powered FP&A solution for mid-sized firms. It ingests data from QuickBooks, HubSpot, Excel, and other operational systems into a centralized PostgreSQL database, applies classical machine learning for time-series cash flow and revenue forecasting, and layers a GPT-4o-powered natural language chat interface on top so any finance team member can query their data without writing SQL. Power BI or Tableau dashboards surface anomaly detection and variance analysis automatically. The modular structure lets companies start with a data foundation and add forecasting, scenario modeling, and 13-week cash flow modules as they scale.
Eventus Advisory Group designed the Finance Intelligence Accelerator as a modular system allowing clients to start with a data foundation and layer on additional capabilities over time. The first step in any engagement is data integration: connecting QuickBooks Online, HubSpot, Excel, and other operational systems into a centralized PostgreSQL database, resolving schema inconsistencies and data quality issues that frequently undermine forecast reliability in mid-market environments.
Once the data layer was clean, Eventus applied Scikit-learn–based time-series models for cash flow and revenue forecasting, with anomaly detection and variance analysis surfaced automatically through Power BI or Tableau dashboards. A GPT-4o natural language interface was layered on top, enabling any finance team member to query data conversationally without writing SQL.
The modular architecture lets clients start with immediate pain points — board reporting or cash visibility — and add scenario modeling, 13-week cash flow projections, and automated narrative generation as they scale. Change management and user trust were built in parallel with the technical deployment to ensure non-technical finance staff could act confidently on AI-generated forecasts.
AI Role
AI performs time-series cash flow and revenue forecasting using classical machine learning models trained on data ingested from the company's GL, CRM, and operational systems. A GPT-4o-powered natural language interface allows any finance team member to query business data without writing SQL, while automated anomaly detection and variance analysis surface insights directly in Power BI or Tableau dashboards.
AI Model
Custom / proprietary
Infrastructure
PostgreSQL (centralized data warehouse) • Microsoft Azure (cloud hosting) • Snowflake (data warehouse, where applicable) • QuickBooks Online (source financial system) • HubSpot (source CRM data)
Integration Points
QuickBooks Online → PostgreSQL (financial data ingestion) • HubSpot → PostgreSQL (CRM data ingestion) • Excel files → PostgreSQL (manual source data ingestion) • PostgreSQL → Power BI / Tableau (dashboard data feed) • PostgreSQL → GPT-4o chat layer (natural language query interface)
Best for mid-market companies ($15M–$1B revenue) in SaaS, e-commerce, or professional services whose finance teams are running on spreadsheets and disconnected systems and want to move from manual reporting to AI-powered forecasting and real-time decision support — without enterprise-level budgets or in-house data science teams.
