How a PE Distributor Cut Board Packs to 4 Hours With AI
A finance team burned four days a month stitching three systems into the board pack. An AI now builds it in four hours — and the PE sponsor sees KPIs in real time.
4 days → 4 hrs
Cut from month-end board pack time
The Challenge
The finance team was spending three to four days every month manually pulling data from three separate systems, ERP, CRM, and ops, to produce the board pack. Between reporting cycles, the PE sponsor had no visibility into how the business was actually performing. Leadership was making decisions on data that was weeks old, and the CFO was burning senior hours on work that should have been automated. When the sponsor asked for an updated view mid-month, it meant another two days of manual extraction. The trigger was a board meeting where actuals were materially off from what the monthly pack had shown, and nobody had seen it coming.
What They Built
An automated management reporting system that pulls live data from the company's ERP, CRM, and operations platform into a single dashboard. The board pack now auto-generates on a scheduled cadence. KPI alerts fire automatically when performance deviates from plan - revenue, margin, fulfillment rate, headcount costs - so the PE sponsor and CFO see exceptions in real time rather than at month end. No new software purchased. Built on existing data infrastructure with a lightweight reporting layer on top.
The engagement ran as a fixed-scope, six-week build with a single point of contact throughout. In weeks one and two the team audited all three source systems - ERP, CRM, and operations - mapping data fields and surfacing gaps and inconsistencies in how each team had been recording data. Weeks three and four went to building and validating the automated pipeline, reconciling historical data against prior manual board packs to confirm accuracy. In week five the team built the dashboard and alert logic, reviewing thresholds with the CFO and the PE operating partner to lock the metrics that actually mattered - revenue, margin, fulfillment rate, and headcount costs. Week six ran one full month-end cycle in parallel with the old manual process to validate before full handoff. The hardest part was data quality: fields meant to match across ERP and CRM did not, because different teams had entered data differently for years, and the new dashboard exposed inconsistencies nobody knew existed. The team's takeaway was to lead with a dedicated data-quality audit and to bring the sponsor, not just the CFO, into metric definition early.
AI Role
The system continuously ingests data from the ERP, CRM, and ops platform, normalizes it against a unified schema, flags anomalies and KPI deviations against plan thresholds, and auto-populates the reporting templates the board and sponsor actually use, routing alerts to the right people before anyone has to ask.
Infrastructure
Client's existing ERP system • Client's CRM system • Operations platform • Existing data infrastructure reused with no new software purchased; a Metabase reporting and dashboard layer added on top
Integration Points
Python ETL (pandas, SQLAlchemy) connecting ERP, CRM, and operations systems into a unified schema, orchestrated by Apache Airflow • Scheduled auto-generation of the board pack on a set cadence • Automated anomaly detection and KPI alerts routed to the CFO and PE sponsor when metrics breach plan thresholds
Impact
Month-end board pack production time, every month
PE sponsor visibility into portfolio KPIs between board meetings, replacing a monthly snapshot
Time from kickoff to full production deployment, fixed fee
Implementation Complexity
Best Fit For
PE-backed distribution, logistics, or light manufacturing companies in the 200-1000 employee range where the finance function is still largely manual and the PE sponsor is frustrated by reporting lag between board meetings. Particularly high-fit where the company is running multiple disconnected systems that nobody has connected yet - ERP, CRM, and ops data all in separate places. Less useful for companies that already have a modern data warehouse or a dedicated FP&A team with existing BI tooling.
