Real problems. Real solutions.

📋 Challenge

This regional equipment distributor operated across 12 locations. Each morning, operations teams manually pulled order status from three separate systems, consolidated the data in a shared spreadsheet, and emailed reports to regional managers.

This daily task consumed 16 hours of admin time per week. Reports were often delayed, occasionally incomplete, and different regions used slightly different metrics—making it hard to compare performance across locations.

🔧 Approach

We designed an automated pipeline that extracted order data from all three systems nightly, unified definitions across regions, and built a real-time dashboard accessible to managers.

• • Set up cloud warehouse for historical data
• • Automated daily data refreshes with error alerting
• • Built region-consistent metrics into the foundation
• • Trained ops team to maintain pipelines independently

🚧 Challenge

A multi-site field-service company processed hundreds of invoices daily from jobs at customer locations. When invoices didn't match job records—missing quantities, rate discrepancies, or approvals—they got stuck in manual exception queues.

The finance team spent two days per week on exception routing and rework. On bad days, invoices sat in queue for over 48 hours, delaying payment and frustrating field teams.

🤖 Approach

We built an AI-assisted workflow that automatically categorized exceptions, routed them to the right owner, and flagged critical issues.

• • Extracted invoice data and compared to job records
• • AI classification of exception types
• • Smart routing to field supervisor or finance
• • Audit trail and approval gates to ensure control
• • Daily summary reports for management

📈 Challenge

A specialty food wholesaler managed dozens of product lines across restaurant supply, retail, and direct-to-consumer channels. Weekly demand planning relied on sales forecasts, but many sales records were incomplete or came from manual spreadsheets.

Data completeness ran at 61%. Planning teams couldn't confidently forecast, leading to frequent stock-outs of popular items and excess inventory of slow-movers. Margin pressure mounted as they couldn't optimize mix.

🔌 Approach

We unified sales data across all channels into a central warehouse and added validation rules to catch missing or inconsistent records at source.

• • Integrated sales feeds from ERP, e-commerce, and channel partners
• • Built data quality rules for missing fields and outliers
• • Automated reconciliation of channel-partner data
• • Created consistent product hierarchies across systems
• • Exposed clean demand signals to planning systems