Real projects. Measurable outcomes.
These are representative client stories. Names changed for privacy. But the results, the challenges, and the approaches are real. This is what we build.
Northline Components
The Situation
Northline is a multi-location industrial parts distributor. They operate three warehouses and manage inventory across 40,000+ SKUs. Their reporting lived in three separate systems: ERP for inventory, CRM for sales, and a custom database for margin analysis.
Every week, the operations team manually pulled data from each system, reconciled discrepancies, and built spreadsheets for reporting. The process took 12 hours and was error-prone. Inventory numbers in the dashboard didn't match the ERP. Margin calculations drifted over time.
What We Built
- → Centralized warehouse in Snowflake pulling ERP, CRM, and custom database data via nightly ELT pipelines
- → Standardized product taxonomy and inventory models
- → Daily operational dashboards: inventory by location, turnover rates, margin by category
- → Automated data quality checks. Alerts when inventory or margin calculations drift.
- → Team training on dashboards and runbooks for common incidents
Outcome
42%
reduction in weekly reporting effort (12 hours → 7 hours)
Northline's team now gets reliable, daily dashboards instead of weekly spreadsheets. They've caught inventory discrepancies days earlier than they used to. Margin analysis is now continuous, not a Friday manual exercise.
Timeline: 14 weeks (Discovery 2 weeks, Build 10 weeks, Handoff 2 weeks). Team: 2 FTE + Northline's ops lead. Ongoing support: monitoring and quarterly optimization.
Key Metrics
Records Standardized
2.1M
inventory transactions consolidated
Effort Reduction
42%
weekly reporting time
Time to Insight
Daily
instead of weekly
Duration: 14 weeks to live
Systems: ERP, CRM, custom database
Warehouse: Snowflake
Harbor & Field Insurance Services
The Situation
Harbor & Field is a regional commercial insurance administrator. Their core process: clients submit policy packets (documents, forms, declarations). Intake staff manually review, classify (commercial, cyber, casualty, etc.), and route to the right underwriting team.
On average, initial triage took 18 minutes per packet. With 300+ packets a week, that's 90 staff hours. Errors in routing meant packets sat in the wrong queue. Underwriters complained about incomplete information.
What We Built
- → Document classification system: automatically identifies policy type, coverage, risk level
- → Intake triage workflow: classifies, extracts key fields, routes to underwriting queue
- → Human review gate: Harbor & Field team reviews every classification before routing
- → Dashboard: classification accuracy, routing time, exception rates
- → Training on system, feedback loops, model improvement process
Outcome
6 minutes
down from 18 minutes per packet (67% reduction)
Harbor & Field cut initial handling time from 18 to 6 minutes per packet. Staff now focus on review and exception handling instead of manual classification. Underwriters receive complete, correctly routed packets on the first try.
Timeline: 12 weeks. Team: 1.5 FTE + Harbor & Field's product lead. The system classifies 300+ packets weekly with 94% accuracy; humans review all, catch edge cases, and provide feedback for continuous improvement.
Key Metrics
Time per Packet
6 min
down from 18 minutes
Weekly Packets Processed
300+
with automated triage
Classification Accuracy
94%
with human review gates
Duration: 12 weeks to live
Workflow: Intake triage, classification, routing
Review Model: Human-in-the-loop
Cedar Peak Foods
The Situation
Cedar Peak is a specialty food manufacturer serving 200+ retail partners. They have three core systems: production planning software, procurement system, and sales forecasting tool. Each operates independently.
Every week, demand planners manually compared sales forecasts, production capacity, and supplier lead times using spreadsheets. Mismatches led to either excess inventory or stockouts. In 2023, they had 27 emergency expedite orders, each costing $3K–$8K in expedite shipping and production delays.
What We Built
- → Unified data model: production capacity, sales forecast, procurement lead times, inventory position
- → Daily ELT pipelines pulling from all three systems
- → Weekly planning model: forecast vs. capacity vs. procurement, with variance analysis
- → Automated alerts: demand exceeds capacity, procurement lead time risks, inventory targets missed
- → Team training and ownership model
Outcome
27 fewer
emergency expedites in the first year (from 27 to 0)
Cedar Peak's demand planners now have visibility into the full supply chain. They catch mismatches within days, not at crisis time. Zero emergency expedites in year one. Inventory levels stabilized. Supply chain planning is now predictable.
Timeline: 16 weeks. Team: 2 FTE + Cedar Peak's planning lead. First year ROI: 27 avoided expedites × $5K average = $135K+ savings, minus project cost.
Key Metrics
Emergency Expedites
0
down from 27/year
First Year Savings
$135K+
from avoided expedite fees
Planning Cycle
Weekly
instead of ad hoc
Duration: 16 weeks to live
Systems: Production, procurement, sales forecasting
Focus: Supply chain visibility
What's your data challenge?
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