Operational problems with working solutions.

Three detailed examples of how we've removed friction for operations-driven businesses.

Northline Supply Co.

3-warehouse logistics & fulfillment

The challenge

Three regional warehouses, three separate inventory systems, three competing views of what was actually in stock. Operations spent Friday afternoons reconciling numbers. Fulfillment teams couldn't see real-time availability. When discrepancies appeared, nobody knew who owned the fix.

Our approach

  • • Unified inventory data from all three systems into a single model
  • • Built real-time stock visibility dashboards for warehouse and fulfillment teams
  • • Created data quality checks to catch discrepancies at 6 AM, not Friday evening
  • • Established clear ownership and escalation for anomalies

Results

14 hours/week → 3 hours/week reporting time spent (reporting preparation reduced 85%)

3-day lag → next-morning detection on inventory discrepancies

"We went from losing Friday afternoons to reporting. Now we make decisions Monday morning."

— Director of Operations, Northline Supply Co. (Fictional illustrative example)

Project snapshot

Scope

3 warehouse systems → 1 unified model

Systems integrated

SAP, NetSuite, custom legacy system

Key deliverables

  • • Unified warehouse schema
  • • Real-time sync pipelines
  • • Operations dashboards
  • • Quality checks & alerts

Timeline

12 weeks from engagement to go-live

Team involved

2 data architects, 1 analytics engineer, 1 delivery lead

Discuss similar challenges

Project snapshot

Scope

Manual intake & routing → AI-assisted classification

Documents processed

3,500–5,000 claims per month

Key deliverables

  • • Document classification model
  • • Intake automation workflow
  • • Human review queue
  • • Audit log & compliance tracking

Timeline

14 weeks from engagement to live

Team involved

2 automation engineers, 1 data architect, 1 compliance lead

Harborwell Benefits Group

Health insurance claims processing

The challenge

Daily intake of 150–200 new claims. Every claim arrived as scans, handwritten notes, faxes. Case managers manually reviewed each one to classify (urgent, routine, follow-up needed), then routed appropriately. The bottleneck: classification was slow, error-prone, and impossible to parallelize.

Our approach

  • • Trained document classification model on 5 years of historical claims
  • • Built intake automation that scans, extracts, and classifies in seconds
  • • Created human review queue for edge cases and high-value decisions
  • • Implemented compliance logging and audit trail for every classification

Results

41% faster case routing (classification time dropped from 12 min/claim to 7 min/claim)

Reduced manual errors by 28% (measured across 6-month pilot)

"The system catches things we'd normally miss at 11 PM on a Friday. Now nothing slips through."

— VP of Operations, Harborwell Benefits Group (Fictional illustrative example)

Discuss similar challenges

Meadow & Pike Manufacturing

Specialty materials production (3 plants)

The challenge

Three production plants, each with different equipment and monitoring systems. Plant managers had local dashboards but no cross-plant visibility. When a quality problem appeared, root-cause investigation took 2–3 days of manual data pulls and spreadsheet work. Production losses stacked up while engineers searched for the signal.

Our approach

  • • Unified production data from all three plants into a real-time warehouse
  • • Built production-quality monitoring dashboards with anomaly detection
  • • Created plant performance comparison views for cross-site visibility
  • • Deployed automated alerts for quality deviations (within 5 minutes of detection)
  • • Documented root-cause analysis playbooks for common failure patterns

Results

62% reduction in incident detection time (from ~3 hours manual investigation to 10–15 minutes automated detection)

$2.1M in prevented production losses (annualized, first 18 months)

"We catch quality issues before they become batches. The system basically pays for itself on the first incident."

— Plant Director, Meadow & Pike Manufacturing (Fictional illustrative example)

Project snapshot

Scope

3-plant production → unified monitoring platform

Data sources

OPC-UA sensors, historian databases, ERP systems

Key deliverables

  • • Unified production schema
  • • Real-time data streaming
  • • Quality monitoring dashboards
  • • Anomaly detection (ML model)
  • • Alert routing & escalation

Timeline

16 weeks from engagement to live, plus 4-week pilot

Team involved

2 data architects, 1 ML engineer, 1 analytics engineer, 1 delivery lead

Discuss similar challenges

Your challenge might be next

Start with a conversation about where friction lives in your operations. We'll listen, ask hard questions, and tell you what's possible.

Talk to an Engineer