Engineered Results

Problem

A commercial services company doing $3.2M in annual revenue was losing 12-15% of billable work to administrative friction. Quotes were generated manually in Word documents. Invoicing happened weeks after service delivery. Payment collection averaged 47 days.

Operational Friction

• Quoting process required 3 people and took 2-4 days per quote
• No connection between quoting system and invoicing
• Field teams recorded work on paper, transcribed later
• Revenue recognition lagged actual delivery by 3-6 weeks
• Collections had no automated follow-up process

System Engineered

• Custom quoting engine pulling from service catalog and pricing rules
• Automated quote-to-invoice pipeline triggered by job completion
• Mobile field reporting integrated with job management system
• Real-time revenue dashboard with aging receivables alerts
• Automated payment reminder sequences with escalation rules

Measurable Outcome

Problem

A regional distribution company processing 400+ orders daily was drowning in manual data entry and classification. Customer purchase orders arrived in dozens of formats — email, PDF, fax, portal. Each required manual reading, data entry, and routing. Error rates were climbing with volume.

Operational Friction

• Order processing required a 4-person team working full shifts
• Average order entry time: 12 minutes per order
• Error rate on manual entry: 6.2% (causing returns, credits, customer friction)
• No standardized classification — routing decisions were tribal knowledge
• Scaling required linear headcount increases

System Engineered

• AI document extraction pipeline for multi-format purchase orders
• Automated classification and routing engine with confidence scoring
• Exception handling workflow for low-confidence extractions (human-in-the-loop)
• Integration with existing ERP for direct order creation
• Monitoring dashboard tracking accuracy, processing time, and exception rates

Measurable Outcome

Problem

A growing professional services firm with 80+ employees had operational data spread across 6 disconnected platforms. Financial data in one system. Project management in another. Resource allocation in spreadsheets. Client data in a CRM that nobody trusted. Leadership couldn't answer basic questions about profitability by service line or utilization rates without a week of manual analysis.

Operational Friction

• 6 disconnected data systems with no integration
• Monthly financial close took 3 weeks of manual reconciliation
• Utilization reporting was done quarterly in spreadsheets (always stale)
• No single source of truth for client or project data
• Every new tool added another silo — tech stack was 14 platforms deep

System Engineered

• Unified data architecture on PostgreSQL with dimensional modeling
• Cloud infrastructure on AWS with managed services for cost efficiency
• ETL pipelines connecting all 6 source systems to the central warehouse
• Real-time operational dashboards: utilization, profitability, pipeline, cash flow
• API layer enabling future system additions without architectural changes
• Reduced platform count from 14 to 6 through consolidation

Measurable Outcome