Case Studies
These are systems case studies documenting real-world problems, architectural decisions, tradeoffs, and operational outcomes. They explain how we think, how we make choices, and what we learn from building operational software. No hype, no marketing claims—just systems thinking, constraints, and lessons learned.
Case Studies
Automated Data Quality Monitoring System
Data quality issues (duplicate records, missing fields, inconsistent formats) caused 20% of operational errors
Custom Reporting Dashboard for Operations Team
Operations team spent 8 hours weekly compiling Excel reports from 5 different systems to track KPIs
Exception Handling Workflow for Order Fulfillment
30% of orders required manual exception handling, creating bottlenecks in fulfillment process
Multi-Department Approval Workflow Automation
Purchase requisitions required 5-7 sequential approvals that took 2-3 weeks, blocking operational purchases
Inventory Forecasting and Replenishment Automation
Manual inventory forecasting led to $400K in excess inventory and frequent stockouts on high-turnover items
Real-Time Inventory Allocation for Dropshipping Operations
Dropshipping model required real-time inventory visibility across 15 supplier systems with inconsistent update frequencies
Multi-Warehouse Inventory Synchronization System
Inventory counts diverged across 4 warehouses, causing 25% overselling and stockout issues
Legacy ERP Order System Replacement
20-year-old DOS-based order system couldn't integrate with modern e-commerce or handle current order volume
Exception-Heavy B2B Order Workflow Automation
60% of B2B orders required manual approval workflows that took 2-3 days to process
High-Volume Multi-Channel Order Processing Platform
Processing 2,000+ orders daily across 8 sales channels required 15 hours of manual reconciliation
Multi-Channel Order Sync Across Marketplace and Direct Sales
Inventory overselling across 5 channels due to manual consolidation and daily-only sync
Replacing Spreadsheet-Based Order Processing at 800 Orders/Day
Spreadsheet-based processing creating 4-6 hour lag at 800 orders/day
Common Patterns We See
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Data inconsistency is the root problem: Operational failures trace back to inconsistent data across systems, manual data entry errors, or mismatched assumptions about what data means. Solving operational problems requires fixing data at the source.
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Manual workflows hide systemic issues: Teams create manual workarounds to compensate for system limitations. These workarounds become standard practice, masking the underlying problems until operations scale beyond manual capacity.
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Scaling exposes bad assumptions: Systems that work for 100 orders per day fail at 1,000. Assumptions about data volume, user behavior, or integration stability break under scale. Building for scale requires understanding where assumptions fail.
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Legacy constraints drive architecture: Existing systems, data formats, and operational processes constrain what's possible. Good architecture works within constraints rather than ignoring them. Integration with legacy systems is often more important than building new ones.
These systems are not templates. Every build starts with understanding your operations, constraints, and objectives. If your operational problems require custom solutions, start with a discovery conversation to evaluate fit.
Evaluate Fit