Shantanu Patel

Technical Deep Dive

Forward Deployed Engineering Work

Detailed examples of AI projects, technical problem-solving, and hands-on implementation work.

500+ Demos 25+ Warehouses 500+ Users Trained
AI & Automation Projects

AI systems built and deployed for warehouse operations at Hopstack.

AI-Powered Document Parsing System

Hopstack

Architected and deployed document parsing tool using Anthropic Claude API for automated data extraction. Processed 20+ template types with human-in-the-loop review workflow.

85%
Accuracy Rate
87%
Time Savings
15min → 2min
Per Document
Details
  • ▹ Integrated directly into Hopstack WMS, eliminating manual data entry for receiving operations
  • ▹ Reduced receiving time from 15 minutes/document to 2 minutes/document (87% time savings)
  • ▹ Achieved 85% accuracy rate with human-in-the-loop review workflow
Tech: Anthropic Claude API, Python, WMS Integration

Auto-Cropping Tool for E-commerce Returns

Hopstack

Built AI-powered label extraction tool solving operational pain point for e-commerce return processing. Extracted 4x6 shipping labels from full-size A4 return labels using computer vision algorithms.

95%
Accuracy Rate
3-5min → 30sec
Time Reduction
Various Formats
Label Types Handled
Details
  • ▹ Achieved 95% accuracy rate across various label formats and print qualities
  • ▹ Reduced returns processing time from 3-5 minutes per return to <30 seconds
Tech: Python, TensorFlow, Claude API

AI-Driven Order Optimization Algorithms

Hopstack

Implemented order batching algorithm using clustering for planning and grouping logic.

80%
Picking Time Reduction
40%
Packing Efficiency
100+
Orders/Day Volume
Details
  • ▹ Reduced warehouse picking time 80% for customers with 100+ order/day volume
  • ▹ Enabled bulk label generation for 100+ identical orders
  • ▹ Improved packing efficiency 40% through intelligent order grouping by product type and destination
Tech: Clustering algorithms, Python

Shipment Visibility Solution

External Customer Patent Pending

Served as forward deployed engineer for external logistics customer requiring real-time pallet tracking. Led end-to-end deployment: hardware installation, camera calibration, barcode detection tuning, cloud integration.

99.2%
Barcode Read Accuracy
50+
Trucks Daily
Real-time
vs. Month-end Reporting
System Architecture
  • ▹ Designed system architecture combining edge computing, computer vision, and cloud-based tracking dashboard
  • ▹ System processing 50+ trucks daily with 99.2% barcode read accuracy
  • ▹ Increased supply chain observability enabling real-time detection vs. previous end-of-month reporting
  • ▹ Published patent application for novel shipment visibility technology
Tech: Edge Computing, Computer Vision, Cloud Integration
Technical Problem-Solving Examples

Real production issues encountered and resolved during customer deployments.

Integration Crisis Recovery

Customer's ERP crashed during go-live. Implemented emergency backup sync mechanism using CSV exports + API uploads.

Context

Real-time API sync with customer's ERP system failed during go-live weekend. Built alternative data ingestion path to keep operations running.

Performance Optimization

Customer experiencing 30-second page load times with 500K SKU catalog. Implemented database indexing, query optimization, and caching layer, reducing load time to <2 seconds.

Approach
  • ▹ Database indexing on frequently queried columns
  • ▹ Query optimization to eliminate N+1 patterns
  • ▹ Caching layer implementation
  • ▹ Result: 30 seconds → <2 seconds page load time

Data Migration Challenge

Legacy system had 15 years of inconsistent data formats. Built Python scripts to clean, normalize, and validate 2M+ records, achieving 98.8% data accuracy post-migration.

Scope
  • ▹ 2M+ records with inconsistent formats across 15 years
  • ▹ Built ETL pipeline for cleaning and normalization
  • ▹ Achieved 98.8% data accuracy post-migration

Warehouse Migration

Customer moved between two warehouses over the weekend. Helped them move, clean-up, upload the new warehouse data, keeping operational aspects running without any interruptions.

Details

On-site support during weekend warehouse relocation to ensure system data stayed synchronized with physical inventory movement.

Customer ROI Examples

Measurable operational improvements delivered to strategic customers.

80% Time Reduction

Reduced order processing time from 10 minutes to 2 minutes per order for 3PL customer processing 10K+ orders/day

$400K Annual Savings

Enabled through AI-powered optimization algorithms (box and carrier selection, order batching, routing optimization)

Compliance Implementation

Supported and guided Alcohol & CCPA compliance implementation that unlocked market expansion for fulfillment brand

99.5% Inventory Accuracy

Improved from 85% through cycle counting automation and real-time tracking

Production Tools & Open Source

Tools built to solve operational problems, used internally and by external teams.

Warehouse Utility Suite

utils.shan0o.com

Production-deployed web application suite solving warehouse operations pain points. Built as stateless client-side application using Replit platform.

Features
  • ▹ PDF label cropping tool: extract shipping labels from full-size documents
  • ▹ ZPL label viewer/editor: preview and modify thermal printer label formats
  • ▹ Excel/CSV data transformation tools: format conversion, data cleaning, column mapping
Usage
  • ▹ Used internally at Hopstack and by external warehouse operations teams
  • ▹ Processing 100+ document transformations monthly
  • ▹ Open source project accepting community contributions
Tech: React, JavaScript, HTML5 Canvas API, PDF.js, Replit

Personal Inventory App

itemom.com

Full-stack home organization inventory application for fast item capture, search, and organization. Sandbox for exploring agentic AI development, authentication, storage patterns, and real-time data architectures.

Features
  • ▹ Fast item capture with mobile camera integration and barcode scanning
  • ▹ Real-time search across item names, descriptions, tags, and custom fields
  • ▹ Tag-based organization with hierarchical categorization
  • ▹ Image upload and storage with thumbnail generation
  • ▹ AI-powered features: automatic categorization, smart search, OCR for product labels
Learning Focus
  • ▹ Exploring modern tech stack and AI development tools
  • ▹ Experimenting with real-time data sync using Supabase subscriptions
  • ▹ Testing UX patterns for AI-first workflows
Tech: Next.js, React, Tailwind CSS, Supabase (PostgreSQL, Auth, Storage), Vercel

Looking for Product Manager or Forward Deployed Engineer Roles

Technical depth combined with customer-facing implementation experience. Available in 2 weeks.