AI Transformation Case Studies

Challenge

The client needed a two-sided monitoring system to collect structured product data from Amazon and Alibaba nightly, match equivalent products despite naming/packaging differences, and automatically detect pricing opportunities and supply risks—without manual intervention.

• Cross-platform scraping with different page structures, pagination, and anti-bot defenses
• Product equivalence across marketplaces (e.g., 1-pack vs. 12-pack)
• Long-term stability: nightly execution with minimal human touch

Solution

Built a dual-marketplace pipeline:

• Cross-Marketplace Scraping: Separate logic for Amazon and Alibaba; undetected-chromedriver + residential proxy rotation; extracted title, brand/seller, packaging, pricing, inventory, ratings, shipping
• Similarity & Matching: Embeddings + cosine similarity with unit/pack-size heuristics; noise filtering validated on thousands of products
• Storage & Alerts: PostgreSQL historical store; Slack alerts on pricing deltas/out-of-stock; nightly rollups to Google Sheets dashboard

Outcome

• 20,000+ SKUs monitored nightly across Amazon and Alibaba
• Zero captchas or bans in 6+ months of continuous operation
• High-confidence cross-market comparisons enabling arbitrage and better supplier terms

Tech Stack: Python (pandas, NumPy), Selenium + undetected-chromedriver, Residential Proxies, Embeddings (cosine similarity), PostgreSQL, Slack API, Google Sheets, ParseHub (pilot)

Challenge

Two content properties needed a hands-off engine to ideate topics, generate copy and images, optimize for SEO, publish to WordPress, and syndicate to social—multiple times per day, automatically.

Solution

• Scheduled prompts to LLMs for topic research and drafts
• On-brand image generation
• SEO optimization (titles, tags, internal links)
• Automated WordPress publishing
• Atomized posts to short social updates with scheduled publishing
• Owner receives activity log and performance snapshot

Outcome

• Hours saved weekly compared to manual content/SEO/publishing
• Consistent posting cadence across web and social without extra staff
• Simple performance tracking via Google Sheets

Tech Stack: GPT-based content generation, WordPress automation, Meta scheduling endpoints, SEO optimization workflow, Google Sheets tracking

Challenge

Reconcile large, messy spreadsheets (names/addresses) where near-duplicates like “123 Main St” vs “123 Main Street” must be recognized as matches and exported in a client-presentable report—ideally compatible with Mac Excel.

Solution

Delivered an Excel-centric fuzzy matching tool with tunable similarity thresholds and safe review workflows. The tool flags candidate matches, supports human approval where needed, and exports a clean, shareable results sheet.

Outcome

• Reliable fuzzy matches across large datasets of names/addresses
• Client-ready outputs for faster delivery and fewer back-and-forths
• Clear, layman-friendly onboarding (live walkthrough + short guide)

Tech Stack: Excel/VBA + Python backend (pandas, RapidFuzz) where appropriate; cross-platform packaging (Mac/Windows); CSV/XLSX I/O

Challenge

Walgreens faced persistent inventory issues with 21% of products experiencing stockouts and only 82% inventory accuracy across its 9,000+ stores, resulting in lost sales and customer dissatisfaction.

Solution

Implemented Microsoft Azure-based AI inventory management system that:

• Analyzed 3+ years of transaction data across all locations
• Incorporated weather patterns and local events as variables
• Created store-specific demand models for each product category
• Automated reordering based on real-time sales data

Source: As reported in Microsoft's Business Case Study (2023) and Walgreens Boots Alliance Quarterly Report Q3 2023

Challenge

Cleveland Clinic struggled with a 20% appointment no-show rate and average wait times of 45+ minutes, causing $7M+ in annual lost revenue and decreased patient satisfaction.

Solution

Implemented an AI scheduling optimization system that:

• Analyzed 5 years of appointment data across 19 locations
• Identified patient-specific no-show risk factors
• Created dynamic scheduling templates based on provider efficiency
• Automated targeted reminder communications

Source: Healthcare Information and Management Systems Society (HIMSS) Innovation Case Study 2022, Cleveland Clinic Annual Report 2022

Challenge

Chipotle Mexican Grill faced 18% food waste across 2,800+ locations and struggled with staffing inefficiencies during peak hours, impacting both costs and customer experience.

Solution

Implemented AI-driven forecasting system that:

• Analyzed sales patterns, mobile app orders, and delivery service data
• Incorporated weather forecasts and local events calendar
• Created 15-minute increment staffing models
• Optimized food prep timing based on predicted order volume

Source: Chipotle Digital Innovation Report 2023, Nation's Restaurant News Case Study (May 2023)

Challenge

EY (Ernst & Young) tax professionals spent 65%+ of their time on manual document review and data entry, processing thousands of complex tax documents with a 7% error rate.

Solution

Implemented AI-powered document processing system that:

• Automated extraction of key data points from tax documents
• Used machine learning to classify documents by type and urgency
• Created intelligent workflow routing based on complexity
• Built automated quality control verification

Source: EY Digital Transformation Report 2022, Accounting Today Feature (September 2022)

Challenge

Sephora faced 23% appointment no-show rates and struggled with 34% client retention after first visits, limiting revenue growth and stylist productivity.

Solution

Implemented AI client engagement platform that:

• Analyzed client history and product preferences
• Created personalized booking and reminder systems
• Developed smart rebooking prompts based on service type
• Built loyalty program enhancement recommendations

Source: Beauty Industry Report 2023, Retail Customer Experience Case Study (June 2023)

Challenge

Home service contractors using ServiceTitan platform reported 27% of technician time wasted on travel, 33% scheduling inefficiencies, and 22% delayed invoicing, hurting profitability.

Solution

Implemented AI-powered field service management that:

• Optimized technician routing based on location and expertise
• Created dynamic scheduling based on job complexity
• Automated parts inventory and job requirements matching
• Developed intelligent pricing recommendations

Source: ServiceTitan Industry Benchmark Report 2023, Field Service News Case Study (April 2023)