AI Transformation Case Studies

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

Dealer Media House needed a sales-ready way to show addressable market opportunity by ZIP (audience density, competitive positioning, and white-space targeting) live in the room and as a leave-behind PDF. Spreadsheets and an older CTV dashboard were not presentation-grade, and they choked on large ZIP polygon sets.

• Visualize ZIP-level audience concentration without loading national boundary data
• Overlay client vs. competitor dealerships with clear radius/trade-area framing
• Support dual branding: agency letterhead (DMH) + per-OEM pitch accents
• Export polished, on-brand PDFs from the live map session

Solution

Built a standalone pitch platform:

• Projects & ingestion: CSV/XLSX upload (long + wide client formats), validation, dataset versioning, Supabase storage
• Interactive map: MapLibre choropleth, ZIP borders/tooltips, layer toggles, click-to-exclude ZIPs, numbered brand-colored competitor pins
• Dealer workflow: Filename to dealer website to Google Places lookup to human confirm; competitor search by brand + radius
• Trade-area analytics: Reach in radius, concentration KPIs, audience composition donuts, white-space ZIPs (high audience, no nearby competitor)
• Presentation export: Dark command-center UI + 2-page branded PDF (metrics + live map snapshot) with agency letterhead

Outcome

• Sales teams can run one project per pitch/market from upload to map story to branded PDF
• Competitive visualization treated as a first-class pitch asset (radius rings, ranked competitors, white space)
• Stable ZIP rendering via batched Census TIGER boundaries + caching (avoids prior OOM failure mode)
• Dual theme system: Dealer Media House / Dealers Direct agency skins + 28 OEM accent palettes

Tech Stack: Next.js 16, React 19, TypeScript, Tailwind CSS 4, MapLibre GL, Supabase (Postgres/Auth/Storage), Google Places API, US Census TIGER ZCTA, jsPDF, PapaParse/xlsx, Render

Challenge

California workers' compensation and consumer attorneys rely on private association listserv archives (CAAA, later CAALA) to evaluate doctors, judges, insurers, and related parties. Doing that by hand meant logging into TrialSmith, digging through message threads, and synthesizing opinions manually.

The client needed a production product that could:

• Authenticate against member-only listserv sites without storing passwords on the server
• Scrape and structure listserv results reliably
• Use AI to filter relevance and synthesize "good/bad" evaluations
• Support real subscribers with accounts, tiers, credits, and billing
• Stay stable as sessions expire, sites change, and usage grows
• Later expand from one association (CAAA) to a second (CAALA) without rewriting the scraper

Solution

Built and shipped a full-stack SaaS product (attorneytoolkit.com):

• Secure access layer: Chrome extension captures authenticated browser cookies from CAAA/CAALA and uploads them to the user's account; per-user cookie isolation (no shared session jar); read-only session keep-alive that pings each user's listserv every 15 minutes so ColdFusion idle timeouts don't force constant re-login
• AI search & evaluation pipeline: Playwright-based scraper against TrialSmith-style listserv search; Claude-powered query enhancement, message relevance filtering, and synthesis (doctor/judge/insurer-style evaluations); doctor comparison flows and search history for attorneys
• Multi-org architecture: Org registry so CAAA and CAALA share one scraper engine with per-org URLs, domains, and cookie files; frontend association picker; shared credit pool across both listservs
• Product / ops layer: Astro frontend, FastAPI backend, PostgreSQL; Stripe subscriptions (tiered search credits), promo codes, admin dashboard; admin QME batch evaluator for bulk doctor runs (~4,500-name CSV) with pause/resume, cost tracking, and durability backups; migrated production from Spenser's DigitalOcean droplet to the client's account with zero data loss (snapshot + delta sync + DNS cutover)

Outcome

• Live multi-user product used by attorneys to search private listserv archives and get AI-assisted evaluations in minutes instead of manual thread-reading
• Expanded from single-association (CAAA) to dual-association (CAAA + CAALA) on one shared platform and credit model
• Batch pipeline capable of evaluating thousands of QME/AME doctors with durable logging and resume-safe runs
• Production infrastructure owned by the client, with auth, billing, admin tooling, and Chrome extension distribution in place

Tech Stack: Python (FastAPI, Playwright, Anthropic Claude), PostgreSQL, Astro + Alpine.js, Chrome Extension (Manifest V3), Stripe, DigitalOcean, Nginx, systemd timers, Render (frontend), Resend/SendGrid (email)

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

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)