Case Studies

Problems solved,
results measured

Three case studies across search, growth, and AI automation. Each one starts with a real constraint and ends with a number.

02
Growth · Monetization · UX

Commerce Platform Growth

Millions
Incremental revenue

A commerce platform with strong supply-side inventory was failing to convert user visits into meaningful engagement. Sessions were shallow, repeat purchases were rare, and personalization was entirely absent from the product.

The Challenge

  • High traffic, low engagement — average session depth under 2 pages
  • No personalization layer — every user saw identical category pages
  • CAC outpacing LTV across all major acquisition channels
  • Product discovery was browse-only; no intelligent surfacing of relevant inventory

The Approach

  1. 1User journey analysis identified four distinct intent modes — mapped each to different product surfaces
  2. 2Designed embedded commerce experience: contextual recommendations integrated into editorial content
  3. 3Built a two-stage recommendation pipeline: candidate generation from collaborative filtering + re-ranking by context
  4. 4Deployed real-time personalization for returning users and cohort-based personalization for new visitors
  5. 5Introduced progressive disclosure of catalog depth based on user engagement signals

Results

Incremental revenueMillions
Session depth increase+34%
Return purchase rate+22%
CAC payback period−28%

Key Insight

“Commerce products over-index on supply quality and under-index on discovery quality. The bottleneck wasn't inventory — it was the product's inability to match intent to the right item at the right moment.”

03
LLMs · Agents · Workflow

Enterprise AI Automation

130×
Processing efficiency

An operations team was spending the majority of working hours on repetitive data extraction, classification, and routing tasks — work that required judgment but was too structured to genuinely require human expertise on every instance.

The Challenge

  • Operations team processing 2,000+ documents per week manually
  • Error rate on manual classification averaging 8–12% with significant downstream impact
  • Zero audit trail on decisions — compliance risk growing with volume
  • No capacity to scale operations as business grew without proportional headcount

The Approach

  1. 1Mapped the full operations workflow — identified which steps required genuine judgment vs. pattern recognition
  2. 2Designed a human-in-the-loop LLM system: AI handles extraction and initial classification, humans review edge cases
  3. 3Built a multi-stage agent: document parsing → structured extraction → confidence-scored classification → routing
  4. 4Implemented evaluation framework with 500+ labeled examples to track model accuracy continuously
  5. 5Created audit dashboard making every AI decision explainable and traceable

Results

Processing efficiency130×
Error rate reduction−91%
Manual review burden−87%
Audit compliance100%

Key Insight

“The 130× gain came from a design decision, not a model choice. By precisely mapping which decisions needed human judgment, we automated 90%+ of volume while improving quality — rather than replacing humans entirely and accepting their error rate.”

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