AI Expertise
AI that ships to production
Anyone can build a demo. I build AI systems that pass enterprise review, hit 80%+ accuracy thresholds, and deliver six-figure savings. First production GenAI at Ally, now the template for every AI initiative that follows.
Areas of Expertise
Battle-tested patterns for enterprise AI systems
Document Processing Pipeline
Production-grade OCR that handles the messiest PDFs, tables, columns, complex layouts.
- YoloX + Tesseract for robust text extraction
- Element metadata preservation for context
- Pre-processing that optimizes LLM consumption
- 40% faster processing through threading optimization
Production Monitoring
If you can't measure it, you can't trust it. Built comprehensive MLOps from scratch.
- Three-dimensional framework: Accuracy, Stability, Robustness
- Automated alerting with Red/Yellow/Green thresholds
- Cosine similarity validation against ground truth
- Human-in-the-loop feedback integration
Prompt Engineering
Evolved from GPT-3.5 hallucinations to GPT-5.1 production reliability.
- Chunking strategies that prevent context overload
- Structured outputs that parse reliably
- Hallucination prevention through grounding
- Single-prompt extraction replacing multi-pass approaches
Data Engineering
Garbage in, garbage out. Built pipelines that ensure clean data for AI systems.
- Strategic sampling for model robustness
- PDF cleansing that preserves document structure
- Metadata-driven text restructuring
- Vector databases for semantic search
Production AI: Lessons Learned
Hard-won insights from getting AI past enterprise review
What It Takes to Ship
Demos are easy. Production is hard. Getting Ally's first GenAI system approved required solving problems most tutorials don't cover:
- Monitoring that satisfies risk: Three-dimensional framework with automated escalation. Red/Yellow/Green thresholds that trigger before users notice problems.
- Data pipelines that don't break: Pre-processing that handles edge cases. Text restructuring that preserves context. Validation at every step.
- Feedback loops that improve: Human-in-the-loop validation. Ground truth expansion. Continuous accuracy measurement.
- Graceful failure: Clear escalation paths. Meaningful error messages. Manual fallbacks when confidence is low.
Hard-Won Technical Insights
Less is more: Cutting chunk size from 12k to 4k tokens improved accuracy. Context overload is real, smaller, focused chunks beat kitchen-sink prompts.
Model upgrades change everything: GPT-3.5 needed multi-pass category extraction. GPT-4o/5.1 handles it in one prompt. Know when to re-architect.
Metadata is underrated: OCR element types (headers, tables, lists) preserved in pre-processing dramatically improved classification. Structure matters.
Need AI that actually ships?
I've done the hard work of getting AI past enterprise review and into production. Let's talk about your challenge.
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