Domain-Specific AI for Industrial Operations
Generic AI fails on your data. I build the kind that doesn't. Fine-tuned on your documents, running on your infrastructure, improving with every expert correction.
Let's TalkProblems I Solve
These are the problems that kill AI projects in industrial operations — not because the technology is wrong, but because the approach is.
Your AI Demos Fail on Real Data
95% of AI pilots deliver zero ROI — MIT, 2025
Generic AI models score 39% on domain-specific tasks. They were trained on the internet — they have never seen your ISA symbols, your tag conventions, or your drawing standards. Fine-tuning on your data, on your hardware, is what closes the gap.
I took frontier vision APIs from 39% to over 93% accuracy on industrial engineering drawings — same task, different approach.
Your Drawings Are Locked in PDFs
$14.2B intelligent document processing market — Precedence Research, 2026
Decades of engineering knowledge sit in scanned P&IDs, datasheets, and vendor documents. Teams rebuild line lists, instrument indexes, and equipment registers manually — often more than once across different project stages.
20+ years extracting structured data from engineering documents. Thousands of drawings processed across multiple industrial facilities.
Your Experts Are Retiring — And Taking Your Data With Them
$31.5B lost annually to knowledge attrition — Deloitte
Traditional knowledge transfer captures about 20% of what experienced operators know. The remaining 80% — tacit knowledge, unwritten rules, contextual judgment — walks out the door. AI models cannot reach production accuracy without that expertise in the loop.
My models reached 93% because domain experts reviewed mistakes and fed corrections back into the system. Every correction became training data.
You Can't Send Facility Blueprints to a Cloud API
72% of energy leaders report increased responsible AI governance interest — EY, 2026
When you route a P&ID through a cloud API, you send facility blueprints to a third-party server. PIPEDA, the CLOUD Act, and trade secret law create real liability. Legal kills these projects before engineering evaluates accuracy.
Local models handle detection and extraction on your hardware. Frontier models see structured outputs, never raw drawings. Your data never leaves your network.
How I Work
Assess
Inventory your document corpus, define extraction targets, evaluate data readiness. Understand the problem before touching a model.
Build
Fine-tune local models on your data. Build review tooling for your domain experts. Deploy on your infrastructure, under your control.
Compound
Every expert correction becomes training data. Active learning loops raise accuracy with each project. The model that processes your 20th facility is better than the one that processed your first.
Industries
AI in Industrial Operations — 6-Part Series
A practitioner's guide to domain-specific AI — from technical reality to organizational strategy. Published on LinkedIn.
Let's Talk
If your organization has engineering documents with decades of institutional knowledge locked inside them — and generic AI hasn't worked — the problem is solvable. It starts with your data, your experts, and your infrastructure.
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