Production-grade AI agents that ship in days, not quarters. 8 live agents deployed across wind energy and industrial automation.
A specialist in production AI for regulated industries.
I'm the equivalent of 913.ai for the energy and industrial sector.
While others talk about AI transformation, I've shipped 8 autonomous agents across all business functions, managing 100+ wind turbines with real-time predictive maintenance.
My approach: Domain expertise first, AI second.
When you deeply understand wind energy (or manufacturing, or industrial IoT), you can build agents that ship in days, not quarters. That's the difference between consultants who deliver PowerPoints and engineers who deliver production code.
I don't do generic AI labs or research projects. I build vertical agents that handle real work, generate measurable ROI, and comply with SOC2/GDPR/HIPAA from day one.
Every agent below is live, handling real work, generating measurable ROI.
RAG-powered conversational AI reduced support queries by 60% with 95%+ accuracy. Semantic search across 10,000+ docs, live in 3 days from concept to production.
Personal AI assistant with voice-first interface saves 10+ hours weekly. Autonomous calendar/email automation with context-aware task prioritization.
Autonomous agent for full employee lifecycle: recruitment, screening, onboarding. 50% reduction in time-to-hire with 24/7 employee support.
Help desk automation cut ticket resolution time by 50%. Tier-1 tickets handled autonomously with on-demand code generation and DevOps acceleration.
AI-driven lead qualification delivers 3x faster response times. Automated proposal generation in minutes, not hours, with consistent customer engagement.
Invoice processing automation with 100% consistent expense categorization. On-demand financial reporting with built-in compliance audit trails.
AI-powered content generation drafts press releases in minutes. 24/7 media monitoring with automatic brand sentiment tracking.
Autonomous agent monitoring industrial operation sites in real-time — tracking KPIs, detecting anomalies, generating incident reports, and escalating critical events before they become outages.
IoT platform monitoring 100+ wind turbines in real-time with AI-powered predictive maintenance. TB-scale data processing with ML anomaly detection prevents downtime. Patent application: DE 10 2023 134 843.4.
AI agent ecosystem directory tracking every major Claw AI variant — from personal assistants to enterprise platforms. Live database with JSON API, sourced from openclaw.ai and Nvidia NemoClaw.
From reactive maintenance and siloed SCADA data to real-time AI-powered operations. Here's exactly how I thought through it.
MOWEA operated 100+ modular wind turbines across distributed sites. Maintenance was purely reactive — engineers only knew something was wrong when turbines failed or sent basic SCADA error codes. Data sat in isolated systems that couldn't talk to each other. Each unplanned outage meant manual site visits, hours of downtime, and thousands in lost generation.
The core issue: too much data, zero intelligence. Turbines generated gigabytes of sensor readings — vibration, temperature, RPM, power curves — but nothing beyond simple threshold alerts was being done with it.
I designed a 3-layer architecture: Edge → Stream → Intelligence.
Edge layer: Lightweight agents on each turbine controller. Sub-100ms sampling of 40+ sensor channels. Local anomaly pre-filtering reduced bandwidth by 70% — only deviations ship to the cloud.
Stream layer: AWS IoT Core for ingestion, InfluxDB for time-series storage. Custom normalization pipeline across 3 hardware generations with incompatible protocols.
Intelligence layer: Isolation Forest for anomaly detection, LSTM networks for failure forecasting. Key insight: don't train on failures (rare) — train on the 72-hour signature before failures. That's the predictable part.
Data quality was the real challenge. Field turbines have intermittent connectivity, sensor drift, and firmware bugs injecting corrupt readings. I built a validation pipeline to distinguish "sensor failure" from "actual anomaly" — getting it wrong meant either constant false alarms or missed failures.
The patent-pending piece: A novel approach to correlating wind shear patterns with micro-vibration signatures to predict blade fatigue before it appears in power output curves. This took 18 months of iteration. (Patent application: DE 10 2023 134 843.4)
Compliance: Industrial IoT in Germany means strict GDPR data residency, audit trails, and change management. I built ISO 27001 controls in from day one — not bolted on at the end.
Deployed across the full fleet in 4 months. Measured over 12 months of production operation:
Engineers now get actionable alerts 3 days before failures — not 3 minutes. That's the difference between a scheduled maintenance window and an emergency site visit.
This isn't a mock. It's a live AI agent — ask it anything about vertical AI, energy systems, or working with Homan.
Powered by n8n + LLM · Real responses, not canned replies