Kaden VanHoecke
ML Engineer | BCI & Real-Time Systems

Kaden
VanHoecke

ML Engineer. Founder of VHTech LLC. Ship production AI systems that run daily under real operational constraints.

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Background

About

I build production AI systems that automate expert workflows. Not demos, not prototypes. Systems that run daily under real operational constraints.

My path here is unusual: mechanical and nuclear engineering coursework at Kansas State, seven years running sprayer equipment on commercial job sites while teaching myself to ship software, then full-time applied ML. That trade background shapes how I build. Systems have to work on the job, not in a deck.

I come from a strong engineering and mathematics foundation, not a CS degree. What I feature is real, built, and grounded in measurable work.

Now moving toward real-time signal processing and brain-computer interfaces. Building SPDRbot, a quadruped on a Jetson Orin Nano, as the bridge: closed-loop control, sensor integration, and the latency intuition that BCI work demands.

Selected Work

Projects

CloudBooks

React 18
live

Multi-tenant task tracking and business management SaaS for informal employers: property owners, parents, and anyone running real work without a formal business entity. React 18 + TypeScript + Tailwind on Vite 6, backed by Supabase Cloud with RLS on 25 tables, Realtime subscriptions, and 9 Edge Functions. 21 domain service modules, pgcrypto-encrypted AI keys and OAuth tokens, Stripe subscription billing gated at the database via the check_feature_limit RPC, and QuickBooks Online OAuth2 with bidirectional sync. 288 unit and 61 Playwright E2E tests, GitHub Actions CI/CD, PWA installable, deployed to Cloudflare Pages.

React 18 TypeScript Tailwind CSS Vite Zustand TanStack Query React Router v6 Supabase Auth

McQueenyML

Frontend: React
built

Parses manufacturer pricing sheets (Armstrong XML, WaterFurnace, Condair, Marlo, Johnson Controls PDFs) into formatted Excel workbooks, matches line items against 195 product scope templates through a deterministic-first pipeline (pattern, fuzzy, pgvector embedding, then AI fallback), and generates MGI or JCI Word scope documents. React 18 + TypeScript frontend, Python 3.11 + FastAPI backend, PostgreSQL via Supabase with pg_trgm and pgvector, and Ollama Qwen3 14B for project lifecycle analysis. A real-time R:\ drive filesystem watcher correlates events to 419 SharePoint-synced projects, 3063 PSR equipment orders, and a 394K-row network drive file index. 7 NSSM Windows services run the stack in production, fronted by Cloudflare Tunnel with LAN-routing bypass for uploads over 100MB. ~111 API endpoints across 21 routers, 223 pytest and 55 vitest tests.

195
scope templates
419
tracked projects
Frontend: React TypeScript Vite Tailwind CSS. Backend: Python 3.11 FastAPI uvicorn. Database: PostgreSQL via Supabase (pg_trgm pgvector). ML: Ollama (qwen3:14b nomic-embed-text). Infrastructure: 7 NSSM services on Windows 11

Adze: AI for SOLIDWORKS

C# .NET 4.8
built

Adze is an in-process SOLIDWORKS COM add-in that reads the active CAD document through 18 typed tools (feature trees, dimensions, mates, custom properties, rebuild diagnostics, reference graphs, closed-file search) and reasons over the results with an agentic tool-calling loop. Write operations follow an 8-step safety lifecycle: plan, preview, approve, apply, verify, trace, undo-label, history. Runs against OpenAI, Anthropic, OpenRouter, Ollama, or LM Studio, with a deterministic fallback when no provider is configured. Built on C# .NET 4.8 and the SOLIDWORKS API. 666 NUnit tests across broker orchestration, tool implementations, write infrastructure, and closed-file OLE indexing.

C# .NET 4.8 SOLIDWORKS COM API NUnit 3 MSBuild PowerShell WinForms WebBrowser

Oracle (Trade Signal)

Python 3.13 (miniconda3)
built

Self-directed market analysis system that generates testable hypotheses, backtests them against historical data, and manages a $499K paper portfolio behind 8 risk gates. Five cortexes (LSTM, FinBERT sentiment, Perplexity LLM deep analysis, rules-based momentum, and a local indicator ensemble) feed an adaptive-weight Oracle brain that reweights across 12 market regimes. The distinctive finding, surfaced by 2,987 logged predictions with outcome tracking, is that the system's own BUY signals are anti-predictive for going long at 27% accuracy but produce a 62.8% win rate as contrarian shorts with 2:1 W/L ratio. A multi-venue adapter layer (Alpaca equities wired live, plus verified fee models for Coinbase, Polymarket, and Kalshi) routes signals to whichever venue fits. Python 3.13, FastAPI, SQLite WAL, systemd daemons, Sentry observability, 506 tests across 22 files.

987
logged predictions
Python 3.13 (miniconda3) FastAPI SQLite TensorFlow PyTorch transformers (FinBERT) React/TypeScript/Tailwind (ava_hub UI)
Also Building

Project Ava

Personal AI development studio running headless on a dedicated Ubuntu server, accessed exclusively over Tailscale mesh from any device. React 19 PWA over an Express 5 API, with SQLite (better-sqlite3 WAL) for durable session continuity, ChromaDB and sentence-transformers for semantic knowledge search, and GitNexus code intelligence running impact analysis on every edit. OpenClaw handles agent orchestration through the Claude Agent SDK 0.2.84. Acts as the operator for a template framework that deploys skills, hooks, and documentation to ten downstream projects.

3D Printing

End-to-end 3D printing system for LEGO-compatible part production, from parametric geometry and automated slicing to direct printer control and telemetry monitoring. Includes a dimensional calibration workflow with measured compensation sweeps and profile locking targeting ±0.05 mm tolerance on clutch-critical geometry. Built as a hardware-adjacent manufacturing pipeline with resolved slicer profiles, automated exports, and production telemetry.

SPDRbot

Robotics platform in active development for building practical skill in low-latency control, hardware debugging, sensor integration, and on-device inference. Represents a deliberate move from software-only ML systems into physical autonomy and real-world control constraints.

Capabilities

Skills

Python 92%
TypeScript 92%
Automation 92%
PyTorch 78%
Docker 78%
Computer Vision 78%

Also in the toolkit

Languages

C# C/C++ SQL

Frameworks

React Node.js Express FastAPI .NET / COM Interop YOLO

Tools

n8n SQLite PostgreSQL Supabase ChromaDB Git / GitHub Tailscale Vite SOLIDWORKS

Domains

CAD / Mechanical Engineering
Résumé

Experience

Founder & ML Engineer

VHTech LLC · Lenexa, Kansas

Jan 2023 — Present
  • Founded ML engineering consultancy supporting production automation and applied AI systems
  • Built and operate McQueenyML, a production HVAC estimating platform that reduced workflows from 4 hours to 12 minutes across 553K indexed files
  • Built Adze, a native SOLIDWORKS AI add-in with 19 typed grounding tools and governed write safety
  • Built Project Ava, a persistent multi-agent infrastructure platform with structured memory, private remote access, and operational monitoring
  • Built CloudBooks, a multi-tenant SaaS platform with real-time sync, subscription billing, and database-enforced access controls
  • Designed automation pipelines that parsed 10K+ XML specifications with 100% accuracy and proved the business case for full ML workflow automation
Python TypeScript C# React Node.js FastAPI Express .NET

ML Engineer (Contract)

McQueeny Group Inc. · Lenexa, KS (Remote)

Dec 2025 — Present
  • Sole developer of McQueenyML, a production HVAC estimating automation platform used by a sales engineering team
  • Built a 5-tier product matching pipeline combining pgvector embeddings, trigram search, and Claude fallback with a self-improving corrections loop
  • Reduced estimating workflows from 4 hours to 12 minutes while maintaining 100% XML parsing accuracy across 10K+ specifications
  • Automated SharePoint and Microsoft Graph ingestion across 372+ active projects and 3,000+ equipment orders
  • Operate a 7-service Windows production stack and maintain a 553K-row file index with real-time project lifecycle tracking
Python React TypeScript FastAPI PostgreSQL pgvector XML Excel

Sprayers Technician

Wallboard Specialties · Lenexa, KS

Jun 2018 — May 2025
  • Operated spraying equipment and handled material mixing on commercial job sites
  • Worked across seasonal and full-time trade crews while maintaining production pace and job site coordination
  • Spent 7 years in physical trade work while independently transitioning into software engineering and ML

Education

Mechanical Engineering Coursework

Kansas State University · Manhattan, Kansas

Aug 2021 — May 2023
Get In Touch

Let's Build Something

Targeting ML Engineer roles with real-time signal processing and BCI focus. Open to consulting on production AI systems and ambitious applied ML work.

📍 Kansas, United States