VMG Systems |
Technical Consulting & Architecture
We come in, strip the technical debt, and ship a production system using our Clean Slate methodology. IaC from day one, zero ClickOps, full observability. Most recent engagement: Automotive Voice AI Platform (VMG client, Q1 2026) — complete full-stack rebuild, migrating legacy services to a modern, fully observable architecture with 100% Terraform coverage. Read the case study →
AI Integration & Data Pipelines
Your data is already valuable. I build the pipelines and retrieval layers that make it usable by AI.
- Vector Database implementation (PostgreSQL/pgvector)
- RAG workflows using Gemini Pro and Claude
- Voice AI data capture and processing pipelines
Cloud Infrastructure & Modernization
I replace manual cloud configuration with Terraform-managed, reproducible infrastructure. GCP and AWS, built to last.
- Custom GCP and AWS (S3) architecture
- Zero-trust infrastructure via Tailscale
- Docker-based containerization and migration
Custom Platform & API Engineering
FastAPI backends, React Native frontends, and the integration layer between them. Shipped in weeks, not months.
- React Native with native background persistence
- High-throughput Python API architecture
- Rapid production launch with full observability
VMG SYSTEMS, LLC
How We Work
Most engineering failures are architectural, not technical. We fix the foundation first, then build on top of it.
The Clean Slate Methodology
Every engagement starts with rules. Before writing a line of code, we define how the system is built, tested, and observed. That upfront work is what makes rapid execution possible.
- Monorepo Architecture: A unified structure provides AI agents and engineers full context across the entire stack.
- Infrastructure as Code (IaC): "ClickOps" is banned. Every resource is codified via Terraform from the start to ensure security and reproducibility.
- The "Plan → Act → Validate" Loop: No massive features in one go — every task is a documented spec executed through rigorous validation.
- LLM Output Testing: AI models drift. Eval datasets built on day one; regression tests run on every deployment to catch accuracy drops before users do.
Technical Standards
Observability on day one.
Langfuse is running before the first feature ships. Every AI call is logged with inputs, outputs, and latency. When something breaks, you know why before your users do.
Engagement Model
Two modes: embedded or sprint. Embedded means we're your fractional chief architect. Sprint means a defined rapid build with a fixed scope and a production handoff at the end.
- Architecture Audits: 1-2 week deep dives into existing codebases.
- Canary Launches: Rapid MVP development cycles.