Software Architect | Production AI | Agentic Systems | Author | Army Veteran | Girl Dad

I help people go from a real problem to a product that can earn money. Bring a problem. Manifest a solution. Check it before the market. Then get paid.

I still design and ship production AI: multi-agent systems, RAG over messy data, evals and guardrails, and UI teams actually use. Remote. Flexible across time zones.

The loop I am building toward, and the products I am putting in front of the market.

• ManifestOS.studio: bring a problem, not an app idea. A problem-solving network that can build software, so a solution can be described, used, shared, and adapted.
• AppHole.pro: check a shipped or nearly shipped app for quality and issues (broken flows, onboarding, payments, trust) before customers find them.
• Make it RAIN: you built something real. Find who may pay, stress-test the offer, and pick the next conversation worth having.

Shipping is the starting line. A great app can still be a bad business. The book is about commercialization: who may pay, why good products stay unpaid, and how builders fix the commercial side.

• Amazon: Great App. Bad Business.
• More about the book, the company, and related work: ReliableAINetwork.com

Other titles: The Agentic AI Week, The AI Architect's Handbook, The AI-Driven Organization, AI Strategy and Implementation.

Select senior work through Reliable AI Network: AI architecture and production readiness, agentic systems and multi-agent workflows, RAG and knowledge assistants, Python / FastAPI backends, reliability, observability, and governance, plus technical roadmaps that connect a product to positioning and monetization.

Typical shape: a short intake, a working session, artifacts you can act on, and optional follow-up (reviews, prototypes, eval harnesses). I also do shipped-to-paid coaching for builders who already have something in the world.

I consult and build for teams that want production readiness, not just demos. Remote only. Flexible worldwide hours.

ReliableAINetwork.com

Same craft, different surface: production AI, not demos.

• Architect and operationalize agentic AI and MLOps pipelines (deploy, monitor, retrain).
• Build agents, retrieval, evals, and the UI that ties it together.
• Lead research-to-production work with governance and auditability intact.

Proof already on this profile: multi-agent risk analysis for PwC DeepRisk, ethics and compliance controls in production, cloud and CI work that cut infrastructure cost and sped up deploys, and mentoring for repeatable delivery. Systems gallery: agentic-portfolio.

• AI architecture: agentic systems, LLM orchestration, evals and guardrails, RAG, NLP, CV
• Build and serve: Python, FastAPI, Pydantic, LangChain, LangGraph, Docker, GitHub Actions
• Cloud and MLOps: Azure, AWS, Kubernetes, Terraform, MLflow
• Governance: security, observability, auditability, regulatory alignment

Top skills: AI architecture, production readiness, MLOps

Lab and systems work, if you want the internals. These sit behind the loop above. They are not the main offer.

• Epiphany: agent orchestration, retrieval patterns, and production pipelines
• MyRiskAgent: risk-focused agent prototypes and RAG experiments
• ACE-Lab: architecture and evaluation tooling for agentic systems
• perception-lab: CV and perception components
• EdgeSight-QA: QA and evaluation for edge and enterprise data