01
Start with the decision
The first question is not which model to use. It is which decision or workflow needs to improve, who owns it, and what being wrong costs.
Step 01
Enterprise AI · Aviation-grade
AI rarely fails
in the demo.
It fails in production.
I build full-stack AI systems that survive real enterprise environments — private LLMs, RAG platforms, decision-intelligence tools, and governed workflows.
Private LLMs
RAG
Decision Intelligence
Workflow Automation
Enterprise AI Governance
Production AI Pipeline
governed · live
Grounded · Sources visible
Reviewed · Human gates
Audited · Trace retained
Rollback · Safe-mode ready
Sanitized proof · executive view
Selected proof, designed to read at a glance.
Most of my strongest AI work was built inside confidential enterprise environments. Numbers below use sanitized, modeled, or observed categories.
0%+
reduction in selected manual review workflows
Estimated
0+
internal AI systems moved from prototype to live use
Sanitized
0+
annual workflow hours addressed through AI automation
Modeled
0%
deployment under access-control and infrastructure constraints
Observed
Production AI Systems
Live use
Built internal AI systems across aviation operations, enterprise knowledge workflows, and workflow automation.
Sanitized
Private LLM / RAG
Secure retrieval
Designed secure knowledge-assistant workflows with source grounding and department-aware retrieval.
Observed
Operational AI
Decision support
Built decision-intelligence tools for resource planning, recovery, and operational support.
Observed
Governed Deployment
Enterprise-ready
Designed around on-prem infrastructure, SSO/RBAC, auditability, rollback thinking, and production constraints.
Modeled
01 — Selected systems
Production-inspired AI systems, grouped by what they do.
Sanitized case studies of internal AI systems that went from prototype to live use. Each card links to the case study with full architecture, impact, limitations, and reliability thinking.
02 — How I build
Production AI is a system problem, not a model problem.
I do not start with the model. I start with the decision, the workflow, the user, and the cost of being wrong. The model is one piece of a much larger, governed product.
02
Map the workflow
Map current handoffs, delays, data sources, failure modes, and approval points before writing a single line of integration code.
Step 02
03
Build the smallest useful system
Do not build the full AI platform first. Build the smallest version that demonstrably changes a real workflow, then harden from there.
Step 03
04
Design for adoption
A technically correct AI system is useless if users do not trust it or cannot fit it into their daily work. Adoption is a design problem.
Step 04
05
Govern inside the product
Governance is not a PDF. It lives in access control, audit trails, approval gates, review states, rollback, and human confirmation points.
Step 05
06
Measure and harden
Production AI needs monitoring, fallback behavior, safe mode, rollback, data-quality checks, and clear ownership. Then iterate.
Step 06
03 — Architecture
Architecture patterns I reach for first.
Three reference architectures that have shaped almost every production AI system I have shipped.
Pattern · 01
Private LLM / RAG Architecture
Document ingestion → chunking → embeddings → retrieval → grounded answers → access control
Pattern · 02
Decision Intelligence Architecture
Operational data → rules/constraints → scoring/recommendations → user workflow → audit trail
Pattern · 03
Deployment Architecture
FastAPI → Postgres → workers → Docker → Nginx → SSO/RBAC → monitoring
04 — Reliability
How I evaluate AI systems in production.
A good demo is not a good system. I evaluate AI by grounding, failure modes, human review, operational risk, adoption, and production readiness — not just whether the answer sounds right.
01
Grounding
Can the system show where its answer came from?
02
Failure Modes
Where can the system be wrong, incomplete, stale, or misleading?
03
Human Review
Which decisions require human confirmation before action?
04
Operational Risk
What happens if the system is slow, unavailable, or wrong?
05
Adoption
Are users actually changing how they work because of the system?
06
Production Readiness
Can the system be deployed, monitored, rolled back, and governed?
05 — Case studies
Sanitized case studies that read like systems, not stories.
Each case study covers the operational problem, the system design, the tradeoffs, the impact signal, the limitations, and the reliability thinking.
06 — Reference implementations
Public demos that recreate the engineering patterns, not the data.
Most production work was built inside confidential environments. These public demos reproduce the core engineering patterns with mock data, sanitized workflows, and reference code.
07 — Writing
Short, practical writing on production AI.
On production AI, aviation AI, enterprise governance, decision intelligence, and the gap between AI demos and operational deployment.
08 — Engage
Building production AI systems is where I do my best work.
Open to applied AI, enterprise AI architecture, LLM systems, aviation AI, and product-focused AI engineering opportunities.
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