Most companies are spending on AI.
Almost none are compounding from it.
Your retained Chief AI Officer. I own the AI strategy, roadmap, and delivery for revenue-stage teams so AI compounds into margin, speed, and operating leverage.
This is a monthly CAIO retainer, not hourly advisory: leadership meetings, hands-on delivery, and team enablement on one operating cadence. Built on Anthropic Claude, AWS Bedrock AgentCore, and the agent infrastructure to run it reliably.
Start with a CAIO discovery call and we map where AI should compound first in your P&L.
$ prefer async? get the AI Production Readiness Checklist
No spam. The checklist I run before I let an AI roadmap near production.
Your AI prototype works in demos.
But production is a different beast.
[ERROR] Hallucinations in critical paths
[ERROR] Prompt injection vulnerabilities
[WARN] Unpredictable token costs
[WARN] Observability gaps in agent workflows
The distance between “cool demo” and “reliable product” is wider than it looks.
$That's where I come in._
Credentials
Expertise Stack
30+ years of experience across five critical infrastructure layers — from enterprise commerce to modern AI/ML systems.
AI/ML Layer
[AWS Certified]
AWS Bedrock Agents, AgentCore orchestration patterns
Anthropic Claude (Sonnet/Opus) via Bedrock and API
Custom subagent & Claude Code skills development
Amazon SageMaker, Guardrails, Knowledge Bases
GCP Vertex AI, Gemini Models
MLflow, Langflow, Langfuse, LangSmith observability
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Application Layer
[20+ Years]
AWS Cognito, Lambda, API Gateway
Authentication at scale (ATG, Oracle Commerce patterns)
B2C/B2B application architecture
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Data Layer
[eCommerce Scale]
RDS, DynamoDB, ElastiCache, OpenSearch
Vector databases for RAG
Data pipelines (Glue, EMR)
Related Services
Infrastructure Layer
[HA/Enterprise]
EKS, EC2, LightSail, CloudFront
GCP GKE, Cloud Run
Multi-region, fault-tolerant architecture
Related Services
Operations Layer
[DevOps/MLOps]
CloudWatch, CloudTrail, Config
Kubernetes-native CI/CD
LLM observability (Langfuse, LangSmith)
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Core Capabilities
Bridging experimental AI and production systems. Each capability is backed by structured assessment modules for measurable outcomes.
> Click any capability to explore related modules
ROADMAP
premiumProduct roadmap alignment with business goals, technical debt prioritization, and long-term technical strategy
AI_SERVICES
premiumAI product development, LLM integration, MLOps infrastructure, and AI workflow automation
ORGANIZATION_LEADERSHIP
standardTeam structure, engineering management practices, hiring, retention, and technical leadership
AI_SERVICES
premiumAI product development, LLM integration, MLOps infrastructure, and AI workflow automation
DEVOPS
premiumDeployment automation, continuous integration, release management, and DevOps culture assessment
CODE_QUALITY
standardCode review practices, test coverage and strategy, refactoring priorities, and engineering quality standards
ARCHITECTURE
premiumSystem architecture review, scalability bottlenecks, performance optimization, and design patterns
SOFTWARE_ARCHITECTURE
standardSoftware design patterns, code organization, module boundaries, and architectural best practices
DATA_AI_READINESS
supportingData infrastructure, analytics capabilities, data governance, and AI/ML readiness assessment
AWS_CLOUD
premiumAWS architecture review, Kubernetes deployment patterns, infrastructure as code, and cloud cost optimization
DEVOPS
premiumDeployment automation, continuous integration, release management, and DevOps culture assessment
CYBERSECURITY_RISK
premiumSecurity posture assessment, vulnerability management, access controls, and risk mitigation strategies
BEDROCK_AGENTCORE
premiumAnthropic Claude integration via AWS Bedrock, AgentCore orchestration architecture, custom subagent development, and reusable skills libraries
AI_SERVICES
premiumAI product development, LLM integration, MLOps infrastructure, and AI workflow automation
Open Source Artifacts
Production tools built with the same engineering standards we use for paid work. Free to use. Actively maintained. Apache-2.0.
Agent Skills marketplace (Anthropic standard) for Claude Code, Cursor, VS Code
gh repo clone agentic-insights/foundrycargo install neocortxcargo install ycbustClaude Code Plugins
Production-ready plugins for Claude Code built with the Agent Skills open standard. Install via the marketplace.
Type-safe LLM extraction with code generation, schema design, testing, and multimodal support
Deploy LangGraph agents on AWS Bedrock AgentCore with managed runtime, memory, and tool gateway
Create, validate, and publish portable skills following the Agent Skills open standard
Adversarial code review based on Block's g3 dialectical autocoding research
PARA-based personal knowledge management with AI-friendly navigation
Professional terminal recordings with Charm's VHS for demos and documentation
claude plugin add <plugin-name>@foundryFree: AI Production
Readiness Guide
The checklist I run through before shipping any AI feature to production. Hard-won from deploying Claude integrations across 3 startups this year.
- →LLM hallucination mitigation patterns
- →Prompt injection defense checklist
- →Token cost control strategies
- →Agent observability stack setup
- →Production deployment checklist (Claude / Bedrock)
Get the Guide
Drop your email. I'll send the guide directly — no opt-in sequence, no drip campaign nonsense.
Prefer to talk first?
> Book a CAIO discovery call →Track Record
High-impact engineering solutions at scale.
Geospatial Platform Modernization
Legacy indoor mapping → modern multi-platform ecosystem
Registered Patent • 10+ engineers • Multi-platform SDKs
- →Replaced SVG artifacts with GeoJSON format (geodetic accuracy + digital twins)
- →Unified SDKs: iOS (Swift), Android (Kotlin), Web (TypeScript/MapLibre)
- →Solved scalability bottlenecks, enabled new market expansion
High Performance Pipeline
Tile-generation pipeline rewrite
48x faster • 24hrs → 30min • 62-worker distributed system
- →Re-architected parallel processing (ForkJoinPool → distributed workers)
- →Resolved concurrency and serialization bottlenecks
- →Enabled rapid iteration for mapping teams
Enterprise MLOps Infrastructure
Production AI infrastructure on AWS EKS
Langflow + Langfuse • High-availability • Security compliant
- →MLOps pipelines with visual workflow orchestration (Langflow)
- →LLM observability and tracing (Langfuse)
- →Cost-optimized containerized deployments with auto-scaling
Engineering Excellence DevOps
Automated quality gates and cloud migration
Docker/ECR • GitHub Actions • 3-layer testing
- →Migrated to containerized cloud-ready infrastructure
- →Unified CI/CD with automated semver releases
- →Unit + Integration + E2E testing with BrowserStack
About
I've run the engineering org — 30 years building at scale, including CTO and founder roles — so I know what “production” actually costs. Now I do one thing: I'm the retained Chief AI Officer for revenue-stage teams, owning AI strategy and delivery so it pays for itself.
Currently researching AI engineering workflows through mem8, a Claude Code plugin for workspace management and context engineering (research phase).
Now consulting independently to help startups and scale-ups navigate the challenges of building reliable AI systems. Focus on practical engineering problems that emerge when moving from prototypes to production.
Ready to turn AI spend into compounding results?
> Book a CAIO discovery call