As a Principal AI Architect, PMP-Certified and MBA, I offer a rare combination: 6+ years of cutting-edge Generative AI specialization built on a foundation of 25+ years of Enterprise Engineering & Leadership. I bridge the dangerous gap between "Business Strategy" and "Technical Execution." While others prototype concepts, I translate ambiguous business requirements into high-performance, autonomous AI systems that deliver measurable ROI. I specialize in designing Enterprise Agentic Workflows, Secure RAG Systems, and Multi-Agent Orchestration for regulated industries (Insurance, Healthcare, Finance).
Currently, I focus on translating ambiguous business requirements into scalable AI solutions that deliver tangible ROI, ensuring data sovereignty and regulatory compliance.
- $1.8M Claims Automation: Architected an Agentic AI platform on AWS (Claude/Titan) that autonomously processes 80% of claims, reducing cycle time from 5.2 to 1.1 days.
- $1.0M Cost Avoidance: Engineered a secure RAG application for policy analysis, eliminating 100% of manual data capture for adjusters.
- 85% Efficiency Gain: Deployed a Multi-Agent GenAI platform unifying 52 reporting systems, saving $340K annually.
Below is a high-level representation of the Agentic AI Data Cleaning and Clustering Architecture I designed, utilizing Multi-Agent Orchestration
Below is a high-level representation of the Agentic Claims Processing Architecture I designed, utilizing Multi-Agent Orchestration to handle complex decision-making logic.
%%{
init: {
'theme': 'base',
'themeVariables': {
'primaryColor': '#BB2588',
'primaryTextColor': '#fff',
'primaryBorderColor': '#7C0000',
'lineColor': '#F8F9FA',
'secondaryColor': '#006100',
'tertiaryColor': '#fff'
}
}
}%%
graph TD
%% --- Define Classes ---
classDef liquidStart fill:#000000,stroke:#333,stroke-width:4px,color:#fff
classDef liquidAgent fill:#8E2DE2,stroke:#4A00E0,stroke-width:2px,color:#fff
classDef liquidModel fill:#F80759,stroke:#BC4E9C,stroke-width:2px,color:#fff
classDef liquidData fill:#00F260,stroke:#0575E6,stroke-width:2px,color:#000
%% --- Nodes ---
Input(["Claims Documents"])
subgraph Orchestration [" Multi-Agent Orchestrator "]
direction TB
Router{{" Router Agent "}}
subgraph Specialist_Agents [" Specialist Agents (CrewAI) "]
Policy[("Policy RAG ")]
Fraud[("Fraud Detection ")]
Medical[("Medical Encoder ")]
end
end
LLM["AWS Bedrock (Claude 3.5) "]
Decision{{"Decision Engine "}}
Output(["Approved/Rejected"])
%% --- Connections ---
Input --> Router
Router -- "Context Retrieval" --> Policy
Router -- "Risk Analysis" --> Fraud
Router -- "Entity Extraction" --> Medical
Policy -.-> LLM
Fraud -.-> LLM
Medical -.-> LLM
LLM ==> Decision
Decision --> Output
%% --- Apply Styles ---
class Input,Output liquidStart
class Router,Decision liquidAgent
class Policy,Fraud liquidData
class Medical,LLM liquidModel
%% --- Link Styling (Simplified) ---
linkStyle default stroke:#BB2588,stroke-width:2px,fill:none