Vision
The Intent Gap
74% of companies report no tangible value from their AI investments. The problem isn't the models—frontier AI is extraordinarily capable. The problem is that nobody has built the intent layer: the infrastructure that translates organizational goals, values, and decision-making frameworks into something AI agents can actually act on.
OKRs were designed for humans. They assume contextual judgment, institutional knowledge absorbed over months of hallway conversations, and the professional intuition a senior employee carries after years on the job. Agents have none of that. They need explicit alignment before they start working—not six months of osmosis.
This is the gap we close.
From NASA JPL Mission-Critical Systems to Enterprise AI Innovation
Our expertise in cutting-edge technology was profoundly enhanced through our team's experience at NASA JPL's Physical Oceanography DAAC, where we worked alongside teams supporting Mars missions and deep space exploration. There, we mastered the rigorous engineering discipline required for systems where failure isn't an option - architecting robust CI/CD pipelines, implementing NASA's stringent data governance protocols, and developing tools that enabled scientists to process petabyte-scale satellite data with unprecedented efficiency.
This foundation in mission-critical systems engineering now drives our approach to AI/ML solutions. We specialize in designing enterprise-scale RAG architectures, spatial AI systems, and intelligent document processing platforms that combine the reliability demanded by space exploration with the innovation required by modern business.
From pioneering hexagonal spatial indexing systems to implementing advanced LLM integrations for federal infrastructure agencies, we bring NASA-grade engineering excellence to every project. Today, we help organizations transform their most complex data challenges into competitive advantages through scalable AI solutions, always applying the lessons learned from supporting humanity's boldest endeavors - where precision, reliability, and innovation converge.
Core Competencies
Organizational Intent Architecture
- Intent Engineering: Machine-readable expressions of organizational objectives, decision boundaries, escalation criteria
- AI Workflow Architecture: Capability mapping (agent-ready vs. human-augmented vs. human-only workflows)
- Delegation Frameworks: Translating leadership principles into agent decision logic
- Context Infrastructure: MCP integration, data governance, semantic consistency across systems
Technical Excellence
- AI/ML Technologies: LangChain, LlamaIndex, Haystack • Vector Databases • RAG Systems • Prompt Engineering • Fine-tuning
- Cloud & Infrastructure: AWS (Full Stack) • Azure • Databricks • Serverless Architecture • Microservices • Docker/Kubernetes
- Data Engineering: Apache Spark • Python • Real-time Processing • ETL/ELT • Data Lakes • Stream Processing
- Geospatial Technologies: ESRI ArcGIS Suite • QGIS • PostGIS • GDAL • Remote Sensing • H3 Hexagonal Indexing
Strategic Capabilities
- Solution Architecture: System design for scalability, reliability, and maintainability
- Technical Leadership: Cross-functional team coordination and mentorship
- Innovation Management: R&D strategy, proof-of-concept development, and technology evaluation
- Business Alignment: Translating complex business requirements into elegant technical solutions
Certifications
- Databricks Generative AI Solution Development (2025)
- CompTIA Security+ (2024)
Services
Intent Engineering & Organizational AI Architecture
The missing layer between your strategy and your AI systems.
The difference between 30% productivity gains and 300% transformation isn't the model—it's whether your agents understand what your organization actually values.
- Goal Translation Infrastructure: Converting human-readable objectives (OKRs, strategic priorities) into agent-actionable parameters
- Decision Boundary Design: Defining what agents can decide autonomously vs. what requires human judgment
- Value Hierarchy Encoding: Teaching AI how your organization resolves trade-offs (speed vs. quality, cost vs. thoroughness)
- Alignment Feedback Loops: Measuring and correcting intent drift over time
"A company with a mediocre model and extraordinary organizational intent infrastructure will outperform a company with a frontier model and fragmented organizational knowledge—every time."
AI/ML System Architecture & Implementation
End-to-end design and deployment of enterprise-scale artificial intelligence solutions.
- Large Language Model (LLM) Integration: Custom RAG architectures, prompt engineering, and fine-tuning strategies
- Intelligent Document Processing: Advanced document cracking, semantic search, and knowledge extraction systems
- Model Context Protocol (MCP): Seamless integration of AI capabilities into existing enterprise applications
- Multi-Modal AI Solutions: Computer vision, NLP, and hybrid systems for complex business challenges
Data Engineering & Cloud Architecture
Scalable infrastructure design for petabyte-scale data processing and real-time analytics.
- Cloud-Native Solutions: AWS, Azure, and Databricks architectures optimized for cost and performance
- Real-Time Data Pipelines: Stream processing, event-driven architectures, and sensor data integration
- Big Data Analytics: Apache Spark optimization, distributed computing, and data lake design
- DevOps & CI/CD: Infrastructure as code, automated testing, and continuous deployment strategies
Geospatial Intelligence & Analytics
Bridging the gap between spatial data and actionable business intelligence.
- Spatial AI Integration: Natural language interfaces for GIS platforms and spatial reasoning systems
- Remote Sensing Analytics: Satellite imagery processing, change detection, and predictive modeling
- Location Intelligence: H3 indexing, spatial optimization, and geographic pattern recognition
- Enterprise GIS Solutions: PostGIS, ArcGIS integration, ESRI map services, and custom geospatial applications
The Intent Engineering Approach
- Intent Audit: Map your organizational knowledge—the judgment, trade-offs, and contextual decision-making currently locked in senior employees' heads
- Architecture Design: Build the context and intent infrastructure that makes this knowledge agent-accessible
- Workflow Classification: Identify which processes are agent-ready, which need human-in-the-loop, and which remain human-only
- Alignment Systems: Deploy feedback mechanisms that detect and correct intent drift before it becomes a problem
Products
Site AI Engineer - Construction Industry Transformation
Major ENR Top 20 General Contractor | 2026-Present
Embedded AI engineering on active mega-construction projects, translating field pain points into deployed AI solutions
Pioneering the Site AI Engineer role—a new position bridging construction operations and AI implementation on billion-dollar infrastructure projects:
- Intent Engineering in Practice: Conducting 18+ weekly field engagements to map organizational knowledge, identifying decision boundaries and workflow classifications for AI deployment
- Pain Point Discovery: Systematic identification of high-impact automation opportunities across RFIs, submittals, safety compliance, QA/QC, and cost management
- Safety AI Suite: Designing automated compliance gap detection, risk score integration with P6 schedules, and AI-powered safety meeting preparation—projected to save hours weekly per safety manager
- QA/QC Intelligence: Developing AI-generated photo descriptions for 55K+ inspection images, enabling searchable metadata across Procore inspection records
- Cross-Functional Convergence: Identifying systemic patterns where safety, QA/QC, and schedule delays cluster—enabling predictive intervention before issues compound
- Tool Integration: Working across Procore, eBuilder, CxAlloy, P6, Smartsheet, and OpenSpace to create unified AI workflows
Enterprise RAG Knowledge Management System
Federal Infrastructure Agency | 2024
Transforming document query capabilities through advanced AI/ML technologies
Led the development of an advanced RAG-based system that revolutionized document query and knowledge management capabilities for a major federal agency:
- Metadata-Enhanced RAG Architecture: Implemented expanded vector schemas with intelligent metadata filtering, improving response quality from basic summaries to professional-grade, authoritative outputs
- Performance Optimization: Achieved >50% reduction in token usage while enhancing accuracy through semantic-aware filtering
- Evaluation Framework: Developed comprehensive RAG evaluation harness with retrieval, response quality, and attribution metrics
- Enterprise Integration: Built prototype Databricks SQL connector enabling natural language queries across structured and unstructured data
- Impact: Elevated information retrieval to operational standards, establishing measurable trust metrics for continuous improvement
GeoAI Platform - DGGS Spatial Intelligence Server
Lead Architect & Developer | 2024-Present
Natural language interfacing with spatial data through Discrete Global Grid Systems (DGGS)
Architected and led development of an innovative GeoAI platform enabling natural language interaction with spatial data through DGGS-based global grid systems:
- DGGS Architecture: Pioneered hybrid approach combining hexagonal global grid indexing with automatic knowledge graph generation, achieving 10x faster spatial operations
- Performance Breakthrough: Delivered 85% I/O reduction, 70% faster query processing, and 50% storage optimization through columnar Parquet format with intelligent compression
- AI-Native Spatial Reasoning: Integrated vector databases for semantic search with spatial operations, enabling natural language GIS queries with sub-millisecond coordinate conversion
- Enterprise Deployment: Designed single-command deployment system with Docker orchestration, supporting both cloud and on-premise installations
- QGIS Integration: Developed native plugin enabling real-time hexagonal visualization and interactive spatial analysis within existing GIS workflows
- Technical Innovation: Combined DuckDB analytics with PostgreSQL/pgvector, processing 2,000+ features/second with 250MB memory footprint for 1M features
Industry Experience
- Construction & AEC: ENR Top 20 general contractor, mega-project AI deployment, field-embedded engineering
- Government & Defense: Federal infrastructure agencies, Department of Defense, NASA JPL
- Technology & Data: UberMedia, Databricks ecosystem
- Real Estate & PropTech: Market analysis and automation systems
- Financial Services: Algorithmic trading and risk analysis
- Engineering & Infrastructure: Arup global projects
- Research & Academia: Scientific computing and oceanographic systems
The Intelligence Race Is Over. The Intent Race Has Begun.
For three years, organizations asked: Who has the best model?
That question no longer matters. GPT-5, Claude Opus, Gemini—they're all extraordinarily capable. The differences between them matter far less than the difference between organizations that give AI clear, structured, goal-aligned intent versus those that don't.
The new question: Who has built the organizational infrastructure that lets AI operate with the fullest, most accurate, most strategically correct understanding of what the organization is trying to accomplish?
Live Demo
Experience our AI-powered parcel processing solution in action. This interactive demo showcases our intelligent document analysis capabilities with real-time natural language interaction.
Note: This demo may take a few seconds to load as the service wakes up. Powered by Chainlit and our custom AI models.
Contact & Connect
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Let's discuss how I can help your organization leverage cutting-edge AI/ML technologies to achieve your business goals.
References available upon request.