Engineering
Evidence
Engineering evidence of deep AI systems — prototypes, demos, and technical artifacts from real problem domains.
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OrenAI
Breast Imaging Intelligence
Problem
Multi-modal detection gaps in early cancer workflows — missing cross-modality fusion. Traditional CAD systems analyze single image types, missing subtle anomalies visible only when combining optical, thermal, and DICOM data.
Approach
Vision Transformers + CNN ensembles for LiDAR + thermal + DICOM fusion; AR visualization and voice UI. Targeting real-time inference on edge devices.
Artifacts
- →AR interface mockup
- →Detection overlay
- →Architecture diagram
Dali Cloud Agent
Autonomous Infrastructure Control
Problem
Multi-cloud infrastructure spanning AWS, Azure, and on-premise requires constant human intervention. Mean time to recovery for Level 1 incidents is typically 45+ minutes, with 24/7 on-call rotations causing team burnout.
Approach
Agentic control plane using Model Context Protocol (MCP). Autonomous agents that monitor, reason, and remediate across all environments. Currently prototyping core MCP integration and agent reasoning capabilities.
Artifacts
- →Control plane dashboard prototype
- →Infrastructure visualization mockup
- →Autonomous remediation interface
- →Architecture diagram
Results
Core MCP integration in progress, agent reasoning capabilities under development.
Context-Aware Generative Digital Branding
AI-Accelerated Website & Brand Execution
Problem
Local businesses need modern, responsive websites without the slow, expensive 6-month production cycles of traditional design agencies. Manual design and development often lead to inconsistent brand experiences, hard-to-maintain component systems, and long turnaround times for updates.
Approach
A human-directed workflow that uses AI selectively to accelerate engineering and design—not replace it. Core brand systems, layout logic, and component architecture are built by hand. AI is applied only where it provides leverage: validating layout options, generating controlled variations, stress-testing responsiveness, and speeding up boilerplate code creation.
The build process remains context-aware and craftsmanship-driven. Design systems are manually constructed, AI supports rapid iteration and verification, and every component and code output is reviewed and refined before production. This delivers dramatic speed gains without compromising control, quality, or technical rigor.
Artifacts
- →Live client websites (multiple deployments)
- →Generated + manually curated component libraries
- →Design system documentation
- →AI-assisted code generation reports & verification logs
Results
Delivered complete website revamps for multiple clients using a human-led, AI-accelerated workflow. Produced over 50 pages, 200+ reusable components, and 15,000+ lines of vetted production code, while maintaining strict brand consistency. Reduced delivery timelines from 4–6 months to 3–6 weeks, with consistent quality and maintainability.
Impact Metrics
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