Michael Russo — Principal AI Programmer

Services

Consulting and delivery centered on systems, outcomes, and operational reliability

AI Engineering & Automation

LLM integrations, agent orchestration, tool calling, and AI-augmented development workflows — from prototype to production.

Cloud & Backend Systems

Scalable APIs, serverless systems, and secure integrations on AWS and Azure.

Mobile Platform Development

Cross-platform mobile applications integrated with real-world operational systems.

Legacy System Modernization

Upgrade aging .NET or enterprise platforms to modern cloud architecture without disrupting operations.

Fractional CTO / Technical Advisory

Architecture reviews, system design guidance, and senior engineering oversight.

Computer Vision & OCR

Object detection, segmentation, and OCR pipelines for blueprint analysis, document processing, and production extraction workflows.

Featured Platform Impact

Long-term delivery and measurable business value

DiSC Mobile Logistics Platform

Cross-platform logistics mobility and document intelligence platform

Founder / Co-Owner (W-Interactive)

Problem

Transportation companies needed mobile workflows for drivers, document capture, and GPS-based stop tracking integrated with existing logistics systems.

Solution

Built a cross-platform logistics mobility platform with document capture (PDF-to-TIFF, deskew, crop), GPS stop tracking, and backend freight integration.

Impact

In active production use since 2005 across live freight operations
Handles GPS stop tracking, driver workflows, and proof-of-delivery document capture end-to-end

Android / iOS Flutter Xamarin C# ASP.NET .NET WCF Python SQL Server

Visit onefleet.pro →

Technical Proof

Selected visual walkthroughs showing applied computer vision, AI extraction, and MCP-based tooling

⬡ Stage 1

Detect — Blueprint Object Detection

Streamlit demo detecting countertops, sinks, and appliances in architectural blueprints utilizing a trained Faster R-CNN model.

◈ Stage 2

Scale — OCR Measurement Extraction

Extracting blueprint scale via OCR (docTR) to compute real-world dimensions, areas, and generate material cost estimates.

◉ Stage 3

Segment — Instance Segmentation

Pixel-perfect instance segmentation masks isolating exact countertop shapes for area computation and material estimation, leveraging a trained Mask R-CNN model.

Architecture: Faster R-CNN and Mask R-CNN utilized as the core backbones for detection and segmentation respectively · Training: 10,000+ annotated blueprint images · Infra: Model training executed on large GPU clusters · Demo: Streamlit interactive frontend

ScraperAI Platform

⚙ Stage 1

Dashboard & AI Provider Setup

Central dashboard for managing AI providers, prompts, scraping jobs, API keys, and analytics. Configure providers like OpenRouter and LM Studio with model selection and API key management.

✎ Stage 2

Prompt Engineering & Configuration

Create and manage scraping prompts with target URLs, custom instructions, system prompts, provider/model selection, output format (JSON/XML), and fetch method (headless browser or direct).

▶ Stage 3

Execution, Monitoring & Results

Monitor crawling jobs with status tracking, filtering, and pagination. View detailed job results including AI processing metrics (tokens, cost, confidence) and validated structured JSON output.

Backend: .NET 8 / ASP.NET Core · Frontend: React SPA · Scraping: Headless browser (Playwright) · AI: OpenRouter, LM Studio, Anthropic, OpenAI providers · Output: Structured JSON/XML with validation

MathMCP Server

∑ The Challenge

Complex DSP Concepts

Excerpt from a Digital Signal Processing textbook. The material is dense, mathematical, and difficult to parse for beginners or high school students.

✓ The Solution

AI Simplification (LM Studio)

MathMCP server connected to LM Studio local LLM. The agent ingests the textbook image, simplifies the concepts for a high school level, and generates practice questions.

Download full conversation log (PDF)

Tech: Model Context Protocol (MCP) · Model: Local LLM (LM Studio) · Input: Visual / OCR + Text · Goal: Educational simplification & accessibility

Selected Case Studies

Representative consulting and architecture work in a problem, solution, and impact format