Revolutionizing OTR's AI Platform

OTR Solutions can fast-track its Copilot and AI-modernization roadmap by bringing on a Principal AI & ML Solution Architect with extensive experience designing and deploying enterprise-grade AI systems. Hiring me means OTR gains a hands-on leader ready to embed production-ready GenAI into your Clutch banking, fuel-card, and HighRadius-to-NetSuite flows—unlocking faster cash-application, richer customer experiences, and new revenue streams within the first 90 days.

Enterprise RAG Copilots

At AT&T, I led the creation of cross-cloud RAG copilots that weave Azure OpenAI, Azure AI Search, and Power Automate into ServiceNow and Salesforce workflows, slashing incident-triage time and surfacing knowledge on demand.

Rapid, High-Impact POCs

To show how quickly I convert ideas into value, I built a container-first, multimodal site-survey proof-of-concept—a MERN app delivered as Docker microservices on AKS that pairs Whisper voice capture with GPT-4 Vision—cutting field-survey time by 35%.

Microsoft-Certified Expertise

My hands-on leadership is backed by a Microsoft-centric skill set validated by Azure AI Engineer Associate and Azure AI Fundamentals certifications, ensuring alignment with OTR's technology stack.

View My Resume

High-Level Two-Year Roadmap

This roadmap provides a bird's-eye view of the key phases and objectives for the first two years. The plan is structured to deliver immediate value (Year 1), build a foundational platform (Year 1), and then accelerate innovation (Year 2).

1

Phase 1: Foundation & Adoption

First 90 Days

Rapidly validate research, build key relationships, establish a robust governance and security foundation, and launch a successful Microsoft Copilot adoption program to demonstrate immediate, measurable value.

2

Phase 2: Multi-Agent Architecture

Months 4-12

Design, build, and govern a robust, enterprise-wide multi-agent platform. This phase focuses on creating the foundational "AI factory" to enable future innovation at scale.

3

Phase 3: Product Innovation

Year 2

Aggressively leverage the platform to rapidly develop and deploy innovative, AI-powered features that enhance OTR's products and create significant competitive advantage.

1

Phase 1: Foundation & Adoption (First 90 Days)

Rapidly validate research, build key relationships, establish a robust governance and security foundation, and launch a successful Microsoft Copilot adoption program to demonstrate immediate, measurable value.

2

Phase 2: Multi-Agent Architecture (Months 4-12)

Design, build, and govern a robust, enterprise-wide multi-agent platform. This phase focuses on creating the foundational "AI factory" to enable future innovation at scale.

3

Phase 3: Product Innovation (Year 2)

Aggressively leverage the platform to rapidly develop and deploy innovative, AI-powered features that enhance OTR's products and create significant competitive advantage.

Detailed Action Plan

This section details the specific actions, focus areas, and key deliverables for each stage of the plan. Each phase is designed to build upon the last, ensuring a logical progression from foundational work to full-scale implementation. Use the tabs to navigate through the two-year timeline.

Departmental AI Use Cases & Business Impact

These are tangible, high-impact AI opportunities identified for key departments, grounded in OTR's specific technology stack. These use cases form the primary backlog for the **Phase 3: Product Innovation** efforts in Year 2. Filter by department to explore how AI can drive value across the organization.

Leadership, Departments & Technology

Understanding the organizational structure and the technology used by each team is critical for successful AI implementation. This section provides an overview of key departments, their leaders, and their primary SaaS platforms. Filter by department to see the specific tools in use.

Rapid Prototype: The "AI Fuel-Coach"

To demonstrate the ability to quickly develop and integrate a high-value, AI-driven feature into OTR's existing ecosystem, this section outlines a tangible Proof of Concept (POC). This POC is designed to be built within one week during Phase 1 and serves as a concrete example of the hands-on architectural and development skills I bring to the table.

A Note on the POC Approach

This Proof of Concept was intentionally designed with a technology stack that mirrors OTR's own environment—leveraging .NET for microservices and a React-based front-end. This approach was chosen to not only demonstrate a high-value AI feature but also to begin familiarizing myself with the specific nuances of OTR's business and technical landscape. All supporting documentation, from use cases to acceptance criteria, is structured to align with common Azure DevOps practices. The prototype itself was developed with rapid, AI-assisted tooling (Cursor), and all documentation is available via GitHub to showcase the velocity at which we can move from idea to functional code.

Problem & Business Win

The Gap: OTR's current app shows *where* the cheapest fuel is now, but doesn't provide a strategic fueling plan considering future price volatility, route length, and truck specs. This leaves significant money on the table for carriers.

The Win: An "AI Fuel-Coach" that delivers a "when, where, and how much to buy" plan can save $0.07-$0.12 per gallon. This is a savings of $14-$24 on a standard fill-up, often enough to offset the entire factoring fee for that load. It transforms a discount tool into a powerful profit engine.

Strategic Value for OTR

✓ Demonstrates full-stack, agentic AI architecture skills.
✓ Creates a feature with a clear, demonstrable ROI.
✓ Increases customer "stickiness" and strengthens OTR's value proposition.
✓ Showcases rapid, agile development velocity.

POC Architecture & Agentic Workflow

OTR Mobile App (React Native)

|-- "Fuel-Coach" Screen

↓ HTTPS/JSON

Azure Cloud

|-- Fuel-Coach API (.NET 8)

|-- ↓ gRPC

`-- LangChain Agent (Python)

|-- Orchestrates -->

`-- [Route, Price, Math Tools]

1. Driver Input

Origin, destination, truck profile.
2. Route Calculation

Tool gets all in-network fuel stops on path.
3. Price Forecasting

Tool predicts 24hr price change at each stop.
4. LLM Plan Generation

GPT-4o agent synthesizes data to find optimal fueling strategy.
5. Actionable Itinerary

Returns simple plan to the mobile app.

Technology & MLOps

This section provides a technical deep-dive, visualizing OTR's system architecture and outlining the continuous learning loop that ensures our AI solutions improve over time. A robust MLOps practice is key to transforming AI from a one-time project into a self-improving, appreciating asset.

Existing AI Resources to Leverage

A key principle of this plan is to build upon OTR's existing strengths. We will not be starting from scratch; instead, we will extend and enhance the current AI capabilities and resources to accelerate our progress.

OTRintelligence Engine

This proprietary machine learning engine for invoice and document processing is a significant asset. In Phase 2, we will treat it as a foundational "tool" that our new multi-agent platform can call upon. New agents can leverage its existing capabilities for document verification, freeing them to focus on higher-level orchestration and decision-making.

AI Architect Role & Ambassadors

The established AI Architect role and the "AI Ambassadors" program demonstrate a company-wide commitment to AI. This plan will formalize this into an AI Center of Excellence (CoE). The existing ambassadors will be the first members of the "Copilot Champions" cohort, and the AI Architect role will naturally evolve to lead the design of the multi-agent platform.

System Architecture Overview

Copilot Optimization & Learning Flow

Source Library

This plan was informed by research compiled from a variety of public sources to map OTR Solutions' product suite, technology stack, leadership structure, and operational priorities.