Tom Sullivan

Product, Systems & AI

Welcome to my site! I use applied AI and product thinking to streamline operations and unlock new value. I bridge strategy and engineering to deliver agents, analytics, and experiences that stick.

  • Applied AI
  • LLM Agents
  • Systems Design
  • Prompt & Context Engineering
  • Conversation Design
  • Workflow Automation
  • Agent Evaluation
  • Testing & Validation
  • Documentation & Training

Current Focus

  • LLM agents: natural-language conversation; user-centric experiences that improve satisfaction and outcomes; scaling service offerings (quantity and quality) efficiently and affordably; prompt and context engineering to optimize performance; and reliable structured outputs when needed.
  • Structured agent evaluation, enhancement ideation, and refinement: define success measures, capture interaction data, review transcripts and failure cases, run small experiments, and ship improvements on a steady cadence.
  • Practical integrations that boost efficiency, productivity, and impact: apply AI to intake/triage, knowledge access, workflow automation, and analytics/reporting.

If you need a builder who can translate goals into working agents, data flows, and interfaces— let’s talk.

Projects

Sage (Wildlife Triage Agent)

AI-powered conversational agent built in Voiceflow that supports wildlife triage functions. Guides users through intake, provides species-specific triage advice, and ensures safe, empathetic support 24/7 until professional staff are available.

  • AI/LLM
  • Prompt Engineering
  • Voiceflow
  • Conversational Design
  • Workflow Architecture
  • Knowledge Base Design
  • Data Structuring
  • UX Writing
  • Branding

Support-in-a-Box (CX Ops Demo)

Lean CX ops stack that ingests tickets, auto-acks with AI + KB links, alerts Slack, tracks FRT/TTR/FCR/CSAT/NPS, and rolls metrics in a live dashboard—built with Google Forms, Sheets, and Apps Script.

  • CX Operations
  • Process Design
  • AI/LLM
  • Google Apps Script
  • Google Sheets
  • Google Forms
  • Slack
  • Gmail
  • Notion
  • Automation
  • Metrics

PokerScout

AI analyst that ingests a Hendon Mob player profile URL and outputs a standardized scouting dossier.

  • Agent.AI
  • LLM
  • Prompt Engineering
  • Product Design
  • JSON Schema
  • Data Parsing
  • HTML/Templating
  • Error Handling

Range Sage

AI poker coach for No-Limit Hold’em that converts any spot into clear GTO ranges—with explanations, exploit notes, combo counts, and rapid drills.

  • AI/LLM
  • Prompt Engineering
  • System Design
  • Custom GPT
  • RAG Architecture
  • Retrieval Tuning
  • Content Normalization

About

I use applied AI and product thinking to streamline operations and unlock new value. I bridge strategy and engineering to deliver agents, analytics, and experiences that stick. My work blends requirements analysis, pragmatic builds, and clear documentation so teams can adopt and sustain the tools we ship.

I help teams with:

  • AI agents & automation (Voiceflow, Twilio, Agent.AI, ChatGPT) to reduce manual work and speed up decisions
  • Systems & process analysis: stakeholder interviews, functional/technical requirements, and flow mapping
  • Data & analytics: instrumentation, attribution/tracking fixes, dashboards, and SQL-backed reporting
  • Product delivery: scoping, iterative build/test, documentation, and handoff that drives adoption

How I work:

  • Discovery: Clarify goals, constraints, stakeholders, and current workflows so we’re solving the right problem.
  • Design hypothesis: Propose an approach, expected outcomes, and success criteria; outline the data and system touchpoints.
  • Iteration & testing: Build a focused version, test with real cases, and refine based on feedback and results.
  • Documentation & training: Create concise docs and handoff guides; provide brief training so the team can own the solution.
  • Monitoring & analysis: Set up tracking and reporting, review outcomes on a regular cadence, and identify enhancements for the next cycle.

Current focus:

  • LLM agents: natural-language conversation; user-centric experiences that improve satisfaction and outcomes; scaling service offerings (quantity and quality) efficiently and affordably; prompt and context engineering to optimize performance; and reliable structured outputs when needed.
  • Structured agent evaluation, enhancement ideation, and refinement: define success measures, capture interaction data, review transcripts and failure cases, run small experiments, and ship improvements on a steady cadence.
  • Practical integrations that boost efficiency, productivity, and impact: apply AI to intake/triage, knowledge access, workflow automation, and analytics/reporting.

If you need a builder who can translate goals into working agents, data flows, and interfaces—let’s talk.

Contact

Interested in working together? Let's build something amazing!

Email me: [email protected]