I build AI products
that actually ship.

GradeForAI

AI agents are beginning to transact on people's behalf. Booking the appointment, placing the order, choosing the vendor. An agent can only act on a business whose site it can navigate, read accurately, and complete a transaction on, and most businesses fail at least one of those. I coined the category that measures the gap, AI Agent Optimization, and built the engine that scores it.

Roughly 18,000 lines of Python. A six dimension scoring engine covering agent compatibility, transaction readiness, agentic commerce readiness, operational data structure, data accuracy, and competitive position, calibrated per industry so a law firm and a plumbing company are each judged against the right standard. Around the engine: a harvesting pipeline, a parallel scorer, a SQLite store holding historical trend data, generated client PDF reports, an admin dashboard, and scheduled re-scoring running as systemd services on a VPS.

Proffio

An AI operating system for real estate agents and brokerages. I spent years inside real estate transactions and watched the same thing every time: agents lose deals to follow up they never got around to, and brokers have no view into it until the pipeline is already dry.

Proffio runs the follow up for them. Over twenty AI services handle the work a good assistant would: assessing lead quality, scoring client health, flagging deal risk, detecting clients worth re-engaging, reading sentiment across conversations, drafting the next message, and executing voice commands so an agent can work between showings. Built across 96 screens with three permission tiers for agents, teams, and brokers, including compliance and performance reporting, plus a mobile build.

Ticker Buddy

Traders keep a dozen tabs open because nothing puts market data, macro signals, and social sentiment in one place. Ticker Buddy was my answer, built to carry the depth of Bloomberg with the clarity of Robinhood.

I owned it from thesis to working code. A React and TypeScript application on Supabase, plus a browser extension for the signature always on ticker overlay, so the product lives inside a trader's workflow instead of asking them to visit it.

The intelligence layer was the differentiator. Instead of pushing an identical feed to everyone, the system was designed to learn from a user's watchlists and in-app behavior, build a model of what they actually hold and care about, then tune alert thresholds and rank relevance against it. Someone holding a specific position hears about what moves that position rather than general market noise, with NLP sentiment and volatility signals feeding the same model. I specified it in full and then held the build, because doing it properly meant licensing market data at a cost that needed capital behind it. Half-shipping the differentiator was not worth it.

Underneath it: a five layer architecture running from data ingestion through AI processing to the user ecosystem, specced against Polygon.io, FMP, and CME. A full Figma design system across four dashboard iterations, pricing, positioning against Bloomberg, TradingView, and Quartr, a 13 section whitepaper, and a secured trademark.

CloudAurum

Most businesses leak revenue in places they cannot see. Leads that never get answered. Work done by hand that should not be. Data scattered across five tools nobody reconciles. CloudAurum finds the leak, then builds only what closes it.

Every engagement opens with a paid diagnostic. I map how the operation actually runs, quantify what each gap costs in revenue, and prescribe the build. Those builds range from AI voice and SMS agents that capture missed demand, to reactivation campaigns that recover dormant customers, to workflow automation and systems consolidation. I run the discovery, scope and price the work, architect the integration, and deliver it.

OptionPulse

A predictive options analytics platform that surfaced unusual activity and modeled signals for retail options traders. I shipped a working React and TypeScript application on Supabase, with the AI signal layer, auth, and data pipeline in place, then put it in front of real traders and secured early interest.

Then I killed it. Market data licensing cost more than the model could carry at independent scale, and publishing predictive financial signals carries regulatory exposure I was not prepared to underwrite. The decision took longer than the build. Choosing not to ship a product you have already built is the judgment I would defend in any interview.