JOHN KANE
AI Solutions Architect. Founder & CTO. Atlanta.
I design, build, and prove production AI systems end to end — data, models, agents, evaluation, and guardrails. I built LazyEdge, a live prediction-markets platform with paying customers, on my own. Now I'm doing it for consumer products with Bellwether.
WHY I WORK
"I prove a system works before anyone has to rely on it."
I've worked in industries where the old models stopped working. I helped local newspapers reinvent themselves. I supported small businesses as they moved online. I ran event campaigns during COVID, helping teams shift fast when everything paused.
The goal was never just to survive. It was to grow in smarter ways — to build platforms, processes, and programs that make work easier and results more reliable.
That's the same instinct I bring to AI. I build production systems end to end: the data, the models, the agents, and the evaluation layer that catches drift before it reaches a user. Anyone can ship a demo. What matters is whether the thing holds up when real money and real decisions are on it — so every model I ship goes through walk-forward backtesting, calibration checks, and ablation testing, and runs behind kill switches and freshness guards.
What powers me?
The drive to keep pushing
The need to scale what works
The belief that a system isn't finished until it's been proven
RESULTS BY THE NUMBERS
These are the engines behind the outcomes.
Now see the systems that are running right now.
> BUSINESS ENGINES I BUILD
Click any tile to explore how I design, build, and prove production AI systems.
Architect Production AI Systems
Map every system from the first data source to the final decision it supports. Train and backtest models on real historical data, not synthetic benchmarks. Route work across Claude, OpenAI, and Gemini based on what each step actually needs. Ship the whole stack, not a proof of concept.
Prove Models Before Anyone Relies On Them
Run walk-forward backtests against held-out control seasons. Score calibration and ablate each component to see what is actually carrying the result. Put kill switches, coherence checks, and leakage guards in front of users. Monitor for drift and convergence continuously.
Ship Agent-Facing APIs and MCP Servers
Expose platforms to outside agents as callable tools over MCP. Design APIs that another company's bots can trade and act on directly. Build agent memory on vector storage so context survives between sessions. Layer in payments, including per-call micropayments.
Fuse Real-Time Data Into Live Decisions
Build event-driven pipelines that react as new information lands. Fuse live market prices with game, news, and social streams in one view. Model how markets respond to news using profiles built from real audience behavior. Keep latency low enough that the output is still worth acting on.
Install Strategic Marketing Infrastructure
Designed CRM logic and funnel tagging using Salesforce. Built campaign dashboards across digital and event ecosystems. Enabled real-time visibility across paid media, events, and partnerships. Improved executive reporting and reduced CAC across GSMA and partners.
Create Product Growth Engines
Founded and scaled products from zero in markets nobody was serving yet. Identified upgrade paths and monetization layers in untapped segments. Aligned creative, sales, and data teams around new customer segments. Used direct-response insights to optimize pricing and onboarding.
> SYSTEM VISOR: LIVE PREVIEWS
> TERMINAL TASK CONSOLE
Click any task to expand inline and see the full solution with proof points.
I build agents that do real work — tool calling, persistent memory, and multi-model routing so each step runs on the model that suits it. They plug into the systems you already have rather than replacing them.
Built MCP servers that expose LazyEdge to outside agents as callable tools; customer bots have placed 150+ trades in a single NBA playoff game
I build the evaluation layer most AI projects skip: walk-forward backtesting, calibration scoring, and ablation testing, with kill switches and drift monitors in front of users. This is what separates a demo from something you can rely on.
The evaluation and reliability layer on LazyEdge is what lets customers put real capital behind the data
I build event-driven pipelines that react as new information lands, fusing multiple streams into one view fast enough that the output is still worth acting on.
LazyEdge fuses live market prices with game and news streams on AWS, repricing every NBA, NFL and MLB game on every play
I assess where AI actually removes friction, scope the solution, choose the models and platforms, build the agents and workflows, and train the team that runs them after I leave.
Delivered an AI transformation for a European OTT streaming device company, reorganizing its sales process with AI-driven workflows tied to bottom-line outcomes