I build enterprise AI products that move from concept to adoption, from shipping the first native Agentforce app on Salesforce to designing data platforms that serve 450M+ records.
Current Focus
Enterprise AI products, agent workflows, and GTM-ready intelligence systems
Best Fit
Advisory, product leadership, platform strategy, and AI transformation work
12+ years across QA, engineering, architecture, and product
First native Agentforce app on Salesforce
Platforms spanning 450M+ records and enterprise AI workflows
ARR growth in 12 months
faster account intelligence
AI cost reduction
Career Arc
I've worked across QA, engineering, solution architecture, and product. That path shaped how I approach AI work: strategy has to survive implementation, and implementation has to create measurable business value.
Today I lead AI product strategy at Sprouts.ai, building systems that make enterprise data usable through GenAI, automation, and agent-ready workflows.
Selected Work
A mix of enterprise AI products, data systems, and workflow experiences designed to ship, scale, and create measurable business impact.
Open Source
Stop recreating, start creating.
Platform
MCP access to 350M+ contacts and 35M+ accounts on Claude & OpenAI
0→1 Launch
AI powered Agentforce app on Salesforce enabling access to 350M+ contacts and 35M+ accounts
Enterprise
Arguably, the most accurate, comprehensive, and affordable signal engine ever built.
Open Source
Stop fixing AI-generated workflows. Start shipping them.
Latest Thinking
A new category of voice dictation is emerging: local-first, GPU-accelerated, and actually learns from your edits. typwrtr runs entirely on your machine, improves after every correction, and never sends your audio or text to the cloud.
The Web Model Context Protocol (WebMCP) is a browser standard that enables websites to expose structured 'tool contracts' to AI agents. Instead of forcing agents to visually scrape screens to identify buttons and fields, WebMCP provides a logical API layer. This allows agents to interact with websites through defined functions and JSON schemas, resulting in faster, cheaper, and more reliable autonomous actions compared to traditional fragile screen-scraping methods.
Until 2025, the bottleneck wasn't model intelligence—it was isolation. GitHub Copilot saw your code but not your logs. ChatGPT wrote SQL but couldn't see your schema. Developers became glorified data routers, manually bridging the gap. This "interoperability gap" crippled even the smartest models. They lacked the sensory input to understand the system's state. The **Model Context Protocol (MCP)** and **Agent Skills** standard have solved this, creating a universal language for tools to expose their capabilities to AI without bespoke integration.
Start a Conversation
I usually work with teams on product strategy, enterprise AI roadmap decisions, platform direction, and high-leverage growth systems.
What to expect
Share a bit of context and I'll usually reply within 2 business days. If it's a strong fit, we can move straight to a short working session.