The engine for human-AI collaboration.

Production infrastructure for operationally complex industries.

Start a Discussion

Building the Critical Systems Behind the Business

ML Systems

Production models that turn your data into real-time decisions.

Agent Infrastructure

Tooling that lets AI agents execute real-world operations.

Custom Platforms

The core software that runs your business—built from scratch, tailored to how you operate.

Ventures & Projects

Architecting AI-enabled infrastructure for complex operations.

Falcon Freight Labs

Founder & Architect AI & MCP Agents

The "Stripe for Freight." An agentic infrastructure layer allowing AI to rate, book, and track shipments via the Model Context Protocol (MCP).


Falcon Freight Labs Logo

Tiger Sitters

Co-Founder Marketplace Platform

A two-sided marketplace for specialized care. Led the product realization from concept to launch, bridging full-stack execution (Django/AWS) with brand strategy.


Tiger Sitters Logo

Nadine West

AWS-Native ML Serverless

Architected a complete ML ecosystem on AWS. Leveraged serverless compute (Lambda) for real-time inference and deployed LLM-driven customer intelligence.


Nadine West Logo

Frequently asked questions

Still have questions?
Let's start a conversation

What is AI and how does it work for business?

Think of today's AI as having a really smart intern who never gets tired, can write in your voice, and can process information faster than any human. The game-changer isn't the technology itself - it's figuring out which of your current manual tasks would benefit from that kind of help.

Look for three things: tasks you do repeatedly, information you have to synthesize from multiple sources, and anything that requires writing in a consistent voice or format. Skip the flashy stuff and focus on work that's eating up hours of your week. These are usually the sweet spots where AI delivers immediate, noticeable results.

Less than you think. Most AI tools work with whatever data you already have - emails, documents, spreadsheets. You don't need perfect datasets or massive infrastructure. You need a browser, a willingness to experiment, and about 30 minutes to try something small.

Start with one good general-purpose tool like Claude or ChatGPT and actually use it daily for two weeks before exploring specialized tools. Most people tool-hop without mastering the basics, which is like buying a race car when you haven't learned to drive stick shift.

The biggest risk isn't AI taking over - it's implementing something you don't understand or can't maintain. Start small, keep humans in the loop, and never automate something you haven't done manually first. Security matters, but over-thinking it often becomes an excuse to never start.

Pick something measurable and boring. Instead of "AI will transform our business," try "this will save Sarah 3 hours per week on report generation." Stakeholders love concrete time savings and cost reductions. Start there, prove it works, then expand.