You don't need another AI tool.
You need someone to tell you if your data is actually ready for one.
I've watched too many mid-market companies drop fifty grand integrating an LLM, only to realize their critical data is locked in unstructured PDFs from 2014. I consult on the messy reality of AI adoption—the stakeholder politics, the legacy systems, and the workforce resistance. Let's figure out what you can actually pull off before you write a massive check to an agency.
Serving companies with 50–200 employees · Governance, Safety & Workforce Enablement
The dirty secret of the AI consulting boom is that most agencies are financially incentivized to sell you a complex implementation, whether you actually need it or not. I'm not.
My entire business model revolves around giving you the ugly truth about your infrastructure. If your data is currently living in forty different unsynced Excel spreadsheets managed by a guy named Gary, no generative AI model in the world is going to save you. We have to fix the foundation first, and I will gladly be the one to tell your board that.
Where I actually spend my time
The API integration takes a weekend. Fixing the human friction takes a month.
AI Readiness Audit
Before anyone touches a line of code, I sit down with your department heads to figure out what they actually do all day. We hunt down the shadow IT, the undocumented processes, and the messy data silos. I'll hand you a brutal, honest report of what will break if you try to automate your current operations.
AI Governance & Safety
You absolutely should be terrified of your employees pasting sensitive client financials into public ChatGPT windows. I build the internal guardrails—both the technical routing layers and the actual corporate policies—so you can adopt LLMs without ending up in a data breach headline.
Workflow Automation Discovery
We isolate the single most frustrating, high-volume bottleneck in your company. Maybe it's invoice ingestion, maybe it's tier-one support routing. We map it out completely, and I tell you exactly how much it would cost to automate, and more importantly, how much it will cost to maintain when the API eventually changes.
Workforce Enablement
Throwing Copilot licenses at your team and hoping for the best is a proven failure strategy. I run workshops that force your staff to break their old habits. We work directly on their actual daily tasks until they trust the new system enough to stop doing things the old, hard way.
A phased approach to keep you from burning cash.
The Initial Conversation
We jump on a 30-minute call. You tell me what you think your business needs. I ask extremely specific questions about how your data is formatted. Usually, we realize the problem you want to solve isn't the problem we need to solve first.
The Reality Check
I spend time inside your systems and talking to the people who do the actual work. I compile an assessment that highlights the massive gap between your executive AI vision and your current operational reality.
The Build (or the Pivot)
If you're ready, we map the architecture and build the governance protocols. If you aren't, I give you a very specific homework list of operational cleanup to do before you spend another dime on AI implementation.
The Hand-off
I don't want to be on your retainer forever. The goal is to train your internal team, document the hell out of the new workflows, and leave you fully capable of running the systems we put in place.
I build the things I talk about.
I don't just write strategy decks. Here's the code I've pushed to production.
ChainMind ↗
Autonomous AI agents with on-chain accountability on Base Sepolia. Built with LangGraph and Coinbase AgentKit to tackle runaway token spend and ensure secure, verifiable execution trails for AI operations.
Conversational Chat Architecture
A real-time WebSocket streaming app. I built this to figure out exactly how to handle context limits and latency spikes in production, so I know firsthand what your engineers are going to face.
Natural Language Database Queries
I wired up an LLM to query a relational Postgres database using the Model Context Protocol. It's the exact pattern I use to show clients how to safely expose private data to AI without letting it hallucinate bad SQL commands.
Streaming UI in Next.js
Server-sent events and live generative UI updates. Getting LLMs to stream responses cleanly into a frontend without breaking the user experience is notoriously annoying. I've documented every workaround I had to use here.
Still reading?
If you're trying to figure out if your company is actually prepared for the AI tools you keep reading about, let's talk. There's no high-pressure sales pitch—just a 30-minute technical reality check. If I think you're chasing the wrong trend, I will absolutely tell you.
Book an honest conversation30 minutes · No obligation · Honest assessment