Production AI in your environment. 30 days.

The last mile is the hard part

Models are commodities. The hard part is everything around them: your data, your processes, your rules, your people.

I sit in that gap. I capture the organizational context that makes an AI system actually useful, and I wire it into something that runs.

Discovery, harness, pilot

Week 1. Discovery.

I meet your sponsor, your business teams, your engineers. I map decision-makers, data sources, and blind spots. By Friday I know what to build.

Weeks 2-3. The harness.

The harness is everything around the LLM: governance, connectors, knowledge base, sandbox. I deploy on AWS: compute, serverless, security, cost management.

Weeks 3-4. The pilot.

Real users, real work. We measure what changes: time saved, errors reduced, decisions accelerated. I iterate until it holds up on its own.

What you walk away with

• A working AI system in production
• A contextual knowledge map of your organization
• Full documentation of the harness
• Before/after metrics on the pilot
• A plan for scaling beyond the first deployment

Everything I build, you keep. You can change models, switch providers, or evolve the architecture.

1 to 3 months

First measurable result within 30 days. I work on-site, hybrid, or remote.

The engagement requires a named sponsor at C-level or director level who can unblock access to people and data.

What I do not do

I am not married to a vendor. I pick the best model for your situation.

I do not produce slide decks or strategy documents.

I do not build a dependency on myself. The goal is that your team operates independently after I leave.