How it works
A forward-deployed lead works inside your team: close enough to the work to see where the time and errors go, and technical enough to build what fixes it.
Strategy decks don’t run, and engineers handed a vague brief build the wrong thing. Most AI pilots stall in the space between those two: they worked on a sample, but nobody owned making them work on real data, inside real systems, at a cost that makes sense.
Forward-deployed means one person accountable for both the business result and the build, working where the work happens rather than from a slide deck.
The method
I sit with the people doing the work, map where the time and errors go, and agree what “working” means in numbers.
Data, security, which models, how every AI output is checked before anyone acts on it, and what each run costs.
Working software in your environment, with tests that hold it to the design.
Your team runs it, with documentation and a measured result.
Engagements
Most clients start with a sprint. Some go straight to deployment when the workflow and the data are already clear.
I map one workflow with the people who do it, build a working prototype on your data, and report what it gets right, what it gets wrong and what each run costs.
A production build in your environment: data, security, model choice, checks on every output, monitoring and a cost limit per run.
For AI companies whose customers need hands-on rollout. I run deployments with your customers’ teams, feed what I learn back to product, and set up the process your future hires will follow.
How I build
These come from building RaiseForge, my own production AI platform, where every answer has to hold up in front of an investor.
Every AI output is checked against its source before anyone acts on it. A separate verification pass has to agree. Unsupported answers are dropped, not shown.
Model calls go through one gateway that meters each call and enforces a hard limit per run, so cost per document is known before you scale.
Each customer’s data is isolated with database-level rules (row-level security), not just application code.
Models are chosen per step on accuracy and cost, across providers, rather than one model for everything.
Multi-step and scheduled runs are orchestrated with each step recorded, so any result can be traced back to its inputs.
Tests that deliberately try to break the system run before anything ships, holding the build to the design.
Remote, on site as needed, anywhere in the United States. Part-time by design: I also build RaiseForge, my own AI platform, and that work keeps my methods current.
The sprint is a fixed fee agreed up front. Deployments and embedded work are priced by days per week and duration.
Sometimes the operation needs a leader, not just a system. Then I step in as interim or fractional COO, two to three days a week.
Start with one workflow
You describe the work. I tell you whether AI should touch it, and what a sprint would produce.