Questions clients ask

A forward-deployed lead works inside your team rather than from a slide deck: close enough to the work to see where the time and errors go, and technical enough to design and build the system that fixes it. One person is accountable for both the business result and the build.

A strategy consultant leaves a recommendation; a development shop builds to a brief. Most AI pilots stall between the two. I own the problem with your team, write the design, build it and run it in the work until the numbers we agreed on are met.

Work that runs on documents and data: invoices and purchase orders, contracts, regulatory and SEC filings, reconciliations, claims and management reporting. The best first candidates are high-volume, rules-heavy and currently checked by hand.

Finance and operations teams first, in any industry. Healthcare, health technology and life sciences are home ground: eleven years at Optum and UnitedHealth Group, and RaiseForge's first market is life-science companies. Earlier engineering work covered financial services, information services, IT services and retail.

Yes. As an embedded deployment lead, I run deployments with an AI company's customers, feed what I learn back to product, and set up the process future hires will follow, before the company is ready to hire a full-time deployment team.

With a 30-minute call about one workflow. If AI should touch it, the next step is usually a workflow sprint: two to three weeks, fixed fee, ending with a working prototype on your data and a go or no-go you can defend.

The workflow sprint is a fixed fee agreed up front. Deployments and embedded work are priced by days per week and duration. Typical deployments run eight to twelve weeks at two to three days a week.

Part-time is by design. Most deployments need focused days, not a full-time seat, and I also build RaiseForge, my own AI platform, which keeps my methods current with what production AI actually takes.

Yes, remotely anywhere in the United States, with time on site when the work needs it, especially at the start of a workflow sprint.

A system running in your environment, tests that hold it to the design, documentation your team can use, a cost limit on every run, and a measured result against the numbers agreed at the start.

Systems are built in your environment. Where more than one customer or business unit shares a system, data is separated in the database itself with row-level security, not only in application code. Access, models and data flows are agreed in the design step before anything is built.

Every AI output is checked against its source before anyone acts on it, and a separate verification pass has to agree. Answers that can't be supported are dropped rather than shown. Regression tests that deliberately try to break the system run before anything ships.

Models are chosen per step on accuracy and cost, across providers, and called through one gateway that meters every call. That keeps you from being locked to one vendor and makes cost per document a known number.

Every run has a hard cost cap enforced in the gateway, and the sprint reports cost per run on your real data before you commit to a deployment.

When the problem is the operation rather than one workflow: a leader has left, the company has outgrown the way it runs, or a deal or reorganization has to be carried through a transition. I step in two to three days a week, and AI comes later if it belongs.

At Optum I built a managed analytics business, led a nine-figure deal in which a large health system outsourced its entire analytics organization to Optum, led the transition, and ran the business's operations and finance as COO/CFO, a $200M+ P&L. Since 2023 I have served as interim CEO of a digital health company and advised early-stage healthcare companies.