Workflow sprint
2–3 weeks · fixed feeOne workflow, mapped and prototyped on your own data, with cost and accuracy numbers you can make a decision on.
Forward-deployed AI for finance and operations
Own the problem. Build the system. Run it in the work.
Forward-deployed AI from someone who has done all three: ten years as a software engineer and technical architect, eleven years at Optum running analytics businesses, and the builder of a production AI platform.
01
The business
Own the problem
I sit with the people doing the work, find the workflow worth automating, and agree what “working” means in numbers before anything is built.
02
The technology
Build the system
Data, security, models and a check on every AI output, designed and built in your environment, with tests that hold it to the design.
03
The operation
Run it in the work
Deployed into the daily work, measured against those numbers, and handed to your team with a cost limit on every run and documentation they can use.
The gap
Most AI work stalls between the demo and the business. The pilot worked on a sample. Nobody owns making it run on real data, inside real systems, at a cost that makes sense, with outputs people can trust.
Strategists can’t build it. Engineers handed a vague brief build the wrong thing. A forward-deployed lead closes that gap by doing both, inside your team.
Where it fits
Work that runs on documents and data: invoices and purchase orders, contracts, filings, reconciliations, claims and reporting. Finance and operations teams first. Healthcare and life sciences are home ground.
- Invoice and purchase-order matching
- Contract review
- Regulatory and SEC filings
- Reconciliations
- Claims
- Management reporting
Engagements
Three ways to work together
Each one starts from a single workflow and a number that says whether it worked.
Deployment
8–12 weeks · 2–3 days a weekThe system built for production, tested, and handed to your team with documentation and a measured result.
Embedded deployment lead
Monthly · part-timeFor AI companies that need someone running customer deployments before they hire a full-time team.
How I build
AI you can put in front of a finance team
The same rules I hold my own platform to.
- 1
Every output is checked
Each AI finding is checked against its source before anyone acts on it. Unsupported answers are dropped, not shown.
- 2
Every run has a cost cap
Model calls are metered and each run stops at a hard limit, so cost per document is a known number.
- 3
Data stays separated
Each customer’s data is kept apart in the database itself, not just in application code.
- 4
Tests hold it to the design
Regression tests run before anything ships, so the system keeps doing what was agreed.
Track record
What I have built
RaiseForge, a production AI platform I designed and built myself. It reads a company’s documents and public records, checks every finding against its sources before anyone sees it, runs under a hard cost cap, and pulls live data from eight federal sources.
Before that: ten years as a software engineer and technical architect, then eleven years at Optum (UnitedHealth Group) running analytics businesses, including a $200M+ P&L and a nine-figure analytics outsourcing deal.
10 yearssoftware engineer and technical architect
11 yearsat Optum running analytics businesses
$200M+P&L run as COO/CFO
8federal data sources wired into RaiseForge
When the operation needs a leader first
Sometimes the problem isn’t a workflow, it’s the operation. I step in as interim or fractional COO, two to three days a week.
Start with one workflow
A 30-minute call
You describe the work. I tell you whether AI should touch it, and what a sprint would produce.