AI-native operator model
AI-native engineering changed my output curve.
I operate at the intersection of executive strategy and hands-on build velocity. The value is not just that I can ship faster with AI. It is that I know what should be built, what should not be built, and how to turn ambiguity into working systems.
The current proof: managing 28-30 active codebases, 6K commits, and 1.8M lines committed across a portfolio where the month-over-month velocity keeps rising.
Retainer
Senior product judgment, architecture, automation, prototypes, and working code without a traditional hiring cycle.
Portfolio window
Working output
Active codebases managed
Lines committed into working products
Commits shipped
Total source movement
Velocity dashboard
Source-code output curve
An anonymized line graph of engineering velocity: lines committed, net movement, commits, and the month-over-month pace of change.
Selected period
May 2026
Month-over-month
+24%
Change in committed-line velocity versus the prior period.
Evidence without noise
The receipts are anonymized. The operating pattern is not.
The data spans real codebases and real implementation work, but this page intentionally groups the work by domain instead of exposing project names. The point is the pattern: executive-level prioritization paired with builder-level throughput.
The speed is not chaos. Everything is pushed through platform patterns for security, integrity, repeatable engineering practice, and serverless scalable foundations where the architecture calls for them.
Healthcare AI platform
32%Clinical workflow, note generation, and operational product systems.
Mobile and desktop surfaces
18%Cross-platform product experiences that meet clinicians where they work.
EHR integration and automation
17%Scheduling, context resolution, writeback, and workflow automation.
Agentic workflows
14%Internal tools and agent loops that compress research, coding, and QA.
Clinical data infrastructure
11%Data movement, validation, test harnesses, and environment plumbing.
Go-to-market systems
8%Brand, sales, documentation, and customer-facing proof assets.
Executive judgment
Start with the business constraint, workflow reality, buyer pressure, and product consequence before touching code.
Hands-on build loops
Turn ambiguous ideas into prototypes, automation, integrations, and production-ready direction instead of slideware.
AI-native leverage
Use agentic workflows to compress research, implementation, review, and iteration into a tighter operating cycle.
Platform discipline
Move fast through reusable patterns for security, integrity, good engineering hygiene, and serverless scalable foundations where they fit.
Fractional AI-native product engineering
AI-native product engineering retainer
$3,000/month for 8-12 hours. This is not staff augmentation. It is a compact operating loop for teams that need product clarity, technical direction, and shipped artifacts before committing to a larger hiring plan.
Current proof point
lines committed into working systems across the portfolio. The important signal is not volume alone; it is sustained executive-to-code throughput on scalable foundations.
Work with me