Once, for Good
Solving the same problem twice never sat right with me. Here’s what AI changed about that.
I build the systems behind how anyone solves problems.
Interesting people are the best part of this. Say hi.
| Rev | Date | Description |
|---|---|---|
| E | 2026.10 | The sky overhead, live. |
| D | 2026.10 | The writing room. |
| C | 2026.10 | Systems you can open. A card that remembers. |
| B | 2026.02 | Repositioned around AI. The writing begins. |
| A | 2025.08 | Rebuilt in Astro. Works on phones. |
Most problems show up at one level but are caused at another. I start by finding which level a problem actually lives at, then build there. Each level links to the work below that sits at it.
Operating models and decision rights: who decides what, and which decisions a system can carry so people don’t have to.
The handoffs between people and AI: where judgment is needed, and where volume can be absorbed without anyone touching it.
Engineering and product processes rebuilt around AI, so more people can build and fewer steps wait on a specialist.
Products that only exist because AI does.
Next — contextOS. Coming soon.
Gupta Advisory Founder I work with organizations on how they should operate with AI. Inside Gupta Advisory
A fintech-healthcare company that underwrites insurance claims and advances cash to healthcare providers, so they aren't waiting weeks to get paid.
Inside CylerityTurns a weeks-long claims cycle into same-day funding decisions. Risk and compliance run in parallel, and providers see what was approved and why.
Backs an eight-figure lending facility
Anyone in the company drops an idea into a shared Claude project. A conversation draws out what's needed and classifies it as an idea, tracked, or a scope, ready to build.
Retired a sprawling spreadsheet
A workers' comp platform that helps injured employees get back to work.
Inside Ready ReboundCo-founded an end-to-end career platform that helps people understand their strengths, upskill, and get matched to opportunities.
Inside Skale.winThe first products I built with AI, from the release of GPT-4 onward.
Built a university advising system that spread across campus through word of mouth. No marketing, no push, just a product that solved a daily problem.
Best pitch at Transcend
Automated claims data extraction for healthcare TPAs while keeping humans in the loop where compliance demands it.
Compliance-first automation
Replaced ad-hoc startup mentorship with a system that structures what each founder has already done, so the right people can help without relearning it.
Solving the same problem twice never sat right with me. Here’s what AI changed about that.
My attempt, after years of failing to find the right words, to finally explain what AI means for everyone — not the technology, but what's already changing in the world around you, right now, while most people are going about their lives.
An observational analysis of three concurrent developments in artificial intelligence — recursive model self-improvement, the commoditization of frontier-class inference, and the compounding automation of cognitive labor — and their implications for institutions designed around assumptions of scarcity.
Work · Jan 2024 – Present
AI Lead
A fintech-healthcare company that underwrites insurance claims and advances cash to healthcare providers, so they aren't waiting weeks to get paid.
Providers can wait weeks for a claim to pay out. Advancing that cash means every funding decision has to be fast, explainable and compliant. A weeks-long claims cycle has to become a same-day decision without cutting corners on risk.
When I joined, Cylerity had a standard roadmap and a traditional way of building. I redesigned the operating model. Today everyone in the company uses Claude Code and produces output independently, and everything flows from business-level features down to technical implementation, step by step.
That operating model took the company from MVP to an eight-figure lending facility.
Work · Dec 2024 – Present
Founder
I work with organizations on how they should operate with AI, starting with how the business actually works, then targeted experiments that prove value fast.
The model is simple. Someone reaches out with a problem. I spend real time understanding their business: not just the symptom, but what the organization actually looks like today and where the friction is.
Then I come back with a few targeted solutions for quick experiments, ways to find value without a large upfront commitment. If we see ROI on day one, we commit to something larger. No lengthy proposals. No months of discovery before anything happens.
The through-line is always the same: figure out where AI changes how the organization works, and design for that shift.
I do my best work where decisions can move quickly. That’s usually smaller teams, sometimes a focused group inside a larger company.
The best first message is a rough picture of what you’re trying to solve and how it’s handled today.
Work · Dec 2025 – Present
AI & Engineering Lead
A workers' comp platform that helps injured employees get back to work.
The goal isn’t to add AI to the workflow. It’s to redesign the workflow around AI, in every department, not just engineering.
Work · Jun 2024 – Feb 2026
Founding member, Product & Strategy
Co-founded an end-to-end career platform that helps people understand their strengths, upskill, and get matched to opportunities.
We set out to reconstruct what hiring is as a whole. The resume has been the standard for centuries, but it’s a flat, low-resolution signal of what a person can actually do. We used higher-dimensional embedding models to capture real capability: not keywords on a page, but what someone is genuinely able to contribute.
The platform covered everything a career needs: understand how to be better, upskill, and find the right opportunities. The business model was white-label and enterprise hiring.
It was the first AI product I took end to end, from infrastructure to deployment, and it was received exceptionally well by the people using it to prepare for interviews.
Cylerity · AI Lead · SYS.02
Engineering requests used to live in a sprawling spreadsheet. Half-formed ideas sat next to work that was ready to build, and scoping an idea depended on someone already knowing what it needed.
Now anyone in the company drops an idea into a shared Claude project. A proactive conversation draws out what’s actually needed, then classifies it: an idea, vague but tracked, or a scope, defined enough for an engineer to build. People focus on the ideas; the system handles the definition, and what ships feeds the next conversation.
Cylerity · AI Lead · SYS.01
Insurance claims took weeks to pay out, forcing healthcare providers into cash crunches that stalled operations and growth. No underwriting process existed to fix it.
I designed the operating model end to end: how claims data enters, gets assessed, and produces a funding decision. Risk assessment and compliance run in parallel, which collapses weeks into hours, and a transparency layer shows providers exactly what was approved and why.
The result is same-day decisions for providers, an underwriting operation that runs end to end, and an eight-figure lending facility.
Ready Rebound · AI & Engineering Lead · SYS.03
An engineer hands the loop a ticket. From there it runs the full engineering process: implementation and review passes, a documentation tree it keeps current and builds from, the pull request, and QA in a real browser.
A person is pulled in only where judgment matters: decisions, specs and architecture. Two people now ship what four did, and for smaller features that don’t need architecture work, non-technical teammates open their own PRs.
Skale.win · Founding member, Product & Strategy · SYS.04
Hiring runs on resumes: a flat, low-resolution signal of what someone can actually do.
The platform started with an AI interview, a quick five minutes or a full thirty. A job link went through Perplexity and came back as structured requirements; without one, an industry or nothing at all was enough. The interview itself ran as a voice loop, speech to text into a model on OpenRouter and back out as speech, in under 500 milliseconds a turn, and every answer reshaped the next question. Each interview was scored on core competencies plus categories generated for that conversation, and the results became specific, actionable feedback and an embedded profile of the person’s skills and preferences.
That model recommended jobs and aimed the next interview at the weak spots. The more someone used it, the sharper their profile got, and the better they got.
Earlier · SYS.06
TPAs spent hours re-typing claim data from noisy scans. The process was slow and expensive, but compliance requirements made automation seem risky.
Automation handles the volume and humans handle judgment. Confidence scoring flags low-certainty items for human review automatically, and the whole system was built around healthcare privacy requirements from day one.
The result is clean, structured data with human oversight where it matters. Automation enhances accuracy instead of introducing risk.
Earlier · SYS.07
Founders and mentors were drowning in scattered information with no way to identify gaps or prioritize. Mentorship was ad-hoc, and the most useful help often arrived too late.
Founders upload their documents and materials. The system builds the rubrics, structure and documentation of where that company actually stands, work that used to fall on people. Launchpad, the University of Minnesota’s tech-transfer office, uses that picture to bring in the right people and the right support, without spending time relearning what’s already been done.
Earlier · SYS.05
Advising information was scattered across dozens of university pages and PDFs. Students couldn’t find straightforward answers. Advisors were overwhelmed with repetitive questions.
The core problem wasn’t a lack of information but a lack of accessible information architecture. Univise surfaces answers in plain language with links to official sources, trust through transparency, and it spread organically because it solved a daily friction point every student experienced.
It was adopted across campus through word of mouth alone and won best pitch at Transcend, proof that the right information architecture reshapes how an institution delivers a service.