Post-launch iteration, rebuilt.
ReUX.ai
An AI system that closes the loop — from scattered signals to executed decisions, verified with data.
MVP launching July 2026
Time
Mar 2025 – Present
Role
Solo Founder · Product · AI Systems
Tools
Figma · Claude API · Cursor · Next.js
What ReUX Does
From Signal to Execution, in One Loop
Most teams don't lack feedback. They lack a system that connects it to action. ReUX closes that gap.
01
Map Your Live Product
Paste a URL or install the Chrome extension — page structure, components, and tech debt are captured in minutes. This becomes the shared memory foundation every AI call in ReUX builds on.
User models built on five psychological frameworks and real web signals — not assumptions. See where users drop off and why, before a single line of code changes.
02
03
Signals to Tasks
User behavior, market shifts, and team friction flow into one unified list. AI synthesizes across all sources and generates prioritized tasks. You approve. ReUX moves.
Research, Design, and Engineering agents share the same product context via a single Background Pack. No context lost in handoffs. Execution stays connected end to end.
04
05
Agent Marketplace
Roadmap
Buy specialist agents or train your own on your own cases. Turn product knowledge and workflows into reusable agents your team can run again and again.
Roadmap
ReUX captures decisions, friction, and follow-ups from Slack in real time — so nothing gets lost between discussion and execution.
06
07
Ship and Verify
Every change is tracked. Before and after states compared. Export to Figma, ship to GitHub, and know with data what actually improved.
Executive Summary
Why I Built ReUX
AI has made 0→1 faster than ever. But post-launch iteration is still manual, fragmented, and context-poor. I built ReUX to close that loop.
The Market Moment
AI agents are finally capable enough to execute, not just suggest. Every product team is doing the same broken loop every day — and no tool closes it end to end. The timing is now.
My Role
Solo Founder. Product strategy, system architecture, UX, and AI workflow — end to end. Engineering background means I don't just design systems, I build them.
What Makes It Different
Grounded in live product context, not prompts or screenshots. Behavior-predictive user models, not persona cards. Signal to execution in one connected loop.
Where We Are
Core architecture complete. Chrome extension, persona engine, signal pipeline, and agent execution layer built and connected. MVP beta launching July 2026.
Why This Problem Matters Now
The Loop Has Always Been Broken. Now There's a Way to Fix It.
I've seen this from the inside. At TigerGraph, a ten-minute window with JPMorgan Chase — and no system to capture it. At Zeta, Starbucks data scattered across a dozen teams. I raised it. My manager said: "I know. Why hasn't it changed?" Not because nobody cared. Because there was no system to act on it. I built ReUX to be that system.
The Industry Is Feeling It Too
This isn't a niche problem. Post-launch iteration is broken for most product teams — and the data shows it.
Teams Report Efficiency Gaps
`AI tools improve efficiency for product teams — but only on isolated tasks. Nobody has connected the full chain from signal to execution.`
Bandwidth Is the Biggest Blocker
Most product professionals say time and bandwidth constraints are their top challenge — even as demand for research keeps growing.
Research Demand Is Rising
More teams need more insights, faster. But the infrastructure to capture, prioritize, and act on signals hasn't kept up.
AI Adoption Is Accelerating
Over half of product teams now use AI in their workflows. But most are using it on disconnected steps — not as a unified system.
Core Product System
How the System Came Together
I didn't start with an architecture. I started with a problem — and kept hitting new ones. Each solution became a layer. The layers became a system.
Real Problems. Real Decisions.
Building It Meant Breaking It First
Every core decision in ReUX came from a real failure. Here's what broke, and how I fixed it.
Screenshot + DOM Dual Input
AI couldn't understand a real product interface.
Raw DOM is noisy and non-semantic — the model couldn't extract layout intent or visual hierarchy from HTML alone. The fix: feed Claude both a screenshot and a simplified DOM together. Screenshot gives visual layout and spatial relationships. DOM gives component structure and text content. Claude outputs a semantic Context Object directly — no manual parsing needed. This became the perception layer of the entire system.
Background Pack
Every AI call kept starting from zero.
Persona generation didn't know the product. Signal synthesis didn't know the users. Agents didn't know the history. Every step was isolated. The fix: a shared context layer — product structure, user models, signal history — injected into every AI call. One memory. Every agent. No context lost between steps.
A Closed Loop, Not a Toolchain
The steps worked. But they didn't connect.
Every handoff was a context drop. The output of one step wasn't the input of the next. Momentum kept breaking. The fix: design it as a loop. Signal → Priority → Execution → Verification → better Signal. Each stage feeds the next. Background Pack keeps it coherent. This is when ReUX stopped being features and became a system.
Design Principles
What I Believe — And How It Shaped Every Decision
These aren't values on a wall. Every principle here has a decision behind it.
Vision → MVP Scope
I Knew What I Wanted to Build. The Hard Part Was Deciding What to Ship First.
ReUX has a full vision — 8 stages, agent marketplace, live comms, auto-iteration. But shipping everything at once is how products fail. I mapped every node, every challenge, every constraint — then made the cuts.
Every branch in this map is a real decision. The ones in the MVP are the ones the loop cannot work without.
MVP Trade-offs
We Didn't Ask "Is This Useful?" We Asked "Can the Loop Run Without It?"
Every cut was a decision. Not based on what sounded impressive, but on whether the loop could run, the value could land, and the team could sustain it.
Loop-Critical
Without it, the end-to-end chain breaks. Context → Persona → Signal → Task → Execution. If a feature doesn't support this path, it doesn't enter MVP.
Value-Revealing
Not every feature is technically required, but without some, users can't feel what makes ReUX different. Live context, realistic reactions, signal-to-task — these had to be in.
Trust-Critical
MVP isn't about shipping more. It's about making users believe the output isn't noise. Grounded context, behavior-based predictions, traceable sources — credibility is non-negotiable. MVP
Learning-Critical
Every feature should help answer a core question: Do users trust this model? Will they test with it? Is the output actionable enough to pay for?
Complexity-Sensitive
If a feature significantly raises cost or maintenance burden without strengthening the above — it gets cut. MVP is the smallest credible, learnable, runnable system.
Team-Sustainable
Every feature in MVP must be maintainable, debuggable, and improvable by a small team. I'm a solo founder building this end to end — complexity that can't be owned doesn't just slow things down, it breaks the loop. And a broken loop ships nothing.
MVP Closed Loop + Architecture
This Is What Actually Got Built
Not a concept. Not a wireframe. A running system. Here's how the eight stages connect, what runs inside each one, and what keeps it coherent end to end.
How I Built It
The Problems Nobody Warns You About
Building an AI system isn't just about prompting. It's about what breaks when you connect real components together — and what you learn when you fix it.
01
LLM Always Plays the Ideal User
Out of the box, LLMs default to helpful, patient, knowledgeable users. That's the opposite of what product testing needs. Real users have knowledge gaps, low patience, and wrong mental models. A simulation that always succeeds teaches you nothing. I tried adjusting the prompt — the model would acknowledge the constraint and then ignore it two turns later. The behavior wasn't sticky.
The fix: Deep behavioral injection at the system prompt level — not instructions, but identity. I defined the persona's knowledge boundaries, frustration triggers, and decision patterns as hard constraints. Then added a schema validation layer to catch drift. Failures trigger a correction turn — not a re-send of the same prompt.
02
03
You Can't Encapsulate What You Haven't Run
Early on, I tried to define Skill encapsulation boundaries upfront — before running real tasks. The result was Skills that were either too granular to maintain or too broad to execute reliably. I was designing abstractions without data. The question I kept getting wrong was: what's worth encapsulating? I was answering it with intuition instead of evidence.
The fix: A four-criteria framework. A task earns encapsulation only if it's recurring, requires expert judgment, has high failure cost, and needs stable output. Anything that doesn't meet all four gets solved with a direct prompt. More importantly: run the real task first, record what breaks, then build the Skill around the failure patterns. Never the other way around.
04
Results, Validation & Next Steps
What the Early Signal Is Telling Me
I didn't launch to prove the product was finished. I launched to learn whether the core hypothesis was right. Here's what I found out.
Do people want this badly enough to wait for it?
5+ teams are on the waitlist for the July beta — primarily startup founders, PMs, and designers. These are not passive signups. They are teams actively waiting because they recognize the problem ReUX is solving in their own workflows.
Is there evidence this actually improves testing and decision-making?
Early feedback consistently pointed to the same three things: more realistic reactions, clearer drop-off points, and outputs that teams could actually act on. The word that kept coming back was not "interesting." It was "useful."
Does the output feel more useful than what teams already have?
In early testing, users responded more strongly to reaction-based persona outputs than to static persona descriptions. The shift wasn't subtle — teams immediately wanted to use ReUX personas in interviews, workshops, and product testing sessions, not just read them.
Do people come back after the first session?
The strongest indicator wasn't the initial reaction — it was what teams asked next. After early sessions, the consistent follow-up wasn't "interesting, we'll think about it." It was "can we run this on our actual product?" and "when can we use this for our next sprint?" Teams weren't evaluating ReUX. They were already planning how to integrate it.
What's Next
The Loop Is Running.
Now We Scale It.
The core system is built. The MVP launches July 2026. The next phase is about real teams, real products, and real iteration — finding out what breaks, what holds, and what the loop looks like when it runs at scale.
If you want to see what ReUX looks like in practice, the product is live.