AI-native software products built from revenue signals.
We use AI reasoning, rapid MVPs, and disciplined validation to find narrow products that can compound without scaling headcount.
1 product live on Apify
Our approach
Built to compound
Yaupon Labs uses a tokens-to-revenue operating system to discover, build, and validate narrow software products. Every opportunity is filtered through the same test: real pain, reachable buyer, simple MVP, fast revenue signal, and economics that can work early.
Revenue before roadmap
Every product starts with a commercial test: a buyer, a painful job, and a fast path to a revenue signal.
AI leverage compounds
We use reasoning models, coding agents, and automated review loops to compress the time from idea to testable product.
Small products, low drag
We prefer narrow tools that can operate with minimal support, clear margins, and compounding distribution.
Current focus
AI-native micro-products
Our first products focus on agent workflows and developer utilities. The broader system is designed to identify small, painful software problems, build MVPs quickly, and push each one toward a real revenue signal.
Agent Output Cleaner
LiveDeterministic JSON repair for LLM output.
Strip code fences, fix unquoted keys, repair trailing commas, coerce types to schema — all without an LLM in the repair loop. If it can't fix it cleanly, it tells you why.
Code fence removal / Wrapper-text extraction / Unquoted key repair / Single-quoted string repair / Trailing comma removal / Lightweight schema coercion
Model leverage. Durable products. Compounding cash flow.
Austin, TX.