Stop citing bad science

⚡ The Signal

A 25-year-old study, foundational to the safety case for a weedkiller used on millions of acres, was just retracted. This wasn't a minor correction; it was a seismic event that confirmed what many have suspected for years: corporate influence can quietly corrupt the scientific record we all rely on. The recent retraction of the Roundup study is a brutal reminder that even peer-reviewed science isn't immune to bias.

🚧 The Problem

Trust is expensive to lose and nearly impossible to win back. Professionals in every field—from VCs vetting a biotech startup to policymakers setting public safety standards—are drowning in data of questionable origin. The cost of getting it wrong can be catastrophic, leading to situations where families are mistakenly sent back to live in toxic homes based on flawed assessments.

This isn't just about corporate meddling anymore. We now face a second front: AI-generated misinformation. We're already seeing how AI in medicine can hallucinate and fabricate information, creating a new layer of noise. The core problem is that the tools for verifying information haven't kept pace with the tools for creating and distorting it.

🚀 The Solution

It's time for a tool that helps us critically ignore bad data. Enter Veriphy.

Veriphy is an AI-powered platform that instantly assesses the integrity of any scientific study. Paste in a URL or a PDF, and Veriphy analyzes funding sources, author histories, conflicts of interest, and statistical methods to generate a simple bias and validity score. It's designed to help you stop wasting time on compromised research and focus on work that actually matters.

💰 The Business Case

Revenue Model

Veriphy operates on a freemium SaaS model.

• Free Tier: 3 free analyses per month. Enough to prove the value and create a powerful user acquisition loop.
• Pro Tier ($29/month): For the individual researcher, journalist, or analyst. Unlimited analyses via web app and Chrome extension, a personal dashboard, and alerts on key studies.
• Team Tier ($199/month): For venture firms, corporate R&D, and policy groups. All Pro features plus API access for programmatic analysis, team management, and integrations with tools like Zotero.

Go-To-Market

We’re not waiting for customers to find us.

1. Free "Study Grader" Tool: A highly shareable, top-of-funnel website where anyone can paste a link and get a high-level integrity report.
2. Programmatic SEO: Every publicly analyzed paper becomes a unique, indexable page. This will capture long-tail search traffic from people looking for information on specific studies or authors.
3. Open Source: We’ll release a simple Python library that does one thing well—like extracting funding statements from PDFs. This builds credibility with the technical community and serves as a lead-in to our full-featured product.

⚔️ The Moat

Competitors like Scite.ai or Semantic Scholar are great for discovery and citation tracking, but they don't focus on scoring bias and integrity. Veriphy’s unfair advantage is data accumulation.

Every study analyzed enriches our private, proprietary database of author histories, funding sources, institutional affiliations, and common patterns of statistical manipulation. Over time, this creates a powerful data moat, making our "Integrity Score" more accurate and contextually aware than any competitor's. The more people use the tool, the smarter it gets for everyone.

⏳ Why Now

The market is at a unique inflection point. First, there's a clear crisis of faith in institutional science, catalyzed by high-profile events like the recent glyphosate study retraction. Second, the rapid proliferation of AI tools is polluting the information ecosystem at an unprecedented rate. Even consumer-grade AIs, like AI fitness coaches, are prone to generating plausible but incorrect information.

This combination of eroding trust and increasing noise creates an urgent, unmet need for a reliable verification layer.

🛠️ Builder's Corner

This is a data-heavy analytics problem, so the MVP stack should be robust and efficient. This is just one way to build it:

• Backend: A Python backend using FastAPI for its speed and simplicity in creating API endpoints.
• Data Scraping/Parsing: Use BeautifulSoup for parsing HTML from study URLs and pdfplumber for extracting text, tables, and metadata from PDF files. These are the workhorses for getting the raw data.
• Core Logic & DB: The "Integrity Score" logic can be built with Python scripts leveraging Pandas for data manipulation. All scraped and analyzed data (author histories, funding links) would be stored in a PostgreSQL database, which handles complex queries well.
• Frontend: A clean Next.js app hosted on Vercel for the main web application. For the browser extension, keep it lightweight with Vanilla JS to ensure it's fast and doesn't bog down the user's browsing experience.

Legal Disclaimer: GammaVibe is provided for inspiration only. The ideas and names suggested have not been vetted for viability, legality, or intellectual property infringement (including patents and trademarks). This is not financial or legal advice. Always perform your own due diligence and clearance searches before executing on any concept.