Your AI Research Assistant for Publication-Ready Papers
StatsPAI empowers researchers with AI-assisted tools for paper outline planning, literature review synthesis from uploaded PDFs, and rigorous statistical modeling — dramatically accelerating your path from data to publication.
StatsPAI, together with LearnPy.online and CoPaper.AI, is committed to rapidly making Python the #1 language for empirical research
Traditional empirical research requires literature review, data cleaning, statistical modeling, and full paper writing — a tedious and time-consuming process. StatsPAI's AI assistant works alongside you — helping structure your paper outline, generate 100% reliable literature reviews, build econometric models, refine your analysis, generate fully formatted papers, and provide Python/R/Stata code for full reproducibility. Let AI handle the tedious groundwork so you can focus on research and innovation, gaining insights into the past, present, and future.
Bryce Wang @Stanford REAP
Founder
Researcher and engineer focused on Agentic AI and statistical empirical analysis. Author of "Building Intelligent Multi-Agent Systems".
Expertise
Scott Rozelle, PhD @Stanford REAP
Cofounder & Strategic Advisor
Helen F. Farnsworth Senior Fellow at Stanford University and Faculty Co-director of the Stanford Center on China's Economy and Institutions. Leading expert on agricultural policy, rural development, and education economics in China. Author of "Invisible China" and recipient of China's Friendship Award.
Expertise
Our Mission
We believe empirical research should be accessible to everyone. By combining cutting-edge AI technology with deep statistical expertise, we're removing the technical barriers that prevent researchers from focusing on what matters most: asking the right questions and interpreting results.
Democratization
Making professional research tools accessible to researchers worldwide
Quality
Maintaining the highest standards of statistical rigor and methodology
Innovation
Pushing the boundaries of what AI can achieve in empirical analysis
Our Product Ecosystem
Two Platforms, One Mission
From learning to professional research - we support your entire journey in data science and empirical analysis
AI Research Assistant — Plan · Estimate · Iterate · Paper
Your AI-powered research assistant for academic paper writing. From outline planning to empirical data analysis and full paper generation — produce publication-ready work efficiently.
LearnPy.online
Free Interactive Learning
Interactive platform for learning Python, statistics, and econometrics. Code in your browser, get AI assistance, and build skills from basics to advanced topics.
Both platforms designed to democratize access to advanced statistical analysis
Core Features
Powered by Advanced AI
Everything you need for professional empirical research
Automated Research Workflow
From data cleaning to final report generation, our AI handles the entire research process automatically.
Publication-Ready Output
Generate comprehensive DOCX reports with methodology, results, robustness checks, and discussion chapters.
Intelligent Analysis
Leveraging Claude 4.5 and GPT-5 for sophisticated statistical modeling and evidence-based insights.
Minutes, Not Months
Complete what traditionally takes weeks of work in just minutes with AI-powered acceleration.
Comprehensive Statistics
Descriptive statistics, baseline models, robustness checks, and advanced econometric analysis.
Democratized Research
No technical barriers. Focus on your research questions, not implementation details.
Free Interactive Learning Platform
LearnPy.Online
Master Python, Statistics, and Econometrics with interactive coding and AI assistance - completely free
Interactive Code Editor
Write and run Python code directly in your browser with instant feedback
AI-Powered Assistant
Get explanations, debug code, and learn concepts with intelligent chatbot support
Comprehensive Curriculum
From Python basics to advanced econometrics - structured learning paths
# Python Statistics Example
import numpy as np
import pandas as pd
from scipy import stats
# Generate sample data
data = np.random.normal(100, 15, 1000)
# Perform t-test
t_stat, p_value = stats.ttest_1samp(data, 100)
# Display results
print(f"T-statistic: {t_stat:.4f}")
print(f"P-value: {p_value:.4f}")100% Free - No Credit Card Required
From Data to Paper, Together
From data to a complete empirical paper in 20 minutes
CoPaper.AI is an AI-powered research co-authoring platform from the Stanford REAP team. Upload your data, set your research direction, and collaborate deeply with AI at every step to produce a reproducible, publication-ready academic paper with complete code.
3,000+
Papers Completed
200+
Universities
5–30 min
Avg. Generation Time
You're the Author, AI Is Your Research Partner
Unlike tools that generate an entire paper with one click, CoPaper.AI pauses at every critical step — outline, variable selection, model specification, result interpretation — and waits for your input. Every decision reflects your academic judgment. The AI handles the heavy lifting; you steer the research.
How It Works
Upload Data
Drag and drop your datasets (CSV, Excel, Stata, and more). The system automatically detects variable types and data structure.
Set Research Direction
Define your research question, choose methods, and select variables. Use AI-assisted inference or set everything manually.
AI Writes, You Guide
The AI generates each chapter step by step, pausing after every section for your review and feedback before continuing.
Refine & Export
Polish your paper with AI-powered refinement. Export a complete, publication-ready DOCX with one click.
100% Reproducible
Every regression, chart, and statistical result comes with complete Python code — plus Stata, R, and EViews translations for full reproducibility.
Human-in-the-Loop at Every Step
AI doesn't decide for you. At each stage — outline, data analysis, results interpretation — you review, revise, and approve before moving forward.
Publication-Ready Output
Generates properly structured papers with introduction, literature review, data & methods, empirical results, and discussion — ready for journal submission.
From Basics to Advanced Agent Systems
Deep Dive into Agentic AI
Building Intelligent Multi-Agent Systems
A comprehensive guide to understanding and implementing intelligent multi-agent systems. Goes beyond drag-and-drop workflows to teach dynamic, intelligent decision-making in AI systems.
“AI era, understanding is king. Abandon non-essential details, directly reach the core of Agentic AI thinking.”
Get in Touch
Get in Touch
Ready to transform your research workflow?
Join researchers worldwide who are accelerating their empirical analysis with StatsPAI.