AI-Powered Research Platform

From Paper to Results

AI that turns finance papers into runnable code for your data.
Every decision tracked. Every step documented.
Built for teams, not laptops.

Upload Paper
AI Reads & Adapts
Generate Notebook
Results & History

The Gap Between Paper and Implementation

PDFs Are a Mess

Equations become gibberish. Tables lose structure. You spend hours retyping what the AI couldn't parse.

Context Is Scattered

Datasets on laptops. Notes in emails. Papers in downloads. Your AI assistant sees none of it.

Knowledge Walks Out

A researcher leaves. Months of methodology decisions exist only in their head. Onboarding takes forever.

Methodi solves all three.

Built for Rigorous Research

The infrastructure quantitative finance teams actually need.

Precision Paper Parsing

Scientific document parsing powered by LlamaParse. Equations stay as LaTeX. Tables keep their structure. Methodology sections are indexed for semantic search. Ask "how do they define MAX?" and get the exact paragraph.

Unified Research Context

Datasets, papers, notes, and notebooks in one place. AI sees your actual data schemas—column names, types, sample values. Your team works from the same source of truth, not scattered laptops and local scripts.

Git for Research

Every methodology decision tracked with commit-like history. What was tried, what worked, what didn't. Explore alternatives on branches. Trace any result back to its source.

Self-Documenting Workflows

AI summarizes each step: inputs consumed, outputs produced, decisions made, results achieved. Stop writing documentation—it writes itself as you work. Onboard new researchers in hours.

How It Works

From PDF to Documented Results

Four steps. Minutes, not weeks.

01

Upload & Index

Add papers and datasets. AI parses PDFs with scientific precision—equations, tables, methodology intact. Data is profiled automatically: columns, types, panel structure.

02

Discuss & Adapt

Ask AI about the methodology. It searches the actual paper, inspects your actual data, and helps bridge the gap. "I don't have daily returns—what are my options?" Save decisions as a methodology plan.

03

Generate & Execute

AI generates documented Jupyter notebooks from your plan. Markdown explains the why. Python executes the how. Run on cloud compute—no local environment setup.

04

Commit & Collaborate

Commit successful runs to your timeline. Every decision documented automatically. New team members can trace the entire research history from day one.

Research That Explains Itself

Stop losing knowledge when researchers leave.

Project Timelinemain
Data cleaning & filteringDec 10
📥 crsp_raw → 📤 crsp_clean
"Excluded financials, penny stocks, pre-2010"
Calculate MAX variableDec 12
📥 crsp_clean → 📤 max_quarterly
"Used quarterly MAX (Chen 2019 validation)"
exploration/3-day-maxDec 13
⚠️ Abandoned: weaker predictive power
Portfolio sortsDec 15
📥 max_quarterly → 📤 portfolios
"Decile sorts, value-weighted returns"
Fama-MacBeth regressionsHEAD

New researcher joins?

Full history visible from day one
AI summaries explain every decision
Click any commit to see the notebook
Abandoned paths documented too
No more "how did we do this again?"

Knowledge stays with the project,
not in someone's head.

Not Another Jupyter in the Cloud

What makes Methodi different

CapabilityJupyter/ColabGeneric AI + IDEMethodi
Paper parsing
Manual copy-paste
Loses equations
Scientific precision
Knows your data
No
No
Schemas in context
Methodology adaptation
Manual
Hallucinated
Grounded in paper + data
Decision history
None
None
Git-like timeline
Auto-documentation
None
None
AI summaries
Team collaboration
File sharing
Chat history
Shared project context

We're not replacing Jupyter—we're adding the infrastructure around it that quantitative research actually needs.

Ready to Transform Your Research Workflow?

Join teams spending less time on data wrangling and documentation, and more time on discovery.

Get Started Free

No credit card required. Start with our generous free tier.