everyrowdocs
Overview
  • Installation
  • Getting Started
  • API Key
  • Skills vs MCP
  • Chaining Operations
  • GitHub
API Reference
  • dedupe
  • merge
  • rank
  • agent_map
  • screen
Guides
  • How to Add A Column to a DataFrame with Web Research
  • How to Classify and Label Data with an LLM in Python
  • Remove Duplicates from ML Training Data in Python
  • Filter a Pandas DataFrame with LLMs
  • How to Fuzzy Join DataFrames in Python
  • How to sort a dataset using web data in Python
  • How to resolve duplicate rows in Python with LLMs
Case Studies
  • Build an AI lead qualification pipeline in Python
  • Fuzzy join two Pandas DataFrames using LLMs
  • Fuzzy match and merge contact lists in Python
  • How to filter job postings with LLM Agents
  • How to merge datasets without common ID in Python
  • How to score and prioritize leads with AI in Python
  • How to Screen Stocks in Python with AI Agents
  • How to use LLMs to deduplicate CRM Data
  • LLM-powered Merging at Scale
  • LLM-powered Screening at Scale
  • Python Notebook to screen stocks using AI Agents
  • Run 10,000 LLM Web Research Agents
  • Score and rank leads without a CRM in Python
  • Use LLM Agents to research government data at scale
everyrowby futuresearch
by futuresearch

Notebooks

Runnable notebooks with real datasets. Each notebook demonstrates an everyrow operation end-to-end with output you can inspect.

Screen

  • LLM-Powered Screening at Scale
  • Screen Stocks by Investment Thesis
  • Screen Stocks by Margin Sensitivity
  • Screen Job Postings by Criteria

Rank

  • Score Leads from Fragmented Data
  • Score Leads Without CRM History
  • Research and Rank Permit Times

Dedupe

  • Dedupe CRM Company Records

Merge

  • LLM-Powered Merging at Scale
  • Match Software Vendors to Requirements
  • Merge Contacts with Company Data
  • Merge Overlapping Contact Lists

Research

  • LLM Web Research Agents at Scale

Multi-Method

  • Multi-Stage Lead Qualification