graphify

Build queryable knowledge graphs from B2B sources and export results to graphify-out/.

152|46|Updated Mar 23, 2026

Search Tags:#knowledge graph #sales intelligence #market research #cross-sell #customer analytics #graph querying

Ready to run

npx skills add https://github.com/iPythoning/b2b-sdr-agent-template --skill graphify-ipythoning

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: graphify
Source: https://github.com/iPythoning/b2b-sdr-agent-template/tree/main/skills/graphify
Command: npx skills add https://github.com/iPythoning/b2b-sdr-agent-template --skill graphify-ipythoning

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires graphifyy.

What problem does it solve?

Graphify converts scattered business information—product catalogs, customer conversations, and market research—into a connected knowledge graph that reveals cross-sell paths and competitive insights that are hard to spot manually.

Core Features & Use Cases

Product catalog graphs: Build a queryable map of product relationships, shared attributes, and cross-sell opportunities to support qualification and quotation prep.
Customer intelligence graphs: Represent companies, people, deals, and relationships extracted from ChromaDB, CRM records, and research notes to cluster behavior and find referral bridges.
Market research graphs: Connect competitors, markets, regulations, and regions to prioritize lead-discovery focus areas and craft differentiated strategies.

Quick Start

Ask an AI to build and analyze your product knowledge graph from the folder product-kb/ and return core nodes plus surprising cross-sell connections.

Frequently Asked Questions about graphify

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I build a knowledge graph from product catalogs and customer conversations?▼

Build a knowledge graph from product catalogs and customer conversations by extracting entities and relationships, then clustering and scoring them to reveal cross-sell opportunities. Graphify processes these B2B sources to generate a queryable map of product relationships and customer buying patterns.

What is sales intelligence knowledge graph querying for cross-sell discovery?▼

Sales intelligence knowledge graph querying is the process of mapping product relationships, customer behaviors, and market trends to uncover hidden cross-sell paths. It connects scattered B2B data from CRM records and research notes into a structured graph for runtime exploration.

Can I use CRM records and ChromaDB data to map customer buying patterns?▼

Yes, you can use CRM records and ChromaDB data to map customer buying patterns. Graphify extracts companies, people, and deals from these sources to cluster behavior and identify referral bridges within a customer intelligence graph.

How do I connect competitors, markets, and regulations for market research analysis?▼

Connect competitors, markets, and regulations by building a market research graph that links these entities to prioritize lead-discovery focus areas. This approach helps craft differentiated strategies by revealing competitive insights and regional trends.

Do I need to prepare input data in a specific folder before building a product relationship graph?▼

Yes, you should organize input data such as product catalogs or research notes in a designated folder like product-kb/ before extraction. Graphify requires structured B2B sources to build the graph and output results into graphify-out/ for querying.

What's the best way to find cross-sell opportunities from scattered business information?▼

The best way to find cross-sell opportunities is to convert scattered business information into a connected knowledge graph. By mapping shared product attributes and customer relationships, you can uncover hidden cross-sell paths that are difficult to spot manually.