graphify

Graphify — Sales Intelligence Knowledge Graph

Build knowledge graphs from your product catalog, customer conversations, and market research to surface hidden connections, cross-sell opportunities, and competitive insights.

Based on graphify — adapted for B2B SDR context.

Triggers

• Manual: "Build a knowledge graph of our products"
• Manual: "Map customer relationships"
• Manual: "Analyze competitive landscape"
• Cron (optional): Weekly rebuild after lead-discovery updates

Prerequisites

# Ensure graphify is installed
python3 -c "import graphify" 2>/dev/null || pip install graphifyy -q --break-system-packages 2>&1 | tail -3

Use Cases

1. Product Catalog Graph

Build a graph from product-kb/ to understand product relationships, shared certifications, overlapping target markets, and cross-sell paths.

When to use: Before quotation, during BANT qualification, when customer asks about related products.

python3 -c "
import json
from graphify.extract import collect_files, extract
from graphify.build import build
from graphify.cluster import cluster, score_all
from graphify.analyze import god_nodes, surprising_connections
from pathlib import Path

# Extract from product catalog
files = collect_files(Path('product-kb'))
ast_result = extract(files)

# Build and analyze
G = build([ast_result])
communities, labels = cluster(G)
cohesion = score_all(G, communities)

gods = god_nodes(G, top_n=5)
surprises = surprising_connections(G, communities, top_n=5)

print('=== Core Products (God Nodes) ===')
for g in gods:
    print(f'  {g[\"label\"]} — {g[\"edges\"]} connections')

print('=== Surprising Connections ===')
for s in surprises:
    print(f'  {s[\"source\"]} ↔ {s[\"target\"]} [{s[\"confidence\"]}]')
"

Sales actions from graph insights:

• God nodes = your anchor products → lead with these in cold outreach
• Surprising connections = non-obvious cross-sell paths → "customers who buy X often need Y"
• Communities = product families → bundle pricing opportunities