graphify

name	graphify
description	Knowledge graph engine for B2B sales intelligence. Builds queryable graphs from product catalogs, customer conversations, and market research. Powered by 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

2. Customer Intelligence Graph

Build a graph from conversation histories and CRM data to map customer relationships, identify buying patterns, and find warm introduction paths.

Input sources:

• ChromaDB conversation history (chroma:recall)
• CRM records (Google Sheets)
• Supermemory research notes (memory:search)

What to extract (semantic, not AST):

• Companies → employees (decision makers, influencers)
• Companies → products they bought or inquired about
• Companies → companies (same industry, same region, competitors)
• People → people (referrals, shared contacts)
• Deals → products, timelines, objections

Sales actions from graph insights:

• Cluster customers by behavior → tailor nurture campaigns per cluster
• Find bridge nodes (customers who connect segments) → referral candidates
• Detect isolated nodes (customers with no follow-up) → stalled lead recovery

3. Market Research Graph

Build a graph from lead-discovery research, competitor intel, and market signals stored in Supermemory.

What to extract:

• Competitors → products, pricing, markets
• Markets → trends, regulations, trade shows
• Customers → competitors they also buy from
• Regions → seasonal demand patterns

Sales actions from graph insights:

• Surprising connections between markets → expansion opportunities
• Competitor clusters → differentiation strategy
• Market god nodes → priority regions for lead-discovery rotation

Graph Query (runtime)

After building a graph, query it for specific sales intelligence:

# BFS — broad context around a topic
python3 -m graphify query "hydraulic excavator certification" --budget 1500

# DFS — trace a specific relationship chain
python3 -m graphify query "Dubai customer fleet" --dfs --budget 1000

Use before:

• Responding to product questions → query product graph for specs and relationships
• Preparing quotations → find cross-sell opportunities in graph
• Cold outreach → understand prospect's market context from research graph

Graph Export

python3 -c "
from graphify.export import to_json, to_html
from graphify.build import build_from_json
from pathlib import Path
import json

data = json.loads(Path('graphify-out/graph.json').read_text())
G = build_from_json(data)

# Interactive HTML for owner dashboard
to_html(G, Path('graphify-out/graph.html'))

# JSON for programmatic access
to_json(G, Path('graphify-out/graph.json'))
"

• HTML: Interactive vis.js graph — share with owner for pipeline visibility
• JSON: Machine-readable — feed into reporting or CRM enrichment
• Report: graphify-out/GRAPH_REPORT.md — god nodes, communities, knowledge gaps

Output Format (report to owner)

Product Knowledge Graph:
- X nodes · Y edges · Z communities
- Core products: [god nodes list]
- Cross-sell opportunities: [surprising connections]
- Knowledge gaps: [isolated products with missing specs]

Recommendation: Update product-kb for [gap products] to improve graph coverage.

Integration with Other Skills

Skill	How Graphify Helps
lead-discovery	Query market graph before searching → better targeting
quotation-generator	Query product graph → include related products in quote
chroma-memory	Feed conversation data → build customer intelligence graph
supermemory	Feed research notes → build market research graph
sdr-humanizer	Graph context → more relevant, personalized conversations

Rebuild Strategy

• Product graph: Rebuild when product-kb/ changes (new products, updated specs)
• Customer graph: Rebuild weekly from ChromaDB + CRM snapshots
• Market graph: Rebuild after lead-discovery runs (daily 10:00 output)

Store graphs in graphify-out/ — survives across sessions, queryable anytime.