AI coding
costs 71×
more than
they should.

One knowledge graph changed that.
450K engineers adopted it in 26 days.

of every AI coding query
is pure waste.

Your tool loads the entire codebase — every file, every function — just to answer a question about 3 files. At 50M queries per month, that's $105,000. Mostly wasted.

Graphify
maps your
codebase.
Then routes
only what
matters.

Knowledge graph + Leiden community detection. Query gets ~1K tokens of relevant context. Not 70K of noise.

4 steps. Seconds to run.

1. 01
   Parse the codebase

   AST analysis builds nodes (files, functions) and edges (imports, calls, inheritance)
2. 02
   Run Leiden detection

   Iterative modularity maximisation groups related code into stable clusters
3. 03
   Route the query

   Semantic match finds the right cluster — auth, DB, UI, infra — whatever you asked about
4. 04
   Load only that cluster

   ~1,000 tokens into the model. 71× fewer than naive loading.

Your codebase
is a city.

Graphify builds the map,
labels every neighbourhood,
and drives you to the right street —

instead of taking you on a tour
of every road, every time.

Cost per million
AI coding queries

A team at 50M queries/month saves $103,500 every month. Same workflow. Same output quality.

When a tool solves a real pain,
engineers show up fast.

4 ways to break it

• Skip reindexing after a major refactor — clusters go stale, quality tanks
• Run naive dump alongside graph retrieval — kills the savings entirely
• Skip resolution tuning on large monorepos — shared utilities won't cluster cleanly
• Treat clusters as file folders — they're logical units, not directory structure

Weekly AI
engineering
patterns.

Real tools, real numbers, no hype.
Full article in the first comment.