LLMs generate code.
IQ architects software.

An LLM normally encounters software as text: files, snippets, search results and whatever fragments fit into its context window. It has to reconstruct the program's structure every time it reasons about it.

IQ changes the relationship completely. It turns code into the structured, persistent world that the code actually represents. Then reasons about it as an abstract execution graph.

Code is a graph.
LLMs mostly see text.

The difference matters more as software becomes larger, more distributed and more AI-generated.

src/auth/service.ts

↓

some retrieved lines

src/users/service.ts

↓

another retrieved fragment

src/api/routes.ts

↓

another context window

relationships must be inferred

architecture must be reconstructed

runtime behaviour must be guessed

User

→

API

→

Service

State

↔

Data

↔

Dependencies

The code expresses a connected runtime graph. IQ makes that graph explicit and persistent.

Every time an LLM reasons about the repository, it has to rediscover the repository.

Repeated reconstruction

The model repeatedly infers symbols, dependencies, call paths, ownership and relationships from fragments of source.

Context inefficiency

Context is spent reconstructing structure that could already exist as explicit machine-readable knowledge.

Semantic drift

Without a coherent model of the whole program, changes are often locally plausible but globally inconsistent.

AI-generated entropy

Similar functionality gets implemented repeatedly because the model cannot reliably see that the capability already exists elsewhere.

AI can make code faster.
It can also make it messier.

When an agent sees only the local text relevant to a task, it naturally optimizes for the local task.

It may create another helper instead of discovering an existing one. Another abstraction instead of extending the existing abstraction. Another service instead of finding the capability already present.

Each individual change can look reasonable. Collectively, the system accumulates duplication, inconsistent abstractions, dead paths and architectural drift.

A coherent model of the software itself.

IQ gives intelligence something text alone cannot: a persistent representation of the program's entities and relationships.

this function implements that capability

this service depends on that interface

these two functions are semantically related

changing this entity affects these paths

this capability already exists

Give intelligence the program's world.

IQ converts source into a canonical GraphIR representation in which the structure of the program becomes part of your world model.

code.ts

→

Code World

Entities · relationships · dependencies · structure · provenance · semantics · annotations

→

code.ts

Coding → IQ → QUERY → REASON → TRANSFORM → IQ → Coding

Don't retrieve more text.
Change what the model knows.

RAG helps an LLM find relevant documents and code fragments. That is useful, but the model is still receiving text and reconstructing the relationships between those fragments.

IQ makes those relationships explicit. The model can reason over the program as structured knowledge rather than repeatedly rebuilding its architecture from retrieved text.

"Here are some files that may be relevant."

"Here is the program and how its parts relate."

Understanding is not a one-way trip.

IQ is designed to preserve enough structure and provenance that the representation can travel in both directions.

Grok the Code

Parse source and construct a canonical understanding of the program.

Engineer the Architecture

Query, reason, analyse, enrich and transform the program as IQ.

Semantic Serialization

Serialize the resulting representation back into executable source.

The next problem isn't generating more code.
It's maintaining coherence.

As AI writes more software, the limiting factor becomes less about producing another function and more about understanding whether that function belongs in the system at all.

Does the capability already exist? What depends on it? What will this change break? Which abstraction should be extended? Which implementation is authoritative?

Those are questions about the graph of the program, not the text of an individual file.

IQ gives AI a persistent model of that graph.

Many languages.
One program model.

IQ understands software through a canonical representation of their structure, semantics, relationships, and behaviour. Language-specific syntax is translated into that shared model — so the intelligence operates on the program itself, not on whichever language happened to express it.

Parse

Map

Grok

Operate

Round-trip

Stop asking intelligence
to rediscover the software.

Give it your world the software already describes. Let it reason over that world. Let it change that world. Then let your world become code again.

LLMs generate code. IQ architects software.

Code is a graph. LLMs mostly see text.