Cartographer: A Thesis for Semantic Code Memory

The Problem

AI coding assistants today are file-bound. Their superpower is instant reasoning, but only within the narrow slice of code you show them. Large codebases are where most professional developers live — yet they’re where AI assistants stumble most.

Key issues:

• Token limits: Context windows are too small for full repos.
• Inefficient context stuffing: Dumping more code is expensive and noisy.
• Lack of structure: LLMs don’t know dependency graphs, symbol usage, or project layouts.

The result: hallucinations, wrong imports, broken reasoning.

The Solution

Cartographer builds a semantic layer between code and AI assistants.

• Representation: Structured summaries of files, classes, functions.
• Incremental updates: Change a file, only that part of the graph updates.
• Cross-referencing: Symbols are linked across files.
• Search & retrieval: Embeddings + symbol graphs ensure the right context is injected.

This shifts the LLM’s job from parsing the repo to reasoning on the right subset of it.

Why It Matters

• For developers: AI assistants stop being toys and start being power tools.
• For teams: Shared code memory → consistent AI context for everyone.
• For tool builders: Cartographer plugs in instead of reinventing the wheel.

This is an infrastructure layer. Every AI coding assistant needs it. Cartographer makes it open, extensible, and local-first.

Business Thesis

Cartographer follows the open-core model:

• OSS core: single-repo, local, developer-friendly.
• Pro/Enterprise: multi-repo, dashboards, cloud sync, integrations, SLAs.

Market signals:

• Developers already paying $20–30/mo for copilots.
• Enterprises struggling with internal AI adoption on large codebases.
• Every new AI IDE hitting the same context wall.

Cartographer is positioned as the enabler for this wave.

Roadmap to Validation

1. OSS Launch: Build traction, adoption, community.
2. Waitlist for Pro: Capture demand signals.
3. Interviews with Teams: Discovery of pain points.
4. Beta Pro Trials: Paid pilots with startups/enterprises.
5. Partnerships: Integrate with Cursor, Sourcegraph Cody, Zed.

Long-Term Vision

Cartographer becomes the semantic substrate for AI coding:

• Runs locally or in cloud.
• Multi-language, multi-repo, enterprise scale.
• The “memory layer” that every AI coding assistant builds on.

In short: LLMs can reason. Cartographer makes sure they know what to reason about.