Nextdocs

The Challenge

Engineering documentation is universally acknowledged as critical, yet it's the first thing teams sacrifice under deadline pressure. The result is predictable: new developers spend weeks ramping up, senior engineers field constant interruptions, and engineering leaders make decisions with incomplete context.

Existing tools fall into two camps: manual documentation platforms that quickly become outdated, or basic auto-generators that produce shallow, unhelpful content. Teams needed something that could understand codebases deeply enough to generate genuinely useful documentation, and keep it current automatically.

What We Built

An AI-native documentation platform treating code as source of truth:

• Intelligent engine deriving module overviews, technical docs, and business context from source code
• Auto-updates regenerating documentation on every merge, maintaining 100% currency
• Visual documentation with automated data flow and user flow diagrams
• Real-time collaborative editor with Notion-like UX for team annotation and expansion
• GitHub integration triggering updates automatically as part of the development cycle

Technical Architecture

A multi-layer system combining static analysis with modern AI:

Analysis (Python + Go): Fast static analysis extracting code structure and dependencies. Go handles performance-critical parsing; Python orchestrates the pipeline. Medium projects (30k-100k LOC) documented in ~15 minutes.

AI Layer (RAG + LLM): Retrieval-Augmented Generation combining codebase context with domain knowledge. AST-level understanding merged with LLM reasoning produces technically accurate, readable documentation that captures not just syntax but architectural patterns and engineering intent.

Collaboration (React + TypeScript + Node.js): Real-time editing with operational transformation and sub-100ms latency. Modern interface makes it easy to annotate and clarify auto-generated content.

Differential Updates: GitHub webhooks trigger regeneration of only affected sections, reducing compute costs by 90%+ and keeping update times to 2-3 minutes per PR.

Impact

Onboarding time reduced from weeks to days. Documentation coverage reaches 100% automatically with zero maintenance overhead. Engineering leaders gain complete visibility into system architecture without digging through code. 100+ hours saved per project in manual documentation effort while reducing dependency on tribal knowledge.

Outcome

400+ projects documented and counting. The platform continues to evolve with active development focused on deeper code understanding and richer visualization - proof that we build solutions to problems we understand intimately as engineers ourselves.