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Everything as Code: How We Manage Our Company in One Monorepo

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Introduction

Last week, I updated our pricing limits. One JSON file. The backend started enforcing the new caps, the frontend displayed them correctly, the marketing site showed them on the pricing page, and our docs reflected the change—all from a single commit.

No sync issues. No "wait, which repo has the current pricing?" No deploy coordination across three teams. Just one change, everywhere, instantly.

At Kasava, our entire platform lives in a single repository. Not just the code—everything:

kasava/ # 5,470+ files TypeScript files ├── frontend/ # Next.js 16 + React 19 application │ └── src/ │ ├── app/ # 25+ route directories │ └── components/ # 45+ component directories ├── backend/ # Cloudflare Workers API │ └── src/ │ ├── services/ # 55+ business logic services │ └── workflows/ # Mastra AI workflows ├── website/ # Marketing site (kasava.ai) ├── docs/ # Public documentation (Mintlify) ├── docs-internal/ # 12+ architecture docs & specs ├── marketing/ │ ├── blogs/ # Blog pipeline (drafts → review → published) │ ├── investor-deck/ # Next.js site showing investment proposal │ └── email/ # MJML templates for Loops.so campaigns ├── external/ │ ├── chrome-extension/ # WXT + React bug capture tool │ ├── google-docs-addon/ # @helper AI assistant (Apps Script) │ └── google-cloud-functions/ │ ├── tree-sitter-service/ # AST parsing for 10+ languages │ └── mobbin-research-service/ ├── scripts/ # Deployment & integration testing ├── infra-tester/ # Integration test harness └── github-simulator/ # Mock GitHub API for local dev

Why This Matters: AI-Native Development

This isn't about abstract philosophies on design patterns for 'how we should work.' It's about velocity in an era where products change fast and context matters.

AI is all about context. And this monorepo is our company—not just the product.

When our AI tools help us write documentation, they have immediate access to the actual code being documented. When we update our marketing website, the AI can verify claims against the real implementation. When we write blog posts like this one, the AI can fact-check every code example, every number, every architectural claim against the source of truth.

This means we move faster:

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