Central AI vs. prompting blind
See what changes when AI actually understands your codebase.
Manual context vs. Central AI
| Task | Manual | Central AI | Time saved |
|---|---|---|---|
| Architecture docs | Write and maintain manually | Auto-generated | 2–4 hrs |
| Dependency mapping | Trace imports by hand | Automatic | 1–2 hrs |
| Convention rules | Write .cursorrules / prompts | Auto-detected | 1–2 hrs |
| Business logic docs | Document each module | Extracted automatically | 3–6 hrs |
| Cross-file context | Manually reference in prompts | Built-in | Ongoing |
| Keep docs updated | Manual after every change | Syncs on push | Ongoing |
| Multi-tool support | Duplicate for each tool | One layer, all tools | 1–2 hrs |
| Onboard new AI agent | Write context from scratch | Instant | 1–3 hrs |
Manual: 10–20+ hours
Central AI: < 5 minutes
vs. other approaches
| Approach | Auto context | Continuous | Multi-tool | Arch map | Conventions | Deep analysis |
|---|---|---|---|---|---|---|
| Raw prompting | ||||||
| Manual docs / READMEs | ||||||
| .cursorrules files | Partial | |||||
| GitHub Copilot | Partial | |||||
| Central AI |
What makes it different
Deep codebase understanding
Not just file-level context. We map architecture, business logic, conventions, and cross-file relationships.
Always up to date
Re-analyses on every push. Your context layer evolves with your code — no manual maintenance.
Works with every AI tool
One context layer powers Cursor, Bolt, v0, Copilot, and any AI tool you use.
Zero setup, zero code changes
Read-only access. No config files, no plugins, no changes to your repo. Connect and go.