Code Conductor records 37,927 monthly visits with +6.2% month-over-month growth and Contact for Pricing pricing. pandō (human.software) records 0 monthly visits with 0% growth and Freemium pricing.
Build apps and AI agents without losing architectural control
A versioned code engine that lets AI agents edit code as a graph while exposing less source
| Dimension / Metric | | |
|---|---|---|
| Official Domain | codeconductor.ai | human.software |
| Pricing Model | Contact for Pricing | Freemium |
| Monthly Visits | 37.93K (37,927) | 0 (0) |
| MoM Traffic Delta | +2.22K (+6.2%) | 0 (0%) |
| Primary Market & Share | 🇺🇸 United States (33%) | 🌐 Global (—) |
| Direct Visits / mo | 12.66K | 0 |
| User Rating | 4.3 | 4.6 |
| Supported Platforms | Website | Website |
Build apps and AI agents without losing architectural control
Describe an application in natural language and get frontends and backends that compile to clean, exportable code following pre-approved architectural patterns.
Assemble secure AI agents connected to your data stores, APIs and knowledge bases, with model choices constrained by workspace policy.
Role-based access, tenant-level data isolation, API rate limits and model-usage rules enforced by default across every app and agent.
Full audit trails of who changed what and when, deploy approvals before production, and automatic redaction of personal data from AI responses.
A versioned code engine that lets AI agents edit code as a graph while exposing less source
Native tools for discover, rename, insert, transform, change parameters, delete and history selection, exposed to any MCP-compatible coding agent through a stdio server.
Code is addressed as a graph, so renaming a symbol carries its bound references across the project and operations report exactly what applied or was refused.
Every committed structural change is checkpointed automatically. You can select any earlier snapshot with a single call and continue from that point without undo sequences.
Benchmark slices in the VS Code report show about 68% fewer source bytes reaching the model provider versus a text-tool agent, because agents request focused reads instead of whole files.
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