Platform Dependencies
Features this venture requires from the platform.
Platform Dependencies
| Feature ID | Feature | Why | Type |
|---|---|---|---|
| AUTH-001 | User registration | Client and consultant accounts | Service |
| USER-001 | User profiles | Consultant identity and client records | Service |
| AI-002 | Conversational AI | Core AI advisory capability — orchestrating agent workflows for clients | Service |
| COLB-002 | Document collaboration | Client-facing proposal and deliverable workspace | Service |
| MKTG-001 | Marketing automation | Agency proposal pipeline and outreach sequencing | Service |
Context
- Platform feature matrix — Full capability register
- First principles — Business model ground truth for any advisory engagement
- Critical path — What must be true before revenue
- Business artifacts — The deliverable types clients pay for
- Navigation principles — How good judgement compounds in practice
- AI orchestration — The technical capability being sold
- Agency business model — How AI advisory compounds
- Verifiable intent protocol — L1→L2→L3 delivery model
- Standards — Naming conventions the advisory work builds on
- Scoreboard reality — Measurement layer the advisory proves and sells
- Agency — The capability development being sold as the service
- Marketing automation — The outreach pipeline the agency operates
- Software protocols — The agent protocol layer the advisory service runs on
- Science principles — The empirical foundation for advisory recommendations
- Platform — The capability layer the advisory builds on
- Matrix thinking — How advisory maps client gaps across dimensions
- Journey priorities — What determines where advisory attention compounds fastest
- Behavioural biases — What blocks client change and how to design around it
- Systems thinking — The reasoning framework the advisory service teaches and applies
- Smart contracts — The protocol layer advisory work is built and verified on
- Purpose — Why this venture exists and what advisory must prove
- Community — The network effect the advisory builds beyond the engagement
- Collective agency — How advisory outcomes compound at team and org level
- Tokenization — How advisory outputs can be tokenized as verifiable proof of transformation
Questions
What is the minimum platform capability required to deliver the first paid AI advisory engagement?
- Which feature, if delayed, would block the first client delivery — and is that blocking feature already built?
- What would it take to replace each platform dependency with a manual process for the first 3 clients — and which of those manual processes should stay manual?
- At what client count does the dependency on COLB-002 become a competitive moat rather than a cost?