# Ecosystem Stack | Deriss

Canonical: https://deriss.com/ecosystem
Description: Deriss maps Europe's Experience Layer thesis and the ecosystem stack building it — from institutional vehicles to consumer products.

---
# Deriss Ecosystem

Source: https://deriss.com/ecosystem
Canonical context: https://deriss.com/llms-full.txt

## Stack

```
Thesis  →  Engine  →  Products  →  Partners
```

- **Thesis** — Research and frameworks (public and private). The category framing "the human-centric era" anchors the stack.
- **Engine** — The Studio (advisory) and the Terminal (diagnostics) convert the thesis into engagements and product.
- **Products** — Tools and ventures built on the thesis. The Deriss Terminal is the flagship.
- **Partners** — Brands, founders, and operators executing engagements with Deriss.

## How the pillars connect

Research authors the language. The Studio applies it in senior advisory work. The Terminal operationalizes the diagnostic patterns that emerge from advisory. Ventures and partners extend the thesis into the market.

The pillars are not independent business units. They are one practice expressed in four registers: written, advised, productized, partnered.

## Read more

- Experience Layer thesis: https://deriss.com/experience-layer
- Research: https://deriss.com/research
- Terminal: https://deriss.com/terminal
- Studio: https://deriss.com/studio

## Agent layer — readable by machines, open to agents

Deriss publishes the way it advises: the thesis, research, and cases are machine-legible by design — AI assistants can read and cite them, and approved agents can contribute back under human review. The Experience Layer posture, applied to our own front door.

- **Public MCP — Read the research.** Connect Claude, ChatGPT, Cursor, or any MCP-compatible agent to search and cite Deriss research and Studio cases. No login.
- **Contributor MCP — Submit drafts.** Invite-only: approved researchers and their agents file drafts straight into the editorial review queue. Nothing publishes without human sign-off.
- **Answer engines & archives.** Structured surfaces for answer engines and long-form ingestion: `llms.txt`, `content-index.json`, per-article `.md`/`.txt` mirrors, RSS.

See how to connect: https://deriss.com/connect