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OpenEden RWA Intelligence Suite

A consulting engagement delivering three research modules for OpenEden’s RWA products: PRISM, USDO, and TBILL — with reproducible pipelines, charts, and tests.

Consulting • Python • pandas • Reproducible reports •
Problem OpenEden needed clear, reproducible analysis on competitive positioning, on-chain usage (velocity) on XRPL vs Ethereum, and drivers behind USDO supply drawdowns — in a form that could be validated and handed off.
Solution Delivered three modules: (1) competitive landscape and objection handling, (2) on-chain velocity, chain prioritization, and 90-day actions, (3) supply cohort modeling with scenario planning and cohort-flow artifacts — with cached raw data, analysis scripts, and unit tests.

What shipped

Module 1 — Competitive Landscape

Competitive matrix for PRISM vs tokenized yield peers (Ondo, Maple, Superstate, Hamilton Lane), plus a value prop and investor objection handler with confidence levels and sourcing where available.

  • Outputs: Competitive matrix, notes on apples-to-oranges comparisons
  • Scope: What’s knowable today vs what’s undisclosed / needs verification

Module 2 — On-chain Velocity & Holder Behavior

XRPL vs Ethereum TBILL activity analysis, a Doppler Finance case study, a weighted chain priority matrix for USDO expansion, and three concrete 90-day recommendations to move XRPL from “storage” to “velocity”.

  • Outputs: Case study, chain scoring model, 90-day action plan
  • Finding: Large velocity gap between XRPL and Ethereum TBILL transfer activity

Module 3 — USDO Supply Scenarios & Cohort Context

A 12‑month bear/base/bull scenario model for USDO supply, plus a root-cause analysis of the post‑incentive drawdown using cohort boundaries derived from structural inflection points in the supply series.

  • Outputs: Scenarios and actions, hypotheses H1/H2/H3 with noted data gaps
  • Artifact: Cohort-flow diagram for “peak → steady state” retention

Artifacts (charts + cohort flow)

Monthly transfer activity comparing XRPL and Ethereum TBILL token velocity
Module 2 — Monthly transfer activity: XRPL vs Ethereum (velocity signal).
Cumulative transfer counts plotted against unique holder snapshots
Module 2 — Cumulative transfers vs unique holders (activity concentration).
Estimated holder archetype breakdown by monthly transfer activity
Module 2 — Holder archetypes by transfer behavior (who drives velocity).
Module 3 — Cohort flow (Sankey): peak → drawdown → steady state, with retention vs redemptions.

How it’s built

Raw data is cached, transformations have unit tests, and the analysis produces Markdown deliverables so the work can be re-run or updated.

Project structure

openEden Consulting Project/
├── reports/                 # Final deliverables (Markdown)
├── data/                    # Cached raw + processed artifacts (timestamped)
├── src/
│   ├── collectors/          # Data ingestion (RWA.xyz, DeFiLlama, etc.)
│   ├── analytics/           # Metric computation + cohort analysis
│   └── formatters/          # PDF generation, report artifacts
└── tests/                   # Unit tests for transformations

Runbook

cd "openEden Consulting Project"

# Run the full pipeline (collect → analyze)
make run-all

# Run tests
make test

Takeaways

  • Velocity ≠ custody: XRPL holds a large share of tokenized T‑bills but shows much lower TBILL transfer volume than Ethereum — driven by composability and secondary-market rails.
  • Supply drawdowns are cohort-shaped: USDO’s post‑incentive contraction fits a cohort retention problem (peak → steady state retention) rather than a “single narrative” issue.
  • Sourcing and uncertainty: Claims are sourced or flagged with [ASSUMPTION]/[DATA: as of]; low-confidence comparisons are called out.