OpenEden RWA Intelligence Suite

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

Consulting • Python • pandas • Reproducible reports • Feb 27, 2026

Problem OpenEden needed institutional-grade clarity on competitive positioning, real usage (velocity) in XRPL vs Ethereum, and the structural drivers behind USDO supply drawdowns — in a form that could be validated, reproduced, and handed off.

Solution Shipped a 3-module intelligence suite: (1) competitive landscape + objection handling, (2) on-chain velocity + chain prioritization + 90-day actions, (3) supply cohort modeling with scenario planning and cohort-flow artifacts — backed by 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 differentiated value prop and an investor objection handler with explicit confidence levels and sourcing.

Outputs: Competitive matrix, self-critique of apples-to-oranges comparisons
Decision utility: 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
Key insight: a ~15,000× velocity gap (XRPL TBILL transfers vs Ethereum)

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 + actions, hypotheses H1/H2/H3 with explicit data gaps
Artifact: Cohort-flow structure 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 (reproducible by design)

The engagement is structured like a small product: raw data is cached, transformations are test-backed, and the analysis produces deterministic Markdown deliverables.

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

Results (high-signal takeaways)

Velocity ≠ custody: XRPL holds a large share of tokenized T‑bills but shows negligible TBILL transfer volume relative to Ethereum — the difference is composability and secondary-market rails.
Supply drawdowns are cohort-shaped: USDO’s post‑incentive contraction is best modeled as a cohort retention problem (peak → steady state retention) rather than a “single narrative” issue.
Institutional tone + explicit uncertainty: every claim is sourced or flagged with [ASSUMPTION]/[DATA: as of], and low-confidence comparisons are called out directly.