For Researchers
Independent analysts, academics, journalists, and data scientists get connected structured-finance data with transparent lineage and AI-ready context for reproducible research.For Researchers
Independent analysts, academics, journalists, and data scientists get connected structured-finance data with transparent lineage and AI-ready context for reproducible research.
Your research becomes assured context
Use Assured Research Data →
The Research Challenge
Analysts, academics, and data scientists know where the data lives — but not how it connects.
Filings are verbose, inconsistent, and rarely machine-readable. Provenance is hard to prove, and normalized, reusable datasets are scarce.
Dealcharts provides open, assured context so you can study structured finance as a connected system — not a collection of PDFs.
What Dealcharts provides
1. Study structured finance as a connected system
Problem: Research workflows rely on scraped filings or vendor-locked datasets that lose relationships between issuers, trusts, and servicers.
What you get: A queryable Context Graph of structured finance — linking deals, counterparties, and filings with lineage and attribution.
2. Publish findings as structured "Signals"
Problem: Insights get buried in PDFs or posts detached from their data.
What you get: Publish Signals directly on relevant deal or entity pages, with attribution and metadata. Discoverable by analysts, APIs, and AI systems using CMD+RVL's Context Engine.
3. Build a transparent research identity
Problem: There's no consistent way to attribute structured-finance research across individuals, labs, or pseudonymous outlets.
What you get: Context-linked author pages showing your publications, datasets, and cited entities — built automatically from your Signals and uploads.
4. Collaborate in shared context
Problem: Coordination across issuers, funds, and data providers is hard without shared data infrastructure.
What you get: Shared context graph with permissioned annotations, co-authored Signals, and lineage-aware visualizations.
5. Train and test AI models on assured data
Problem: Model builders need provenance-rich data to meet compliance and reproducibility standards.
What you get: Access to CMD+RVL's assurance-grade datasets (EDGAR, NPORT-P, deal metadata) with lineage and attribution intact.
Summary
| Category | Action | What You Get | Why It Matters |
|---|---|---|---|
| Connected System | Link deals, issuers, filings | Relational understanding | Enables systemic research |
| Publication | Publish structured Signals | Attribution + visibility | Work stays discoverable |
| Transparency | Context-linked author profiles | Open authorship | Builds credibility |
| Collaboration | Shared data context | Reproducible co-research | Promotes co-curation |
| AI Research | Use assurance-grade datasets | Provenance + structure | Build reliable models |
See it in action
Explore how independent researchers and data scientists use Dealcharts to connect structured-finance data, publish contextual insights, and build transparent AI models.
Ready to make your research part of the public context?
Use Dealcharts to connect data, publish findings, and power explainable AI models.
Use Assured Research Data →