Semeris vs Dealscribe for CLO analysis
In short: Semeris and Dealscribe both bring structure to CLO documents, but from opposite directions. Dealscribe is a pre-extracted research database — its researchers extract 350+ standardised deal terms across 3,000+ CLOs, and you query their taxonomy. Semeris is a workflow platform where you define the datapoints, AI extracts against your own templates, and in-house analysts verify every result. The real question is who defines the questions.
Two models, one market
Dealscribe answers "show me the standard terms across the CLO market, ready to query." It's a reference database: a team of researchers pre-extracts a fixed set of 350+ deal terms across 3,000+ CLOs, with Bloomberg Terminal and Moody's Analytics integrations, so subscribers get instant market-wide data with zero setup.
Semeris answers "analyse these deals on my framework." Rather than a fixed taxonomy, you build custom datapoints and topics; AI extracts them against your templates, and 15+ in-house analysts verify every result before it reaches your workflow. Where Dealscribe hands you their questions pre-answered, Semeris lets you ask your own — and verifies the answers.
Side by side
| Dimension | Semeris | Dealscribe |
|---|---|---|
| Model | Software platform — you define the analytical framework | Research database — pre-extracted, fixed taxonomy |
| Custom datapoint / topic definition | Curated presets + full flexibility to build your own | Fixed to 350+ preset terms; filter and query only |
| Pre-extracted market database | Extract on demand and compare across the market | 350+ terms across 3,000+ CLOs, ready to query |
| Human verification | 15+ analysts verify every extract | 25 researchers verify every term |
| Stip / obligation tracking | Dedicated module: Excel → AI map → resolution → export | Not available |
| Market-wide comparison | Dynamic blackline compare | Static database of 3,000+ deals |
| Deal scoring | DvES — transparent debt-vs-equity subcategory breakdown | D-Score — standardised 0–5 quality rating |
| Bloomberg / Moody's integration | Not available | Native Bloomberg + Moody's Analytics |
| Turnaround & uploads | ~1-day document turnaround; unlimited uploads | ~1 week to cover new documents |
| Inline document translation | Multilingual matching in Stip Tracker | Full inline translation (Japanese and others) across docs |
When to choose which
Choose Semeris when you want to analyse the deals you're actively working on your own framework — defining custom datapoints, tracking stip lists from Excel to resolution, running dynamic cross-deal blacklines, and getting verified extractions into Word or Excel fast. This is what Semeris is built for, with a human-verification layer on every result.
Choose Dealscribe when you want a ready-made, market-wide database of standardised terms with zero setup, and native Bloomberg/Moody's integration or full inline document translation are priorities.
Some teams use both — Dealscribe for instant standardised breadth, Semeris for custom, verified analysis on the deals that matter most.
FAQ
What's the difference between Semeris and Dealscribe? Dealscribe is a pre-extracted research database: its researchers extract 350+ standardised deal terms across 3,000+ CLOs, and you query that fixed taxonomy, with Bloomberg and Moody's integrations. Semeris is a workflow platform where you define custom datapoints, AI extracts them against your templates, and 15+ in-house analysts verify every result. Dealscribe defines the questions for you; Semeris lets you define — and verify — your own.
Is Semeris an alternative to Dealscribe? They overlap on cross-deal CLO data but work differently. Dealscribe gives ready-made standardised terms with zero setup; Semeris gives custom, analyst-verified extraction, stip tracking, dynamic blacklines and fast turnaround on the deals you actively work. Teams with proprietary analytical views tend to prefer Semeris; teams wanting instant standardised breadth value Dealscribe.
Which is better for custom CLO datapoint extraction? Semeris — it's built around user-defined datapoints and topics rather than a fixed term set, so you can extract and compare any language you choose across the market, with every AI extract verified by an in-house analyst and traceable to its source.