Semeris vs Hebbia for CLO analysis
In short: Hebbia is an enterprise AI platform that ingests large document libraries and synthesises answers across any document type. Semeris is a CLO document specialist where every extraction is verified by in-house analysts. The difference is precision and accountability: Hebbia gives you AI synthesis at scale; Semeris gives you CLO-specific, human-verified answers you can take into a credit committee.
Scale vs verified precision
Hebbia's strength is horizontal reach. Its Matrix product ingests enterprise-scale document corpora — funds, private equity, credit, legal — and produces AI-generated answers with citations across any document type. For teams comfortable with AI-first, prompt-driven workflows over diverse libraries, that breadth is genuinely powerful.
Semeris is built the other way round: narrow and deep. It's purpose-built for CLO indentures and structured-finance documents, with custom datapoints that encode what to ask of a CLO deal, and a human-in-the-loop layer — 15+ in-house analysts verify every extraction before it reaches you. Hebbia can search almost anything; Semeris knows the CLO-specific questions to ask and confirms the answers are right.
Side by side
| Dimension | Semeris | Hebbia |
|---|---|---|
| CLO / structured-finance specialisation | Purpose-built, CLO-first | General enterprise documents |
| Human-verified extraction (HITL) | 15+ analysts verify every extract | AI output only, no verification layer |
| DvES deal scoring | Transparent debt-vs-equity subcategory breakdown | Not available |
| Stip / obligation tracking | Dedicated module: Excel → AI map → resolution → export | Not available |
| Custom CLO datapoints | Full flexibility + curated presets | Matrix queries, no CLO-specific verification |
| Market-wide deal comparison | Dynamic blackline compare | Keyword / semantic search across a corpus |
| Excel live data pipelines | Live pipelines + Data Table Wizard | Export-oriented, no live sync |
| Document ingestion scale | Unlimited uploads, no hidden fees | Enterprise-scale ingestion |
| Horizontal document-type coverage | CLO-first (handles complex docs broadly) | Any document type |
| Large-scale AI synthesis | Focused extraction engine | Matrix — broad AI synthesis at scale |
When to choose which
Choose Semeris when the work is CLO-specific and accuracy is non-negotiable — extracting and comparing indenture terms, scoring debt-vs-equity friendliness, tracking stip lists, and producing verified outputs you can defend. The human-verification layer exists precisely because one wrong extract in a credit committee is one too many.
Choose Hebbia when you need broad, AI-native search and synthesis across a large, diverse document library spanning many asset classes, and your team is comfortable working with AI output directly.
Both are AI-native; the distinction is that Semeris adds a human check on every CLO extraction, where Hebbia's AI output is the final output.
FAQ
What's the difference between Semeris and Hebbia? Hebbia is an enterprise AI platform that ingests large document libraries and synthesises answers across any document type, horizontally across funds, credit, PE and legal. Semeris is a CLO document specialist: it uses CLO-specific templates to know what to extract from an indenture, and 15+ in-house analysts verify every extraction. Hebbia offers AI synthesis at scale; Semeris offers CLO-specific, human-verified precision.
Is Semeris an alternative to Hebbia for CLO work? For CLO document analysis, yes — Semeris is purpose-built for it, with human-verified extraction, DvES scoring and stip tracking that a general enterprise-search platform doesn't provide. Hebbia's advantage is breadth across many document types; Semeris's is verified depth in structured finance.
How does Semeris handle AI accuracy compared with Hebbia? With Hebbia, the AI output is the final output. With Semeris, every AI-supported extraction is checked by an in-house analyst before it reaches the user, giving 96% extraction accuracy with full source traceability — human oversight built into the product rather than relying on the model alone.