Asset Classes

Request A Demo

Collateral Loan Obligations (CLO)

Our CLO library spans both US and European markets, covering deals backed by broadly syndicated loans (BSL) and middle market loans (MML).

Different collateral pools, different risk profiles, different documentation approaches. BSL deals typically feature larger, more liquid loan portfolios with standardized covenant packages. MML transactions contain smaller, less liquid credits with tighter manager oversight and more restrictive eligibility criteria. Then add the jurisdictional layer - US deals versus European structures with different legal frameworks, rating agency approaches, and market conventions.

The platform extracts 400+ normalized datapoints across all these deal types. Every metric from eligibility criteria to coverage tests to manager rights gets extracted using consistent definitions regardless of whether you're analyzing a US BSL transaction or an EU MML structure.

This includes standard terms that appear in every deal and bespoke topics you define for your specific needs. Build a checklist for EU sustainability provisions, run it across US deals, see what's comparable and what's not. Track how concentration limits evolved across vintage years. Compare reinvestment language between managers. That's what normalized data means: consistent extraction that enables actual comparison across market segments without forcing analysts to reconcile terminology differences manually.

Leveraged Loans

Wall Street obsesses over the numbers, and for good reason. But the real risk lives in the fine print. In leveraged loans, all those quantitative metrics you track are just outputs of something more fundamental, the Credit Agreement itself.

At Semeris, we start with a simple premise: words are data. While other platforms focus on deal metrics, we go straight to the source, the actual legal language that creates those metrics. We take dense credit agreements and convert them into structured, searchable data. Think of each 500-page document as a dataset with hundreds of defined terms that drive everything downstream.

With Semeris Docs, you get precise extractions that are:

  • summarized and concise,

  • normalized for comparability,

  • indexed for fast search-and-find,

  • source-referenced so you can always trace data back to its original context.

We don't believe in one-size-fits-all. Semeris offers fully customizable datasets built around what you actually need. We provide verified reference data for core economic terms—such as amortization schedules, PIK toggles, interest rate mechanics, etc.—all linked back to source documents. Want to track specific risk parameters? Build tailored checklists for transferability restrictions, whitelist provisions, confidentiality clauses, or whatever matters to your analysis.

We organize every document iteration, from preliminary drafts through amendments and compliance certificates, so you can see how deals evolve. Compare terms side-by-side, run blacklines, benchmark against the market. It's the kind of granular analysis that pure numerical databases simply can't deliver, because they're not built on the underlying language that actually governs these deals.

Semeris handles the full range of asset-backed securities (ABS) structures from mainstream auto and consumer loan deals to equipment finance, esoteric collateral types, and private transactions that don't fit standard templates.

Each ABS sector has distinct structural features: auto ABS with straightforward amortization schedules, equipment ABS with residual value considerations, and consumer loan ABS with diverse repayment patterns. Then you have the truly esoteric stuff - whole business securitizations, royalty-backed structures, litigation finance deals - where every transaction is essentially bespoke. Standard document templates don't exist for these deals, and the structural features that matter vary significantly from one collateral type to another.

Asset-Backed Securities (ABS)

The comparison and analysis capabilities work across this entire spectrum. Whether you're examining a publicly rated prime auto transaction or a private equipment deal with custom waterfall provisions, Semeris Docs identifies the relevant structural features, payment priorities, trigger mechanisms, credit enhancement levels, whatever drives the performance of the deal. This includes esoteric structures where the documentation doesn't follow any standard form. Compare reserve accounts across consumer loan deals.

Track how rapid amortization triggers evolved in auto ABS. Analyze servicer termination rights across equipment transactions. We let you compare similar structures and track how specific features evolved across deals in minutes rather than hours. That's what "smart and dynamic" means: tools that adapt to the structure you're analyzing rather than requiring the structure to adapt to the tools.

Private Credit

Private credit documentation is inherently varied - every deal reflects specific borrower circumstances, negotiated terms, and counsel preferences.

A unitranche facility for a software company looks nothing like a first lien/second lien structure for a manufacturing business. Add asset-based lending, NAV facilities, and subscription lines to the mix, and you're managing dozens of document types with limited standardization across them. This variety is intentional - it's what makes private credit flexible - but it creates real challenges for building security masters and tracking portfolio-level data.

Semeris brings the same workflow to private credit that works for CLO and ABS analysis - combining AI-powered extraction with human analyst verification to create normalized, reliable data from complex documents. Issuers track precedent language across deal pipelines with confidence that the extracted terms are accurate.

Investors build security masters that roll up correctly despite documentation differences. Arrangers compare terms across opportunities quickly, knowing the data they're comparing is verified.

The document intelligence challenge is the same across all these roles: extract varied data from complex documents in forms that enable fast, comparable analysis without sacrificing accuracy.