ZUVELEKCAPITAL

Origination

We find transactions before they come to market.

The firm runs a proprietary origination engine, built in-house and running continuously. It reads what lenders file with the SEC line by line, layers intent and behaviour signals over the record, and builds coverage by direct research where the filings stop. The output is a ranked view of the companies the evidence says will need to transact. We call the discipline Evidence First Origination, and every mandate this firm takes begins inside it.

Most companies meet the market once, near the deadline.

The non-marketed market is the set of companies that will have to transact within two years and have told nobody. No bank is running a process, so there is no teaser and no data room. The maturity date is filed with the SEC, and the lender who set it has known it since the day the loan was signed.

If you own one of those companies, you are negotiating against the only counterparty that has been preparing for the date, and the extension offer you receive prices the absence of alternatives. If you run a sponsor or a fund waiting for marketed deal flow, you see each opportunity at the same moment as every competitor, in an auction that sets the price. If you are building a platform by acquisition, you are doing that repeatedly, and every auction you enter is paid for out of the same thesis.

These are performing companies with a deadline, which puts the work outside distress and outside restructuring. Most origination starts from a list and argues the evidence afterwards. Ours starts from the evidence, and reading it on your behalf, before a process exists, is what we mean by Evidence First Origination.

How Evidence First Origination runs

Business development companies file a schedule of investments with the SEC each quarter, and each line of that schedule is a loan to a private company. Evidence First Origination runs in five stages, from the raw filing through to a written report on a single situation. Alongside the filings, the engine reads intent and behaviour signals drawn from the record itself: the marks lenders put on their own loans, how books shift quarter to quarter, and amendments as they land.

01

Filings

Every line of every schedule of investments, quarter after quarter, broken into borrower, instrument, spread, principal and maturity date. Filings are read in full, so a small loan in a large book is treated the same way as a headline position, and where disclosure stops, coverage is built by direct research against written criteria.

02

Borrowers

Borrower names are matched across filings and across lenders, so one company's debt picture emerges from documents filed by several parties who each see part of it. Matching is probabilistic and we treat it that way, holding a name back where the evidence is thin.

03

Ranking

Companies scored and ranked by what the evidence says they will have to decide, and when. A tranche maturing in the next six to twenty-four months is the clearest signal, and each ranked name faces a decision with a small number of answers: refinance, sell, raise new capital, or take whatever the incumbent lender offers to extend.

04

Lenders

Lender families profiled from their own filings, so the capital side of the situation is known before anyone is contacted.

  • Typical check size and the range around it
  • Pricing and lien mix across the book
  • Share of sponsored deals against direct lending
  • Sector concentration, and where it is moving quarter to quarter
05

The Lender Report

The written output, delivered to you in six fixed sections. It names the situation, the filings behind it, the comparable funded loans, the lenders and the reason each one is listed, the limits of the data and the date the maturity forces.

Decision pointThe report is written before any counterparty is approached, so you decide on evidence whether a transaction is worth running at all.

Never bring a name without the evidence that put it there. A name we cannot source stays out of the ranking, and the same test decides which lenders reach the list.

How the engine stands up in diligence

Before you rely on a data-driven origination claim, you would ask the same six questions we would: where the data comes from, how much of it there is, how often it refreshes, how names are matched, what happens to bad rows and what the output leaves out. These are the answers. Every figure below is a count from the engine's own database as it stands, built from filings up to period end 30 June 2026.

Engine Specification
Sources
SEC EDGAR. Business development companies attach a schedule of investments to every 10-Q and 10-K, and each line of each schedule is one loan to one private company. The engine fetches the filings directly from the SEC, with the firm named in every request, and parses each line into borrower, instrument, principal, spread and maturity date.
Universe
The SEC issues every entity that elects BDC status a file number in a single series, and the engine enumerates the whole series, so the lender census is exhaustive by construction. 369 file numbers currently carry a filer with filings on record; 112 filers produced loan rows for the latest reporting quarter. The rest are dormant vehicles, older filings or filings the parser rejected, and a rejected filing contributes nothing.
Scale
The current build holds 43,470 loan-level positions, 14,144 raw borrower name strings and 10,351 resolved companies. These counts move at every refresh and still carry a known level of parser noise, which is why no headline market total appears on this site until it survives re-verification.
Refresh
The refresh anchor is the filing calendar, because that is when new information exists. A 10-Q lands about 45 days after quarter end, so the freshest loan book is 45 to 135 days old, and every report states the period it draws from. The engine tracks its own staleness, and when a full reporting cycle is missing it marks its own queue as not current.
Entity resolution
Names are matched in two layers. A strict normalisation layer matches exact forms at full confidence. A fuzzy layer runs behind it, tuned for precision: a merge must clear a high similarity threshold and carry corroboration from a shared sector or sponsor, holding-company tiers are never collapsed into one borrower, and fuzzy confidence is capped below exact. In the current build the fuzzy layer scored 69,377 candidate pairs and accepted 39 merges. Resolution stays probabilistic and can be wrong, so a name with thin evidence is held out of the ranking.
False positives
Every parsed row carries a confidence score. The most dangerous error in this data is a silent scale error, thousands read as millions, so scale detection runs as its own check, and a row that fails plausibility tests has its figures nulled and the reason recorded. A filing that fails to parse contributes zero rows.
Refusals
The engine refuses to answer where the sample is thin. Indicative pricing is withheld below five comparable funded loans, and 828 of the current build's 10,568 comparable-loan matches carry no pricing for that reason. A lender profile needs three funded deals for a median and eight for quartiles, and 85 lender families currently clear that bar. No leverage figure is ever printed, because the filings carry no EBITDA, and if a section of a Lender Report cannot be filled from sourced rows, no report is produced.

Every number carries its limits.

Filings show principal, cost, fair value and rate. They do not show EBITDA, leverage or covenants. They arrive about 45 days after quarter end, so the freshest data is 45 to 135 days old, and an extension may already have been agreed. BDCs are one part of private credit: banks, private placements and most club deals do not appear, so a company missing from the data is simply a company we cannot see. Entity resolution is probabilistic and can be wrong.

These limits are printed in every report we send. Stating what the data cannot say is part of the product, and the anatomy below is the published specification that makes it checkable. Every Lender Report we deliver has these six sections, in this order, whatever the mandate.

Anatomy of a Lender Report
1. Situation
Borrower, tranche, principal, maturity date, incumbent lender family.
2. Evidence
The filings the picture is drawn from, each with its filing date.
3. Comparables
Funded loans of similar size and sector. The sample size is printed before the range.
4. Lenders
Each lender named, with the reason it is on the list next to the name.
5. Limits
What the data cannot show for this situation, and how old it is. This section is never omitted.
6. The date
What the maturity forces, by when and the date the picture should be reconfirmed.

Before any engagement, we will build a sample Lender Report for a single situation entirely from real public filings, with a filing date and a source against every number, so you can read the document itself before you decide anything about the firm.

Request a sample reportView a sample

Coverage is deepest in the US, and the method travels.

Mandates run in more than one market. The engine's deepest coverage is the US private credit market, where lenders disclose their books to the SEC each quarter and the maturity dates can be read directly. Other jurisdictions disclose less, so coverage there is built from what is filed: statutory accounts, company registries and listed-debt disclosures, supplemented by direct outreach against written criteria where the public record covers less.

Wherever an opportunity comes from, the sourcing and the reasoning are shown on the page, including how much of the picture the local record actually supports.

Every mandate starts with the read already done.

The filings are parsed, the borrowers resolved and the ranking rebuilt whether or not a client has called, and the lender profiles are drawn from filings published years before any particular engagement. A mandate inherits that work on the first day.

For owners

You inherit your own situation in writing: the tranche, the filed maturity date, the lender family holding it, comparable funded loans in your size and sector, and the lenders whose books show they write this kind of loan. The first meeting starts from that document.

Debt Advisory

For sponsors and funds

You inherit a queue built against your written criteria: companies that fit your criteria, each with a dated decision in the next six to twenty-four months, each name carrying the filed maturity that dates it. Approaches begin while the owner is still deciding what to do about the date. Where you are building a platform by acquisition, the queue is held per platform and refills continuously, so add-on sourcing does not stop between deals.

Buy-Side M&A