LatestAll news

Home / Use cases / PCT-010

Underwrite · PCT-010 · Sourced card

Scan agreements for LME trapdoors

Scans every credit agreement in a book for liability-management trapdoors (unrestricted-subsidiary transfers, non-pro-rata uptiers, guarantor releases) and lists the blockers each one has.

Direct lendingSpecial sits / distressedPublic dataVendor + in-house
INPUTCredit agreements across the bookAI STEPSExtract key clauses · Check each blocker· Rank the bookOUTPUTBlocker matrix ranked by gapHUMAN CHECKLegal interpretation

Today vs automated

TODAY
  1. Open 40 agreementsAssociate · manual
  2. Search for each blockerAssociate · manual
  3. Log results in a sheetVP / counsel · manual, re-keying
  4. Email / meeting hand-offAssociate → VP / counsel
AUTOMATED
  1. A new or amended credit agreement is loadedTrigger: starts on its own
  2. Extract key clausesAutomated · Document parser
  3. Check each blockerAutomated · LLM
  4. Rank the bookAI draft, human review · Model template
  5. Legal interpretationApprove / override · VP / counsel

AutomatedAI draft, human reviewHuman onlyTools: Data room, Document parser, LLM, Model template

Estimated valueestimate

80–1,100 analyst hours a year
$12k–$275k of time, vs an assumed $20k–$100k tool cost

Net: −$88k to $255k a year net of tool cost; at the low end of the assumptions it does not cover the tool cost.

Manual3–6 h per agreementWith AI0.5–1 h per agreement, review included
Assumptions
  • 3–6 analyst hours per agreement today; 0.5–1 hours with an agent, including review
  • 40–200 agreements a year for one team
  • $150–250 per hour loaded cost
  • Tool or build cost $20,000–100,000 a year (both)

Estimate, based on the assumptions shown; your numbers will differ. How we estimate

What else it can do for the team

  • Re-run the matrix after each amendment
  • Compare a new term sheet's protections with the book's norm
  • List positions where a non-pro-rata buyback is permitted
  • Draft the asks for the next amendment negotiation
  • Feed results into the watchlist for names under stress
How you get thereFour steps from prompting to an agentic workflow; most teams start at step 4.
1 · PromptingAn analyst asks a chat tool about one agreement.
Needs: an approved AI tool, a confidentiality policy.
Risk: unchecked answers, data pasted outside policy.
2 · WorkflowA fixed pipeline extracts the same fields into your template every time.
Needs: field definitions, a template, document parsing.
Risk: breaks silently when formats change.
3 · AgentIt watches the inbox or data room, runs the checks and drafts the output.
Needs: inbox/doc-store access, system write-back, logging.
Risk: acting on a misread; needs review gates.
The automated lane above
4 · Agentic workflowSeveral agents work across the deal or book; people approve exceptions.
Needs: shared data model, permissions, audit trail, exception queue.
Risk: errors compound across steps; needs monitoring.
Typical starting point
Solution optionsBuy (10 vendors listed), build or hybrid, with the trade-offs of each.
Buy
Hebbia, Ontra, 9fin, Octus, Indus Valley Partners (IVP for Credit), Blueflame AI, Alphastream, CovenantIQ, CredCore, CreditBase
Fastest start; you adapt to the product's data model and pricing.
Build
An LLM API, a document parser, a vector or document store, your data warehouse, and a review queue.
Fits your definitions exactly; you own maintenance and controls.
Hybrid
Vendor extraction feeding your own checks, templates and systems.
Common middle path; two things to integrate and govern.

Listing is not endorsement.

What changes for the team3–6 h → 0.5–1 h per agreement (estimate); coverage goes from the sections someone had time to read to every clause and schedule checked against the term sheet.
ROLESAssociates move from reading and retyping to checking cited exceptions.
CYCLE TIME3–6 h → 0.5–1 h per agreement (estimate)
COVERAGEFrom the sections someone had time to read to every clause and schedule checked against the term sheet.
CONTROLSEvery output cites its source; approvals and overrides are logged.
How it works

Full title: Lender-on-lender violence radar: scan every credit agreement for J.Crew, Serta, and Chewy trapdoors

“Three filed credit agreements. One has no Serta blocker. Here is the clause.”

Scenario

SAMPLE: A fund holds 40 names. After a peer was primed in a non-pro-rata uptier, the PM asks which of the fund's own agreements are open to the same move.

What the human still decides

Legal interpretation. The scan flags language, and counsel decides what it means.

Why it matters

Liability management risk and the spread of blocker provisions are a 2026 documentation trend.

What the record shows

Checked 2026-10-15.

A real scan of Viavi's October 2025 term loan found J.Crew blockers (no Material IP into unrestricted subs), a Serta-style subordination consent with an exception if every affected lender is offered its pro rata share, a Chewy guarantor-release fix, and non-pro-rata open-market purchases and Dutch auctions permitted. That last one is the route used in some uptiers. 'Material IP' is not quantified, so the blocker is narrower than the label.

Try it: prompt and data
For this credit agreement, state whether it contains (a) a J.Crew blocker, (b) a Serta blocker, (c) a Chewy blocker. Quote the operative language and section, or say 'not found'. Do not give a legal opinion.

Data

Public inputs only (SEC/EDGAR filings, published methodologies, public signals). Every figure is cited.

Who uses it, vendors and build
Segments
Direct lending, Special sits / distressed
Who uses it
Associate, Special sits analyst, Counsel
Build
Vendor + in-house
In-house
Clause extraction with a checklist + counsel-reviewed test set. Estimate: 2–4 weeks; needs legal review of the eval set.
Last verified
2026-10-15

Vendors that claim this capability

Tags reflect each vendor's own public claims at the linked page (checked 2026-10-15). Not tested, ranked or endorsed.

Sources and related
In the news.

Part of the deal lifecycle: stage 4 of 7, Documents.

Next step

Get new walk-throughs

Subscribe to the newsletter

Not investment advice. Names marked SAMPLE are fictional. Found an error? See the corrections log.