Underwrite · PCT-068 · Sourced card

Price an LP fund stake

A secondaries buyer or selling LP wants to triangulate a bid for a private credit fund interest against the published H1 2026 surveys and the fund's own loan marks.

Credit secondariesPublic dataVendor + in-house

Today vs automated

TODAY

Read adviser surveysAssociate · manual
Map the fund bookAssociate · manual
Triangulate the bidVP / counsel · manual
Email / meeting hand-offAssociate → VP / counsel

AUTOMATED

A fund stake is offered for saleTrigger: starts on its own
Collect survey pricingAutomated · Document parser
Check marks vs publicAutomated · LLM
Place the bidAI draft, human review · Model template
The bidApprove / override · VP / counsel

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

Estimated valueestimate

30–360 analyst hours a year
$4k–$90k of time, vs an assumed $20k–$100k tool cost

Net: −$96k to $70k a year net of tool cost; at the low end of the assumptions it does not cover the tool cost.

Manual10–20 h per stakeWith AI2–5 h per stake, review included

Assumptions

10–20 analyst hours per stake today; 2–5 hours with an agent, including review
6–20 stakes 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

Check fund marks against public peers
Draft the bid memo
Track pricing surveys over time
Compare stakes across managers
Log bids and results

How you get thereFour steps from prompting to an agentic workflow; most teams start at step 2.

1 · PromptingAn analyst asks a chat tool about one stake.
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.
Typical starting point

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.

Solution optionsBuy (6 vendors listed), build or hybrid, with the trade-offs of each.

Buy
The LoanEdge, Chronograph, Cobalt, 73 Strings, Oxane Partners, WhaleBlue
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 team10–20 h → 2–5 h per stake (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 TIME10–20 h → 2–5 h per stake (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: Pricing a private credit LP stake: reconcile the bid to the advisers' surveys and the underlying marks

“Jefferies says credit LP stakes got cheaper in H1. Campbell Lutyens says they got dearer. Both are right.”

Scenario

Public: a secondaries buyer or selling LP wants to triangulate a bid for a private credit fund interest against the published H1 2026 surveys and the fund's own loan marks.

What the human still decides

The bid. Surveys disagree on direction, so the agent shows the range; it doesn't pick a number.

Why it matters

H1 2026 surveys disagree: Jefferies has credit LP pricing down to 89% of NAV from 91%, while Campbell Lutyens has the private credit discount narrowing to 5.1%. Evergreen buyers paid 92.1% of NAV on LP portfolios vs 86.7% for the market.

What the record shows

Checked 2026-10-16.

The H1 2026 surveys point in opposite directions on credit. Jefferies: credit LP pricing fell to 89% of NAV from 91% at year-end 2025, with demand for software-light portfolios. Campbell Lutyens: the private credit LP discount was 5.1%, among the tightest of any strategy, and evergreen buyers paid 92.1% of NAV on LP portfolios against 86.7% for the market overall. Evercore has GP-led credit deals at about 99% of fair market value. Definitions, deal type and buyer mix explain much of the gap, so a bid can only be judged against the survey whose definition matches it.

Try it: prompt and data

For {fund interest}, tabulate H1 2026 credit secondary pricing from Jefferies, Campbell Lutyens and Evercore with each one's definition. Then place {bid} against each, adjusting for senior/junior mix and software share. Cite every figure.