Private credit term sheets: where standardization ends and negotiation begins

A market built on customization

Private credit was sold to investors and borrowers as the flexible alternative to syndicated markets. Where leveraged loans follow templates polished over decades and where the negotiation margin is narrow, direct lending was supposed to deliver structures tailored to each borrower's profile. Funds can underwrite case by case, propose non-standard loan structures, build flexible covenants and make relationship-driven decisions — for instance aligning covenant tests with the seasonality of a business, as Morgan Stanley notes in its 2026 analysis of the evolution of direct lending.

This flexibility carries an operational cost. For a fund that reviews 200 deals per year, every term sheet must be read, compared mentally against a hazy reference market, and negotiated on dozens of points. For the law firms that draft the final documentation, every deal starts from a base close to the previous one but slightly different. The result is a market where conventions are largely implicit, transmitted through the experience of practitioners rather than codified in public documents.

What is standardizing — and why

Pressure toward standardization comes from several sources. On the lender side, the diversification of capital sources — funds, BDCs, insurance partners — demands documentation that is more readable and more comparable across deals. On the sponsor and borrower side, the arrival of specialized law firms and the rapid circulation of precedents between transactions pull toward common formulations. On the secondary market side, the prospect of increased liquidity on private credit positions presupposes terms that are more predictable from one deal to the next.

The most structurally significant initiative of 2025 is the publication by the Loan Syndications and Trading Association of an Exposure Draft of Model Credit Agreement Provisions for Private Corporate Credit Deals (PCC MCAPs), dated November 26, 2025. These model provisions target a precise segment of the market: senior secured facilities provided by a handful or fewer direct lenders to a private equity-sponsored company with EBITDA between $15 million and $150 million. The document starts from the MCAPs already established for the LevFin market and adapts them to the specifics of club lending and direct lending. It is binding on no one, but acts as an anchor point: as practitioners reference it, deviations become signals during negotiation.

Beyond the MCAPs, several building blocks have already converged in practice. The mechanics of interest calculation — reference to a benchmark rate, applicable margins, floors, day-count conventions — have followed very stable conventions since the post-LIBOR transition. Change of Control definitions and voluntary prepayment mechanics still vary in their thresholds but are homogeneous in structure. Procedural clauses (notices, lender voting mechanics, transfers) converge toward common formulations that documentary review tools identify without difficulty.

What is still negotiated systematically

Standardization stops where economic value moves. Termgrid's analysis of 2025 trends and 2026 outlook identifies a stable core of terms that remain negotiated deal by deal: incremental debt capacity, Most Favored Nation clauses, their sunset provisions, and unsecured debt capacity. Open market purchases — the sponsor's ability to buy back its own loans on the market — also remain poorly stabilized and are arbitrated case by case, with broader adoption in larger deals.

Beyond these blocks, several customization zones weigh disproportionately on the risk profile of a position. The definition of adjusted EBITDA is the textbook case. There is no standard definition: each contract lists its own add-backs (expected synergies, non-recurring costs, pro forma adjustments) and its own exclusions. Two contracts with the same 6x maximum leverage ratio can in practice reflect very different constraints depending on the generosity of the permitted add-backs. Proskauer's 2025 private credit restructuring year in review recalls that the liability management exercises (LMEs) that multiplied in 2025 often rely on exploiting carve-outs and baskets whose precise wording — fixed at the term sheet stage — determines what is legally feasible years later.

Baskets themselves are an intense negotiation zone. Restricted payments, permitted investments, asset sales, additional debt: each basket category typically combines a fixed amount (hard cap), an amount expressed as a percentage of EBITDA (grower basket), and sometimes an amount rebuilt from cash flow (available amount, builder basket). Conditions of use, ratios to satisfy at the time of use, and interactions between baskets form a combinatorial graph that makes comparison between two contracts laborious by hand.

An LSTA note on reconciling flexibility and standardization reinforces this point: in direct lending, nothing ever looks quite like anything else, and that variability extends to how credits are drafted, processed and funded. MCAPs standardization addresses procedural and technical building blocks; economically sensitive terms remain the object of negotiation.

What AI can compare between term sheets

The gap between the standardization of technical building blocks and the customization of economic terms determines what automated analysis tools usefully do, and what they do not. On the standardized layer, AI reaches a high level of reliability: identifying that a Change of Control clause is present and matches a template formulation, verifying that voting mechanics follow the LSTA model, extracting the pricing grid with its margin steps based on leverage. These tasks are mechanical, repetitive, and typically account for 60-70% of the textual content of a term sheet.

On the negotiated layer, AI is useful but becomes an analytical aid rather than a substitute. Comparing two adjusted EBITDA definitions between two deals is now feasible in an automated way: a model can list the add-backs present in each definition, flag those that appear in one but not the other, and measure the gap between applied caps. What it does not do is judge whether that gap is acceptable given the fund's strategy, the borrower's profile or the dynamic of the ongoing negotiation. That synthesis remains an analyst's or counsel's work.

The most tangible contribution lies in detecting deviations from an internal reference. A fund that has already signed 30 or 50 deals effectively holds a private reference base: its own positions on maximum leverage, on MFN sunsets, on basket thresholds, on accepted EBITDA definitions. Comparing a new term sheet to this base quickly identifies the gaps to challenge in negotiation. Tools such as MyClauze, Ontra or Termgrid build this comparison layer from the contractual history specific to each fund, rather than imposing an external market benchmark whose relevance is by construction limited in private credit.

The value of AI in term sheet review is not to tell you what the market is — it is to tell you how this deal differs from what you usually sign.

The limits of the exercise

Several limits must be kept in mind by teams relying on term sheet analysis tools. The first is the evolving nature of the market. Dechert's annual private credit review notes that market conditions in 2025 saw spreads compress and competition intensify between lenders, which translated into a progressive softening of terms on certain segments. A reference base built on deals signed two years ago can overestimate the rigor of what is being negotiated today.

The second limit concerns the confusion between term sheet and final documentation. A term sheet sets out the main economic principles and lists the main protection points, but the full drafting takes place in the credit agreement, where precise definitions and cross-references between articles determine the real application of the terms. A term sheet that looks stricter on its face may, after full drafting, prove more permissive than expected if the underlying definitions contain generous carve-outs. Tools that analyze only the term sheet miss this layer of depth.

The third limit is the relational dimension. DLA Piper's insights from the Debtwire Private Credit Forum recall that in upper and middle market segments, the relationships between sponsors and a narrow core of private credit managers rest on multi-year track records. Certain terms are granted because they sit within a long-standing relationship, not because they represent a market standard. AI measures gaps; it does not measure the relational consideration.

How to integrate these tools into a review process

For a fund looking to tool up term sheet review, the most cost-effective approach is to separate three layers in the process. The first layer is verifying compliance with market technical conventions: presence and wording of procedural clauses, conformity with MCAPs (LevFin or PCC depending on the segment), pricing grid. This layer can be largely automated and handed to a tool as soon as the term sheet arrives.

The second layer is comparison against the fund's internal base: maximum leverage vs. portfolio average, structure of adjusted EBITDA vs. historical positions, MFN sunset vs. the fund's recent practice, basket thresholds and structure. This layer produces a gap sheet that becomes the starting point for the discussion between the investment team and counsel.

The third layer is qualitative analysis: assessment of credit profile, negotiation dynamic, sponsor context, prospect of a long-term relationship with the borrower. This layer remains human. Its quality depends on the time the teams can devote to it — time that is mechanically freed up when the first two layers are prepared by tools rather than rebuilt by hand for each deal.

The practical challenge is not to deploy a generic AI on the term sheet and expect a verdict. It is to build a chain where each layer does what it does best, and where human work concentrates where judgment and market knowledge make the difference.