Legal Due Diligence in Private Credit: What AI Actually Automates

A rapidly growing document volume

The global private credit market has reached $3.5 trillion in assets under management, according to the Financing the Economy 2025 report published by the Alternative Credit Council (ACC) and Houlihan Lokey. Capital deployment surged 78% between 2023 and 2024, reaching $592.8 billion in 2024 alone. This acceleration mechanically translates into a multiplication of due diligence files for funds' in-house legal teams to process.

Each private debt transaction — whether a unitranche, second lien or revolving credit facility — generates a substantial document set: confidentiality agreements, term sheets, credit agreements, guarantees, intercreditor agreements, legal opinions, and KYC/AML compliance documents. Proskauer's 15th annual Private Credit Insights report, published in February 2026, analyzed over 450 transactions representing $123.6 billion in total value for 2025 alone. With 82% of lenders anticipating increased deal activity in 2026 according to the Proskauer Private Credit Survey 2026, pressure on due diligence teams is unlikely to ease.

What legal due diligence actually involves

It helps to break down the legal due diligence process in private credit to understand what AI can — and cannot — handle. The work broadly falls into four distinct phases.

The first phase is document collection and organization. Documents arrive via data rooms, email, sometimes physical mail. They come in varying formats (PDF, Word, scans), in different languages, with inconsistent naming conventions. Before reading anything, teams must sort, classify, and index.

The second phase is reading and extraction. The lawyer reviews each document to extract key information: parties, dates, amounts, conditions precedent, prepayment clauses, events of default, financial and operational covenants, waiver mechanisms. This step is the most time-consuming and repetitive.

The third phase is comparison and verification. Extracted terms are compared against the initial term sheet, the fund's standards, negotiated precedents, and applicable regulatory requirements. This is where discrepancies, unusual clauses, or potential risks are identified.

Finally, the fourth phase is analysis and recommendation. The lawyer formulates an opinion on identified risks, proposes amendments, and participates in negotiation. This last step is inherently human: it requires judgment, contextual knowledge, and an understanding of the fund's commercial interests.

Where AI delivers measurable gains

Large language models (LLMs) and contract intelligence tools are now mature enough to automate a significant portion of the first two phases — collection and extraction — and to assist with the third phase of comparison.

In document classification, current models reliably identify document types (NDA, credit agreement, security agreement, intercreditor agreement) based on content, regardless of file naming. This enables automatic structuring of a disorganized data room and flagging of missing documents against a predefined checklist.

Clause and data extraction is where gains are most tangible. A survey conducted by State Street among nearly 500 institutional executives in Q1 2025 found that 77% of North American respondents were using or planning to use LLMs to process unstructured data related to their private markets investments. The reason is straightforward: most information relevant to due diligence — default clauses, waterfall mechanisms, prepayment conditions — is buried in text documents that traditional systems cannot exploit.

Specialized platforms such as Kira (Litera), Luminance, and Spellbook can automatically extract key terms from a credit agreement: interest rates, maturity, financial covenant thresholds (leverage ratio, DSCR, FCCR), material adverse change conditions, and cure mechanisms. According to Litera, the Kira platform achieves extraction accuracy above 90% and can reduce contract review time by approximately 50% on standardized extraction tasks.

In document comparison, AI can cross-reference the terms of a final credit agreement with those of the previously negotiated term sheet and automatically flag divergences. It can also compare a contract against a fund's internal template or a set of precedents, quickly spotting clauses that deviate from the lender's usual positions.

Limitations that should not be underestimated

Enthusiasm around AI in legal due diligence deserves to be tempered by a clear understanding of its current limitations.

The first limitation is context sensitivity. An LLM can identify that a material adverse change clause exists in a contract, but it cannot judge whether its wording is unusually broad or restrictive compared to current market practice in a given sector. This assessment requires sector-specific expertise that general-purpose models do not natively possess. The most advanced tools partially address this by relying on annotated clause databases, but the final judgment remains human.

The second limitation concerns cross-clause interactions. A credit agreement is an integrated legal system: the prepayment clause interacts with events of default, which interact with cure mechanisms, which depend on financial covenant definitions. AI excels at clause-by-clause analysis but still struggles to evaluate the combined effects of multiple interacting provisions.

The third limitation relates to output reliability. Hallucinations remain a real risk with LLMs. In a due diligence context where an error can have significant financial consequences, every automated extraction must be verified by a human. AI does not eliminate verification work — it shifts the lawyer's role from exhaustive reading to targeted quality control.

Legal due diligence is not an intelligence problem — it is first and foremost a problem of volume and time. AI handles the volume. The lawyer retains the judgment.

Structuring adoption: where to start

Integrating AI into an existing due diligence process does not happen by switching the entire workflow overnight. Funds that succeed in this transition proceed incrementally, initially targeting high-volume, low-analytical-complexity tasks.

The most natural entry point is standardized extraction. Configuring a tool to systematically extract the 20 to 30 key fields from a credit agreement (amount, rate, maturity, financial covenants, events of default, prepayment conditions) generates an automatic summary sheet for each incoming deal. The lawyer no longer starts from a blank page: they verify and complete a pre-populated analysis.

The next step is automated comparison between term sheets and final contracts. This reconciliation work, which can take several hours manually on a complex contract, is reduced to a gap report generated in minutes. The lawyer's attention then focuses on identified divergences rather than a full reading of both documents.

Finally, capitalizing on institutional memory represents a lasting competitive advantage. By progressively feeding the tool with the fund's negotiated positions, internal standards, and precedents, teams build a comparison base that grows richer with each transaction. Tools such as MyClauze, Kira, and Luminance support this incremental approach, where the tool's value increases with the fund's historical data volume.

The confidentiality question

Data security is particularly sensitive in private credit. Due diligence documents contain highly confidential information about borrowers, financing structures, and commercial terms. Adopting an AI tool requires answering several questions: where is the data stored? Is it used to train the model? Is client isolation (multi-tenancy) guaranteed at the infrastructure level?

Public cloud-hosted solutions pose an additional challenge for funds subject to strict regulatory obligations. Some managers prefer on-premise deployments or private cloud environments. Others accept public cloud provided the vendor contractually guarantees no training on client data and end-to-end encryption.

This confidentiality criterion is often the decisive factor in tool selection, more so than extraction quality itself. A performant tool that is opaque about data handling will not be adopted by a serious private debt fund.

Perspective: a tool, not a silver bullet

AI applied to legal due diligence in private credit does not eliminate the need for competent lawyers. It frees them from mechanical tasks — reading, extracting, comparing — so they can devote more time to what makes a lender truly valuable: assessing risks, negotiating protections, and structuring the fund's positions.

With a private credit market that continues to grow and legal teams whose headcount does not scale at the same pace, automating the mechanical layer of due diligence is not a technological luxury. It is a pragmatic response to a capacity problem. Funds that implement these tools today do not become smarter — they become faster, and they reallocate their teams' time toward the decisions that matter.