Best AI Tools for Private Credit

The best AI tool for private credit in 2026 is Kepler, because it reads the documents lenders run on (credit agreements and amendments, compliance certificates, monthly financials and lender presentations), computes covenant headroom in code on each agreement’s own definitions, and warns you when a borrower drifts toward a covenant. Your whole book lives in one model, with every input cited to its page.

This list is for direct lenders, BDCs, private credit funds, asset-based finance teams and the credit arms of multi-strategy managers. It covers underwriting, borrower monitoring, covenant tracking and portfolio surveillance, and leaves out loan administration and fund accounting.

Adoption is real but uneven. In an Apex Group survey of 105 senior private credit leaders, 36% said they use AI to extract information from credit agreements and financial documents, and 4% use AI models to value positions automatically (October 2026). In an Allvue and Crisil Coalition Greenwich survey of 102 private markets leaders, 59% named accuracy or reliability as a barrier to AI, and 8% rated their data maturity as high (February 2026).

How we picked

• Document reading: does it read every page of a credit agreement, certificate and financial package, scans and exhibits included?
• Borrower numbers you can trace: does each figure open the page it came from, with the period and units?
• Covenant logic: can it apply each agreement’s own definitions, as amended, and show the arithmetic? Our guide to reading a credit agreement with AI walks through the definitions that matter.
• Monitoring: does it watch the book and alert you when something moves?
• Controls: permissions, audit logs, information barriers, and a clear commitment that your documents won’t train shared models.

We build Kepler, so it sits at number one with a disclosure. Everything we say about other vendors is drawn from their websites, announcements and press coverage as of November 2026, and none of the specialist vendors publishes a price list.

Quick comparison

Tool	Best for	What it covers	How it shows sources
Kepler	Your whole book in one model, covenant headroom computed in code	Agreements, certificates, borrower financials, public filings	Exact page, line or cell; formula for every ratio
Octus	Credit intelligence and covenant data	News, deal documents, 170+ covenant fields per instrument	Responses link to source documents (per Octus)
Chronograph	Portfolio monitoring for credit funds	Borrower financials, certificates, covenant terms	Figures traceable to source documents (per Chronograph)
Hebbia	Credit agreement analysis across many deals	Covenant terms, baskets and carve-outs	Source-cited tables
V7 Go	Spreading financials and extracting agreement terms	Statements, covenants, borrowing bases	Visual citations on the page
9fin	Leveraged finance and private credit intelligence	Covenants, cap tables, financials, news	Cited answers
Lumonic	Covenant monitoring from certificates	Compliance and borrowing base certificates	Each figure traced to its cell (per Lumonic)
73 Strings	Monitoring and valuation for credit portfolios	Borrower health, covenant performance, valuations	Not detailed on its credit page
Allvue	Firms already on Allvue’s credit front office	KPIs, covenant headroom, deal benchmarks	Not detailed publicly
Cardo AI	Asset-based finance data and surveillance	Loan data, deal structures, automated controls	Automated data controls
Claude / ChatGPT for Financial Services	A frontier model with credit connectors	Moody’s, Octus and Chronograph connectors	Links to source data

The best AI tools for private credit

1. Kepler (our pick)

We build Kepler.

Best for: direct lenders, BDCs, private credit funds and credit teams at multi-strategy managers that underwrite and monitor borrowers from documents.

In private credit the numbers arrive as PDFs. Each borrower reports in its own format, and the definitions that decide whether a covenant holds sit in a credit agreement that can run to hundreds of pages before the amendments. A general model can summarize a compliance certificate. It can’t show you, with sources, whether consolidated EBITDA as the third amendment defines it supports the leverage ratio the borrower reported.

Our engineers build your firm’s ontology around the book. Every borrower, facility, tranche, sponsor, guarantor, agent and amendment resolves to one record, and each agreement’s defined terms (consolidated EBITDA with its add-backs and caps, the leverage and coverage tests, the step-downs, the equity cure) are captured with the clause that sets them. Extraction models we train read every page of every certificate, financial package and agreement as it arrives, scanned or native, and file each figure against the right borrower and test period.

Code computes covenant headroom. Reported EBITDA, each add-back against its cap, the debt figure and the ratio run as code over cited inputs, and the result opens to its formula with every input linked to its page. When the borrower’s reported ratio and the recomputed one differ, Kepler shows both and flags the difference. Agents watch the book on schedules and events: a certificate that hasn’t arrived, a margin that slipped two months running, an add-back near its cap, news on the sponsor. Alerts reach Slack or email with the exact quote, and the credit memo and quarterly portfolio review draft in your house format, from templates your team saves once. Behind the scenes, extraction runs on compact models we train for credit documents, frontier models take the question and draft the write-up, and we change models whenever a better one ships, so your team never has to. Our engineers built this kind of data layer at Palantir, and they deploy on site with your credit team, connecting your book and building its ontology in the first week.

Why it’s our pick for private credit

• Borrower financials read from every certificate and package, each figure cited to its page and period.
• Each credit agreement’s own definitions, as amended, applied in code borrower by borrower.
• Covenant headroom that reproduces: the same inputs give the same ratio, with the formula shown.
• Early warnings from agents that run overnight and on every new document, acting as the person who set them up.
• Public data loaded on day one, including 26M+ SEC filings, for public borrowers and comparables.
• Kepler where credit teams work: covenant tables in Excel, the credit memo in Word, answers in Claude, ChatGPT and Slack, and your loan systems through the Kepler API.
• One permission check on every read, every run logged, and your data never trains models other firms use. We hold SOC 2 Type II.

Questions credit teams ask Kepler

• “Which borrowers have less than 15% headroom on their leverage covenant this quarter, using each agreement’s own EBITDA definition?”
• “Show every add-back in the Q3 compliance certificates that exceeds its cap under the credit agreement.”
• “Alert me when any sponsor-backed borrower’s trailing twelve-month revenue falls two months in a row.”

“I can’t put a number in front of a client if I can’t show where it came from.” (Managing Director, multi-billion dollar fund)

Not the right fit if: your main job is trading syndicated loans and bonds off market news, or you’re shopping for loan administration and fund accounting.

Try Kepler on public borrowers at app.kepler.ai, or book a demo to see it on your book.

2. Octus (CreditAI)

Best for: credit investors who want news, documents and covenant data on syndicated and private deals in one place.

Reorg renamed itself Octus in October 2024, and on October 22, 2026 LSEG agreed to buy it from Permira-advised funds and management, with closing expected in the first half of 2027. Its unified CreditAI (March 2026) answers across Octus’s public content, permissioned data-room documents, covenant content and structured data such as capital structures, for both broadly syndicated loans and private credit agreements, and Octus says every response links to source documents. Deal Term Analytics and CovenantAI (October 2025) cover 170+ fields per instrument and compare clauses. An MCP connector brings Octus into Claude, ChatGPT and Perplexity for an added fee.

How Kepler differs: Octus is a credit intelligence library about the market. We model your own book, borrower by borrower, and Kepler computes your covenant tests in code.

3. Chronograph

Best for: credit funds that want a dedicated portfolio monitoring system with covenant tracking.

Chronograph’s private credit page describes collecting compliance certificates from PDFs and Excel without rigid templates, keeping borrower financials and covenant terms together with every figure traceable to source documentation, and checking adjusted EBITDA add-backs against negotiated caps. Data reaches Excel through its xConnect add-in, a GraphQL API, and apps in ChatGPT and Claude.

How Kepler differs: Chronograph is a monitoring system of record. We read every document behind it, and Kepler computes headroom on each agreement’s definitions and runs agents across the book.

4. Hebbia

Best for: analyzing and benchmarking terms across many credit agreements at once.

Hebbia’s private credit write-up lists four uses: an IC memo library, screening new deals with a first-pass memo, spotting refinancing signals, and extracting and benchmarking covenant terms, baskets and carve-outs across agreements, delivered as source-cited tables in Matrix. Credit data arrives through integrations with Moody’s, Fitch Solutions and Octus (a partnership announced in October 2026), plus PitchBook, Preqin, Datasite and Intralinks. Hebbia says customer documents are never used to train models.

How Kepler differs: Matrix runs a model over the agreements you select for each question. We read the whole book once and keep each agreement’s terms in your ontology, and Kepler applies them in code every quarter. See Hebbia alternatives.

5. V7 Go

Best for: credit analysts spreading financial statements and extracting agreement terms.

V7’s private credit page covers extracting borrower financials, covenant terms and collateral details for underwriting, then monitoring borrower reporting against covenants, with every extracted data point cited to its source. Its financial statement spreading agent handles documents up to 200 pages with visual citations, and its loan and credit agreement agent pulls financial covenants, borrowing base calculations, default events, amendments and waivers. V7 has no public price list.

How Kepler differs: V7 extracts terms document by document. Kepler connects them, so a definition from the third amendment is the one applied to next quarter’s certificate.

6. 9fin

Best for: leveraged finance, distressed and private credit teams that want market intelligence with AI on top.

9fin covers leveraged finance, distressed, private credit, asset-based finance and CLOs. In June 2026 it launched AI Chat and Research Grid: AI Chat gives cited answers to credit questions drawing on covenant, cap table, financial, filing and news data, and Research Grid builds structured comparisons across companies.

How Kepler differs: 9fin covers the market’s deals. Kepler covers your book, with your own documents, definitions and calculations.

7. Lumonic (PitchBook)

Best for: lenders that want covenant tracking built from compliance and borrowing base certificates.

Morningstar acquired Lumonic in March 2025, and it now runs as a PitchBook subsidiary. Lumonic describes itself as an AI-native portfolio monitoring platform for private credit: it ingests compliance and borrowing base certificates, tracks covenants with awareness of amendments, and traces every figure back to the cell it came from.

How Kepler differs: Lumonic is a monitoring point tool. Kepler puts monitoring on the one model of the firm that runs underwriting, research and reporting, with agents and every surface your team uses. For more monitoring options, see the best AI portfolio monitoring tools.

8. 73 Strings

Best for: credit managers that want monitoring and credit valuations from one vendor.

73 Strings’ private credit offering uses 73 Extract for data extraction (which it says is up to 99% accurate), 73 Monitor for credit risk, covenant performance and borrower health, and 73 Value for credit valuations. Its September 2026 acquisition of Callisto, a Paris agentic-AI firm working with private equity and private credit clients, adds agent capabilities and MCP access.

How Kepler differs: 73 Strings centers on monitoring outputs and valuations. Kepler starts from the documents and your definitions, so every monitored figure opens to its source and formula.

9. Allvue

Best for: credit managers already running Allvue’s front office.

Allvue added Document IQ, AI extraction with human review, in January 2026, and in July 2026 launched OneVue, with Portfolio Intelligence (KPI monitoring, covenant headroom and AI-assisted analysis), Deal Analytics benchmarks built from private credit deal data, a conversational assistant called Andi and MCP connectivity. OneVue is available to select clients on Allvue’s Credit Front Office.

How Kepler differs: Allvue’s AI sits inside its own system of record. Kepler works across all of a firm’s systems and documents.

10. Cardo AI

Best for: asset-based finance and structured credit teams that need clean loan-level data and surveillance.

Cardo AI calls itself the operating system for asset-based finance, with a Loan Data Agent, a multi-agent workflow engine, deal surveillance and a deal data audit tool, across 15+ structure types and 30+ asset classes with 600+ automated controls. It exports to Snowflake and Databricks.

How Kepler differs: Cardo specializes in loan tapes and structures. Kepler covers the documents and decisions around them, from the credit agreement to the IC memo.

11. Claude and ChatGPT for Financial Services

Best for: credit teams that want a frontier assistant with credit data connected.

Anthropic’s Claude for Financial Services added a Moody’s connector in October 2025 with credit ratings, research and company data, and its financial services page lists agent templates for credit memos. OpenAI’s ChatGPT for Financial Services, launched in September 2026, includes SEC filings and transcripts with 50+ connectors. Both can reach Octus through its MCP connector, and Chronograph offers apps in each. Kepler runs inside both.

How Kepler differs: a frontier model on its own writes ratios itself and learns your agreements only from what you paste in. With Kepler in the same window, each agreement’s definitions are applied in code and every input is cited.

Also worth a look

• AlphaSense: has no dedicated credit product listed on its site, but credit analysts use its library and its SuperAnalyst agent for company and industry research. See AlphaSense alternatives.

How to choose

1. Map the job. Market intelligence on syndicated and distressed credits is a different job from monitoring a book of private borrowers. Many firms need both.
2. Test covenant math on a real borrower. Give the tool a credit agreement with two amendments and a quarter of compliance certificates, and ask for debt-to-EBITDA headroom on the agreement’s own EBITDA definition. Check every add-back and cap against the clause.
3. Count what it reads. Ask for every borrower whose agreement has an equity cure, or every certificate with an add-back above its cap, and compare with what you know is there.
4. Click the numbers. Each figure should open its page with the period visible. Our guide on checking AI-generated numbers lists the tests.
5. Set an alert. Put a trigger on a real borrower metric and see what arrives, and whether it quotes the source.
6. Check the walls. The tool should respect information barriers and log every query.

For the equity side of the same deals, see the best AI tools for private equity due diligence, and for the wider buy-side field, the best AI tools for investment research. How Kepler’s approach differs from general AI tools is laid out on Compare and the platform page.

Request a demo and we’ll run Kepler on a borrower file like yours, or start at app.kepler.ai.

Frequently asked questions

What is the best AI tool for private credit?

Kepler is our pick for lenders that want their whole book in one model. Borrower financials are read from every certificate and package, covenant headroom is computed in code on each agreement's own definitions as amended, every input is cited to its page, and agents flag a borrower drifting toward a covenant. We build Kepler.

Can AI read compliance certificates and track covenants?

Yes. Several tools now extract figures from compliance certificates and borrowing base certificates and compare them with covenant levels. The harder part is applying each credit agreement's own definition of EBITDA, with its add-backs, caps and amendments, and showing the arithmetic. Ask any vendor to recompute one borrower's debt-to-EBITDA ratio from the agreement and certificate, with every input cited to its page.

How are private credit firms using AI?

Mostly for document work: extracting borrower financials, analyzing credit agreements, drafting credit memos, and monitoring portfolios for covenant and performance changes. In an Apex Group survey published in October 2026, 36% of private credit leaders said they use AI to extract information from credit agreements and financial documents, and only 4% use AI models to value positions automatically.

Can ChatGPT or Claude analyze a credit agreement?

Yes, for a first read: either will summarize an agreement and answer questions about its terms, and both connect to credit data: Claude to Moody's, and both to Octus through its MCP connector. On its own, a frontier model can misread a defined term across amendments and writes ratios itself. Kepler runs inside both, adding each agreement's definitions applied in code and every input cited to its page.

What should a private credit AI tool cover on security?

A SOC 2 Type II report (we hold one), a written commitment that your data won't train models other firms use, permissions that mirror your firm's, and an audit log of every query. Credit teams often hold material non-public information, so the tool should also respect information barriers between public and private sides. Kepler checks permissions on every read by a person or an agent and logs every query and calculation.