Best AI Portfolio Monitoring Tools for Private Markets in 2026

The best AI portfolio monitoring tool for private markets reads every package as it arrives and computes each KPI on your firm’s own definitions. Our pick is Kepler: our engineers build your firm’s ontology, every reporting package, board deck and compliance certificate is read in full the day it lands, agents watch every company and borrower, and code computes and cites every number, from covenant headroom to the figures in the LP report.

This list is for private equity firms, growth and venture investors, private credit lenders and BDCs that monitor portfolio companies or borrowers after the deal closes. It covers collecting packages, KPIs on the firm’s definitions, covenant tracking, early warnings, valuations support and LP reporting. Allocators monitoring GPs should also see our list for LP and fund-of-funds due diligence.

We build Kepler, so it’s first and labeled. Facts about the other tools come from their websites and announcements as of November 2026, linked inline. None of the specialist vendors here publishes a price list.

How we picked

• Collection: does it read packages in whatever format companies send (PDFs, Excel, scans, board decks), or does it need a template?
• Your definitions: are KPIs and covenant tests computed the way your firm and each credit agreement define them?
• Traceable numbers: does each figure open the page or cell it came from, and do calculated figures show their formula?
• Early warnings: do agents watch for missed reports, slipping margins and add-backs near their caps, without someone asking?
• Reach: does monitoring connect to the deal model, diligence files, public peers and the LP report, or stop at a dashboard?
• Controls: permissions by fund and team, every query logged, and a written commitment that your data won’t train shared models.

Quick comparison

Tool	Best for	How data arrives	AI features	How it shows sources
Kepler	One model of the firm, every number computed and cited	Every package read in full from email, portals, SharePoint, data rooms	Ontology, agents on events, computed KPIs and covenant tests	Exact page, line or cell; formula for every calculation
Chronograph	A dedicated GP and LP monitoring system of record	Automated collection from companies; PDF and Excel intake for credit	ChronoAI, Semantic Search, Claude plugins, MCP	Document and page for MCP answers (per Chronograph)
iLEVEL (S&P Global)	GPs and LPs that want monitoring tied to S&P data and valuations	Automated Data Ingestion from PDFs and spreadsheets	iLEVEL Agent, Document Search, MCP	Annotations linking data points to sources (per S&P)
73 Strings	Monitoring tied to valuations	73 Extract across financial and operational KPIs	Agentic AI with human review; MCP planned	Traceable, audit-ready outputs (per 73 Strings)
Allvue	Credit teams on Allvue’s Credit Front Office	Data already in Allvue	Portfolio Intelligence, Andi, benchmarks	Not detailed publicly
Lumonic	Credit and equity funds that want monitoring run for them	Inbox and document intake, any format	Lu agent, restatement flags, MCP	Every number traced to its source (per Lumonic)
V7 Go	Extraction workflows from packages to LP decks	Extracted into its Context Graph as reports arrive	Agents, Context Graph, Slides	Source page cited
Hebbia	Searching and benchmarking terms across documents	Documents you load or connect	Matrix grids, libraries	Source links per cell
Claude / ChatGPT	An assistant over a monitoring system’s connector	MCP connectors from Chronograph, iLEVEL and others	Chat, skills, plugins	Links to connector sources

The best AI portfolio monitoring tools

1. Kepler (our pick)

We build Kepler.

Best for: PE firms, credit funds and BDCs that want every portfolio company and borrower in one model of the firm, with KPIs and covenant tests computed on their own definitions and every number traceable from the package to the LP letter.

Portfolio monitoring breaks in the same places at every firm. Packages arrive in forty layouts, some scanned, some late. The definitions that matter (the fund’s adjusted EBITDA, the credit agreement’s consolidated EBITDA as amended) live outside the monitoring system. A board deck and a monthly package disagree, and nobody notices until the valuation committee. And a frontier model asked to help writes numbers as text, with nothing to show which ones are wrong.

Our forward-deployed engineers, who built this layer before at Palantir, build your firm’s ontology from your deal folders, data rooms, inbox, CRM, models and monitoring history. Every company, borrower, facility, sponsor, fund and person resolves to one record; your KPI definitions and each agreement’s defined terms carry one meaning; and the model knows time, so it can answer as reported, as restated or as of any quarter-end. Extraction models we train read every page of every package as it arrives, scans and tables included, and file each figure against the right company and period with its value, scale and unit beside its source passage.

Code computes every KPI, margin, covenant ratio and valuation input from cited lines, and each result opens to its formula. When the borrower’s reported leverage ratio differs from the recomputed figure, or a board deck disagrees with a package, Kepler shows both and flags the gap. Agents run on schedules and events: a package that hasn’t arrived, a margin that slipped two months running, an add-back near its cap, a filing from a listed peer, news on a sponsor. They act as the person who set them up and never take instructions from what they read. The quarterly portfolio review, the valuation memo and the LP letter draft in your template from figures already computed, in Excel, PowerPoint, Word, Claude, ChatGPT, Slack or email, and your engineers can build on the same engine through our API. Public data comes loaded, 26M+ SEC filings and 40M+ IR documents and transcripts, so listed peers and listed BDCs sit beside private companies.

Why it’s our pick for portfolio monitoring

• No templates. Packages read in the company’s own format, from wherever they land.
• Your definitions, computed. Fund KPIs and each credit agreement’s terms, as amended, applied in code.
• Conflicts caught. Package vs board deck, reported vs recomputed, shown side by side.
• Agents you can leave alone. Missed reports, covenant drift and peer filings flagged with the exact quote.
• Diligence to LP report on one model. The definitions from the deal model are the ones you monitor.
• Built for your rules. One access check on every read, walls between funds and deals, every run logged, and we hold SOC 2 Type II (security).

Questions monitoring teams ask Kepler

• “Which portfolio companies are more than 10% below budget on adjusted EBITDA this quarter, on our definition, and what did management say about it?”
• “Leverage covenant headroom for every borrower in Fund IV, on each agreement’s own EBITDA definition, with every input cited.”
• “Tell me the day any company’s monthly package shows revenue down two months running.”

“Our analysts used to spend more time verifying AI outputs than generating them. With Kepler, every number is already traced to source.” (Senior Managing Director, Top-10 Global Asset Manager)

Not the right fit if: your portfolio is a handful of companies reporting on one standard template and the team needs only a quarterly dashboard.

See it on a package of your own at app.kepler.ai, or book a demo.

2. Chronograph

Best for: GPs and LPs that want a dedicated portfolio monitoring, valuation and reporting system of record.

Chronograph sells Chronograph GP, which automates portfolio company data collection, analytics, valuation, reporting and data warehousing, and Chronograph LP for allocators. Its private credit product collects compliance certificates and borrower financials from PDFs and Excel without rigid templates and checks adjusted EBITDA add-backs against negotiated caps. ChronoAI and Semantic Search, which reached GP clients on September 30, 2026, answer questions across board materials and credit agreements. In October it released Claude plugins that assemble quarterly review packs, flag companies off budget and attribute TVPI by company, and its MCP server also serves ChatGPT and Perplexity.

How Kepler differs: Chronograph answers well about the data collected into it. Kepler builds one model of the whole firm, from the deal model to public peers, and computes every figure in code. See Kepler vs Chronograph.

3. iLEVEL (S&P Global Market Intelligence)

Best for: GPs and LPs that want monitoring connected to S&P Global’s valuations, data and benchmarks.

iLEVEL, with more than 15 years in the market, is S&P Global Market Intelligence’s platform for private markets portfolio monitoring, valuations, reporting and data management. Automated Data Ingestion extracts data from PDFs and spreadsheets into iLEVEL with a validation step and click-to-data tracing, and Document Search answers plain-language questions across board decks and financials with annotations back to sources. On October 22, 2026, S&P added a native iLEVEL Agent, an MCP server that connects iLEVEL data to Copilot and Claude under existing permissions, covenant monitoring for private credit, and tighter links to its private market valuations service.

How Kepler differs: iLEVEL organizes the data collected into it, inside S&P’s ecosystem. Kepler reads every document the firm holds and computes each KPI on your own definitions.

4. 73 Strings

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

73 Strings describes itself as the data and valuation infrastructure layer for private markets. 73 Extract captures and harmonizes hundreds of financial and operational data points and KPIs, 73 Monitor tracks the portfolio across reporting cycles, and 73 Value produces deterministic, audit-ready valuations across equity and credit, with agentic AI and human validation across all three. On September 24, 2026, it acquired Callisto, a Paris AI firm, to make its platforms addressable by AI agents over MCP.

How Kepler differs: 73 Strings centers on getting from document to mark. Kepler carries the same model of the firm from diligence through monitoring to the LP letter, with every figure cited.

5. Allvue

Best for: private credit teams already running Allvue’s Credit Front Office.

Allvue launched Portfolio Intelligence and Deal Analytics on OneVue on July 20, 2026. Portfolio Intelligence is a workspace for KPI monitoring, covenant headroom, peer benchmarking and AI-assisted analysis built on data already in the Credit Front Office, with Andi, Allvue’s conversational AI, generating borrower snapshots and answering a defined set of questions. Deal Analytics benchmarks a firm’s deals against Allvue’s private credit dataset. OneVue also carries MCP connectivity and agentic workflows, and the release went first to a select group of credit clients.

How Kepler differs: Allvue’s AI works on data inside its own system. Kepler reads every document and system the firm holds and computes covenant tests on each agreement’s own definitions.

6. Lumonic

Best for: credit and equity funds that want an agent to run collection and extraction for them.

Morningstar acquired Lumonic in March 2025. Lumonic now calls itself the self-monitoring portfolio for credit and equity funds. Its agent, Lu, sets up a company’s reporting schedule and covenants from the credit agreement, chases late reporters, and files every document from the inbox against the right company and period. Statements, scans included, are extracted into the firm’s own fields, with restated values marked, and Lumonic offers MCP access alongside PitchBook’s.

How Kepler differs: Lumonic is a monitoring point tool. Kepler puts monitoring on the same model of the firm that runs diligence, research and reporting.

7. V7 Go

Best for: teams that want configurable extraction workflows from packages to LP decks.

V7 Go runs document workflows for private markets, insurance and real estate. Its Context Graph lists portfolio monitoring among its uses: extracting and structuring portfolio reporting as it arrives, flagging variances and tracking KPIs. V7 says a monitoring run across 30 quarterly reports can end in an LP reporting deck built with V7 Go Slides, and it prices by platform, users and pages processed.

How Kepler differs: V7’s Context Graph links the firm’s files and entities to their sources. Kepler adds the public record already read, your KPI definitions written into one ontology, and covenant tests computed in code on each agreement’s own terms every quarter. See Kepler vs V7 Go.

8. Hebbia

Best for: teams that want to search and benchmark terms across a large library of portfolio documents.

Hebbia’s investing page pitches company and portfolio libraries that a team can query in real time and helping credit teams track more covenants without adding headcount. Its Matrix grid runs the same questions across a stack of documents, such as every credit agreement in a fund, and Matrix 2.0 (August 2026) carries the answers into memos and decks with a review step.

How Kepler differs: Matrix answers questions over the documents you select. Kepler reads every package once and applies your definitions in code every quarter.

9. Claude and ChatGPT

Best for: teams that want to ask questions of their monitoring data in a general assistant.

Monitoring vendors now meet investors inside the assistants. Chronograph’s MCP server works with Claude, ChatGPT and Perplexity, S&P’s iLEVEL MCP server connects to Claude and Copilot, and Claude for Financial Services offers agent templates for credit memos and fund work. Kepler runs inside Claude and ChatGPT too.

How Kepler differs: an assistant on its own answers from whatever a connector returns and writes the numbers itself. With Kepler in the same window, every package is already read and every figure is computed and cited.

Also worth a look

• Arch and Canoe Intelligence (Bloomberg): document collection for allocators and family offices; see the best AI tools for family offices.
• Addepar: wealth and portfolio reporting with private fund look-through, for multi-family offices.
• FactSet Cobalt: FactSet’s portfolio monitoring platform. AI Doc Ingest, released in beta on February 4, 2026, extracts data from PDFs, Excel files and board decks into a client-defined data model and shows extracted metrics beside their source documents.

How to choose

1. Test with your messiest company. Send last quarter’s package from the company that reports in the worst format, scans included.
2. Recompute one covenant. Give the tool a credit agreement with two amendments and a compliance certificate, and check the EBITDA definition, each add-back and its cap.
3. Ask what changed. “What moved versus last quarter, and why?” should come back with figures and quotes, each cited.
4. Tie out the LP report. Every figure in a draft letter should trace to the package it came from.
5. Set an alert. Put a trigger on a real metric and see what arrives, and whether it quotes the source.
6. Check permissions. Fund and deal teams should see only what they’re entitled to, with every query logged.

For lenders specifically, see the best AI tools for private credit. For the reporting side, read how to automate LP reporting with AI, and for agents that run on their own, the best AI agents for finance.

Send Kepler a reporting package at app.kepler.ai, or book a demo and we’ll run last quarter’s portfolio through a model of your firm.

Frequently asked questions

What is the best AI portfolio monitoring tool for private equity?

Our pick is Kepler. Our engineers build your firm's ontology, so every portfolio company, borrower, fund and deal is one record carrying your own KPI definitions. Every reporting package, board deck and compliance certificate is read in full the day it arrives, agents watch every company and flag what moved, and code computes and cites every number, from covenant headroom to the figures in the LP report. We build Kepler.

What does portfolio monitoring software do?

It collects the financials, KPIs and documents that portfolio companies and borrowers send each month or quarter, maps them to a standard set of metrics, tracks covenants and valuations, and produces reports for the investment team, the valuation committee and LPs. The newer AI tools also read packages in any format, answer questions in plain language and raise early warnings.

Can AI track covenant headroom from compliance certificates?

Yes. Several tools now read compliance certificates and compare reported ratios with covenant levels. The test is whether the tool applies each credit agreement's own EBITDA definition, as amended, with add-backs checked against their caps, and shows the arithmetic. Kepler recomputes each ratio in code from cited lines in the agreement and the certificate, and flags any gap from the borrower's reported figure.

Do portfolio companies have to fill in a template?

Older systems often asked them to. Most current tools, including Chronograph, iLEVEL, Lumonic and V7 Go, now extract figures from PDFs and spreadsheets in the company's own format. Kepler reads every page of whatever arrives, scans included, and files each figure against the right company, fund and period without a template.

Can ChatGPT or Claude do portfolio monitoring?

They can analyze the data a connector hands them: Chronograph's MCP server works in Claude, ChatGPT and Perplexity, and iLEVEL's connects to Claude and Copilot. On its own, an assistant doesn't collect packages, keep history or apply your definitions, and it writes the figures itself. Kepler runs inside both and brings the firm's ontology, the read documents and computed numbers into the same conversation.