Directory of AI deal diligence and document intelligence vendors
The AI FinTech Index holds 15 of them, each graded on the same 15 capability axes from public sources, with the artifact every grade was read from attached to the record.
No vendor pays for inclusion, placement or rating. Counts generated 2026-08-24 across 490 indexed vendors. What moved is in the change log.
The failure that matters is omission rather than error: a diligence summary that silently drops the document establishing the risk changes the outcome of a transaction. Ask what the system does with a document it cannot parse, and whether that appears in the output or only in a log.
What is in this directory. Screened to products used to assess a specific transaction or manager. Ongoing portfolio monitoring and fund administration are held separately.
Part of the wider Capital Markets & Research AI category.
What the public record shows in this directory
The share of the 15 indexed vendors here whose public record answers each of the nine regulatory questions a financial institution diligence process works through, and where this directory ranks against the other 50 directories in the index on the same question, highest share first. A thin share means the public record is thin, not that a control is absent.
These products read the documents behind nine figure commitments, and on what happens when the reading is wrong, this segment has a ceiling. No vendor reaches the top band on liability and recourse. The best anyone manages is the second band, and the three that get there all arrive the same way, by building traceability that makes an error findable during review rather than by accepting any consequence for it: a verifier class at one, closed loop grounding at another, buyer set confidence thresholds at the third. The path beyond exists and is not taken. Not one member of this segment publishes an error rate for its own reading, warrants any output, or describes what it owes a client when a misread term has already shaped a price. That matters more here than the grade suggests, because the failure mode in this work is omission rather than error: a summary that silently drops the document establishing the risk looks complete. Ask any vendor here two questions. What is your measured extraction error rate, and what happens when you are wrong.
The AI FinTech Index lists 15 AI deal diligence and document intelligence vendors, graded on 15 capability axes from public sources with no paid placement and no aggregate score. Across this directory the best documented part of the public record is how much the system decides on its own at 93 percent, and the thinnest is AI governance and bias testing at 0 percent, which is 33 highest of 50 directories in the index on that question. Across the whole index of 490 vendors, none documents all nine regulatory axes in public and the average documents 2.94.
These products read the documents behind nine figure commitments, and on what happens when the reading is wrong, this segment has a ceiling. No vendor reaches the top band on liability and recourse. The best anyone manages is the second band, and the three that get there all arrive the same way, by building traceability that makes an error findable during review rather than by accepting any consequence for it: a verifier class at one, closed loop grounding at another, buyer set confidence thresholds at the third. The path beyond exists and is not taken. Not one member of this segment publishes an error rate for its own reading, warrants any output, or describes what it owes a client when a misread term has already shaped a price. That matters more here than the grade suggests, because the failure mode in this work is omission rather than error: a summary that silently drops the document establishing the risk looks complete. Ask any vendor here two questions. What is your measured extraction error rate, and what happens when you are wrong.
Source: AI FinTech Index, August 2026
| Vendor | Category | AI Centrality | Website |
|---|---|---|---|
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C
Capsa AI
Capsa AI builds an operating system for private equity that lets investment professionals delegate the manual, low-skill due diligence work that consumes about a quarter of their time, covering company profile creation, extraction and consolidation of financial information from company records, financial performance analysis and customer contract review. Its later positioning extends across the full deal lifecycle from sourcing through diligence to portfolio monitoring, surfacing and tracing answers to complex investment questions and letting teams build custom workflows. By connecting to existing customer systems, shared drives and market data platforms and indexing historical deal and communication data, it allows a firm to retrieve its own institutional knowledge while maintaining auditability across the investment process. Early customers reported a fifth off due diligence time, and it serves funds managing over 30 billion dollars across the United States, United Kingdom and Germany from offices in London and New York.
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Capital Markets & Research AI | A | capsa.ai |
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C
CENTRL
CENTRL sells diligence automation to both sides of the institutional manager selection relationship and to the banks that oversee their own counterparties. Four products sit on one platform. DD360 is the allocator facing due diligence system, carrying questionnaire construction from industry templates or proprietary uploads, a manager and fund database, a document repository, scoring methodologies, question level benchmarking, time series analytics and remediation tracking with severity indicators. Response360 faces the other way, helping asset managers answer incoming questionnaires and requests for proposal from a centralised answer library with automated verbatim matching. BNM360 serves securities services, global custody and depositary teams overseeing agent and correspondent banks. Vendor360 covers third party risk. Above all four sits an agentic layer the company calls CentrlX, built around curated workflows, prebuilt industry integrations and permissioning controls, alongside a generative assistant that pre populates answers, detects issues, answers natural language questions across manager records and assembles board ready reports in several document formats. Founded in 2015 and headquartered in Silicon Valley with offices in New York, the United Kingdom, India and Australia, the company states that its platform is used by some of the largest banks and investment management firms across the Americas, Europe and Asia Pacific.
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Capital Markets & Research AI | B | centrl.ai |
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C
Claira
Claira turns the data locked inside financial legal agreements into granular structured datasets that institutional investors can act on, serving private credit funds, banks, investment firms, trading desks and law firms across collateralised loan obligations, municipal bonds, leveraged loans, commercial real estate and structured credit. Its argument is that investment analysis in private markets still relies on people remembering past lessons while firms sit on troves of proprietary research they never reuse, so the platform both accelerates document work and systematically captures institutional knowledge for future transactions. Documents arrive by email to a dedicated secure address and are ingested, classified and routed automatically with no uploads or manual tagging, and executed documents received at loan closing produce an auditable and traceable data feed. A named bank executive reports structured credit document analysis falling from over twenty minutes to minutes.
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Capital Markets & Research AI | A | claira.io |
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C
Clarum
San Francisco company building AI agents for private market diligence, monitoring and reporting, founded 2023 by Anton Otaner and Tommy He and part of the Y Combinator Winter 2024 batch. Connects to data rooms, cloud drives, CRMs and third party datasets, works directly in Excel, Word and PowerPoint rather than requiring teams to change tools, and structures deal materials to answer diligence questions and surface risk. Its later positioning is a firm level data model built once from a private capital firm's own deal memos, diligence files, partner notes and returns, which every AI tool or agent the firm uses can then draw on.
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Capital Markets & Research AI | A | clarum.ai |
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C
Cognaize
Cognaize extracts decision ready information from the most complex unstructured financial documents, including credit agreements, financial reports, ESG disclosures, loan applications, regulatory filings and trustee reports, for banks, insurers, asset managers, data providers and credit rating agencies. Its approach combines neuro symbolic agents with finance specific language models trained on more than 1.3 million financial documents, a separate class of AI verifiers whose function is to validate extracted values against each institution's own definitions and rules, and an interface built to engage human experts in the loop, which the company calls hybrid intelligence. Models are deliberately downsized and fine tuned so they can run on premise or in a private cloud, keeping customer documents inside the institution's own environment.
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Capital Markets & Research AI | A | cognaize.com |
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C
Cyndx
Cyndx is a deal origination platform for private equity, venture capital, corporate development teams, advisers and companies raising capital, built around a search engine that reads what a company actually does rather than matching keywords or static industry codes. Natural language models generate a taxonomy that shifts as markets shift, so a user can map a niche sector and retrieve companies doing closely related things even without the right search term. Its distinguishing capability predicts which private companies are likely to need capital or transact, and the company publishes how often those predictions proved right within the stated window. A product suite covers sourcing, investor identification, ownership research, valuation, generative deep research and a conversational interface across a universe of more than 16.5 million companies and investors.
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Capital Markets & Research AI | A | cyndx.com |
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D
Dasseti
Dasseti, founded as Diligend and rebranded in January 2023 with Nasdaq Ventures among its strategic shareholders, builds due diligence and data exchange software sitting between institutional allocators and the managers who report to them. Two platforms face opposite sides of that exchange. Dasseti COLLECT serves asset owners, consultants and fund of funds teams running operational and investment due diligence, covering digitised questionnaires built on industry standard templates or custom ones, automated response flagging, comparison and scoring, research management, fund review workflows, a manager relationship record, an external manager portal and analytics. Dasseti ENGAGE serves the manager side, helping investor relations teams respond to questionnaires and requests for proposal, and carries the only direct integration between a proposal platform and the Nasdaq eVestment Omni consultant database. A separate module collects sustainability data against a long list of reporting frameworks. The inference layer runs through the workflow rather than beside it: Sidekick AI embedded across the process, Smart Docs extracting structured data from portable documents, word processor files and adviser registration filings, Smart Writer assembling investment committee ready reports directly from structured responses and source documents, and automated flagging of regulatory gaps in responses. Clients include asset owners, consultants and managers holding more than 20 trillion dollars in combined assets.
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Capital Markets & Research AI | B | dasseti.com |
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E
Ezra
Ezra, formerly Ezra Climate, turns the unstructured data rooms behind asset backed credit and project finance transactions into structured datasets, extracting deal terms, surfacing risks and drafting investment memos, research reports and diligence question sets for credit teams. It is built as a closed loop system in which every output is grounded in the underlying deal documents and traceable back to source material, a design the company adopted after a year of internal benchmarking found general purpose models answering private credit questions incorrectly or without support around 30 percent of the time. Alongside the analysis platform it is building a network connecting companies raising capital with institutional lenders seeking deal flow, across renewable energy, infrastructure, fintech and real estate.
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Capital Markets & Research AI | A | ezra.finance |
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G
Gain
Gain, formerly Gain.pro, supplies private market intelligence to private equity investors, M&A advisers, corporates and consultancies, covering the companies, industries, investors and advisers that make up the private deal ecosystem. Its data graph spans every company above ten employees alongside 20,000 investors, 500,000 deals and 11,000 advisers, assembled by combining automated collection with hundreds of researchers who verify profiles by hand. On top sit AI search, market mapping that identifies patterns and likely buyers, watchlists that surface opportunities automatically, agentic workflows, and a model that predicts whether a company is open to being acquired. Integration runs through two way relationship management sync, interfaces, bulk data, a spreadsheet add in and structured feeds into a customer's own language models.
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Capital Markets & Research AI | B | gain.ai |
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K
Keye
Keye automates the financial diligence private equity teams run on a target, ingesting raw deal files in any format and producing structured data cuts, cohort and margin analysis, anomaly detection and investor ready outputs that mirror how a deal team already works. Its stated architecture pairs models for reading and structuring with deterministic pipelines for the arithmetic, so every figure carries its source, formula and supporting quote, and analyses export as fully linked spreadsheets that can be edited and reloaded for the system to update.
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Capital Markets & Research AI | A | keye.co |
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K
Kruncher
Kruncher builds private market intelligence for venture capital, private equity, family offices, emerging managers, banks and corporate development teams, unifying a fund's internal documents, data rooms, call transcripts, customer records and more than twenty premium external sources into one source-traceable data layer. More than thirty specialised agents score every company against the fund's own strategy and route signals to the relevant partner as they fire, generating deep configurable company reports in fifteen to thirty minutes and investment committee memos in under ten. Its distinguishing design is thesis tuning: each fund configures its own scoring criteria, signal thresholds, report templates and key metrics, up to three hundred deal-score parameters, so two funds asking the same question receive different answers. Every vote, edit and configuration sharpens the intelligence inside that fund's own tenant. It holds three security certifications and reports more than a hundred funds as customers across three continents.
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Capital Markets & Research AI | A | kruncher.ai |
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P
Pactio
Pactio automates the closing of private equity deals, synchronising the four artefacts that must reconcile at completion and normally live in separate spreadsheets: sources and uses, capitalisation tables, expenses and wiring schedules. It flags errors before they propagate and produces a transparent, audit-ready deal log with controlled access, addressing work usually scattered across counsel trackers, fund administrators, spreadsheets, email chains and repeated investor data requests. The workflow tool sits inside Microsoft Office rather than asking deal teams to leave the tools they already use. Its founders are a former Goldman Sachs private equity executive director and an AI researcher, and the company is building toward a single source of truth across the whole investment lifecycle, extending into private credit and secondaries. A major global professional services firm has announced a strategic alliance to digitise deal execution using it.
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Capital Markets & Research AI | B | pactio.com |
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T
Termina
Termina, operated by Termina Systems, sells on demand quantitative diligence to investors, analysing a target company's financial and transactional data rather than its documents to assess what it calls quality of growth. A scan ingests transaction level data at scale and returns findings against the company's own global benchmarks, covering customer acquisition cost, payback period, contract value, revenue retention and growth composition, with one to one comparison against prior targets. The same engine runs across a portfolio ahead of follow on decisions and board meetings. Buyers are venture capital and private equity firms, sovereign wealth funds, corporate development and merger teams, and company founders.
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Capital Markets & Research AI | A | termina.ai |
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T
ToltIQ
ToltIQ, formerly DiligentIQ, applies generative models to private markets due diligence, ingesting the contents of a virtual data room and turning them into queryable intelligence for a deal team working against a deadline. Purchase agreements, quality of earnings reports, customer cohort analyses and side letters are analysed and categorised so investment professionals can interrogate thousands of pages rather than read them, surfacing growth opportunities, operational issues and early risk signals. The company was founded by a former partner and chief information officer at a major global private equity firm, and its buyers are general partners, limited partners, family offices and diligence advisory firms.
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Capital Markets & Research AI | A | toltiq.com |
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V
V7 Go
V7 Go is an agentic document workflow platform from V7 Labs, sold into document heavy industries with private markets, insurance and finance among its named verticals. Its financial services proposition covers the investment lifecycle, with pre built agents for confidential information memorandum analysis, due diligence questionnaire completion, portfolio monitoring, annual and quarterly filing analysis, and know your customer and underwriting extraction. Users assemble workflows visually without engineering, setting confidence thresholds and routing rules that send uncertain items to human review, and every output carries a citation tracing it back to its exact location in the source document. The platform routes between multiple frontier models depending on which performs better on a given task.
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Capital Markets & Research AI | A | v7labs.com |
Common questions
Is there a directory of AI deal diligence and document intelligence vendors?
Yes. The AI FinTech Index lists 15 AI deal diligence and document intelligence vendors, each graded on the same 15 capability axes from public sources, with the artifact every grade was read from attached to the record. No vendor pays for inclusion, placement or rating, no vendor is contacted before it is listed, and nothing sits behind a form. Counts generated 2026-08-24.
What counts as deal diligence and document intelligence in this directory?
Screened to products used to assess a specific transaction or manager. Ongoing portfolio monitoring and fund administration are held separately. The index holds 15 vendors meeting that screen, drawn from a wider Capital Markets & Research AI category and from adjacent categories where the vendor belongs on the same shortlist. A vendor filed under a different category can still appear here, because a buyer building this shortlist does not sort by our filing.
What should a buyer check before shortlisting deal diligence and document intelligence vendors?
Start with what this segment does not publish. Across the 15 indexed vendors, the thinnest parts of the public record are AI governance and bias testing at 0 percent, regulatory status and licensure at 0 percent, and deployment model and data residency at 7 percent. A thin public record predicts the length of a diligence process rather than the absence of a control, so these are the questions to put in writing early. The failure that matters is omission rather than error: a diligence summary that silently drops the document establishing the risk changes the outcome of a transaction. Ask what the system does with a document it cannot parse, and whether that appears in the output or only in a log.
Comparisons inside this directory
Other directories in Capital Markets & Research AI
One category is several buying decisions sharing a label. Each of these narrows the same market to a different one.