Jurimesh vs Marveri: how they compare in 2026
Jurimesh and Marveri both run legal due diligence on a transaction's data room, organizing the documents, flagging gaps and producing cited findings for the deal team. The difference is in the terms behind each product. Jurimesh's terms bar training on customer information without explicit consent, acknowledge that documents may be covered by professional secrecy, and delete live data within thirty business days of termination. Its data processing policy commits to notice before any disclosure to authorities, keeps data inside the EU, and lists each subprocessor with its hosting location. Marveri states on its security page that customer data never trains models. Marveri's counterweight is the range of deal work product it produces. It drafts disclosure schedules from every representation and warranty, ties out cap tables against the record, and answers request lists, with exact quote and calculation checks linked to each source.
Which one fits
Choose Jurimesh if
- You work from the seller's existing data room. Jurimesh connects directly to Ansarada, Virtual Vaults, HighQ and iDeals, and to Google Drive and SharePoint, classifies every document against the buyer's request list, and shows what is missing before anyone starts reading.
- You need data kept in the EU under a contract you can read. Jurimesh's data processing policy keeps data stored and processed inside the EU, lists each subprocessor with its entity and hosting location, gives 30 days to object to changes, and commits to notice before any disclosure to authorities.
- You want the vendor to indemnify you. Jurimesh's terms indemnify the customer for direct damages from its own breach and from third party intellectual property claims, with renewed performance first and compensation up to a year's fees, and its checks come from a library of more than a thousand authored legal checks.
Choose Marveri if
- You need disclosure schedules and tie outs, not just findings. Marveri drafts disclosure schedules from every representation and warranty and exports them to Word, verifies cap tables against the underlying record, and builds clause tables for change of control, assignment and governing law.
- You want answers you can check without prompting. Marveri runs structured analysis with no chat interface, links every output to its source document with exact quote and calculation verification, and flags missing signatures, board approvals and amendments with a supplemental request list.
- Your data room spans languages and both sides of a deal. Marveri supports more than a hundred languages, answers request lists on the sell side and shows how a target should respond on the buy side, and states SOC 2 Type I and Type II.
At a glance
All 15 axes, side by side
The same grid applied to every vendor in the index, graded from public sources. Hover a grade to see what the letter means on that axis.
AI Centrality
How much of the product is actually AI. Whether the machine learning is the mechanism the buyer is paying for or a feature layered onto conventional software, and whether the vendor is specific about which is which.
The agreement defines the Solution as a proprietary software platform that uses artificial intelligence to speed up legal due diligence. Customers upload legal and business documents, and the platform analyzes them and extracts insights. Every published function is inference over an uploaded set: the platform recognizes and classifies documents against a request list, analyzes gaps in what the seller provided, runs more than a thousand authored legal checks across the data room, extracts risk provisions, and generates the due diligence report. There is no document management layer underneath, by design. The product connects to the customer's existing data room rather than replacing it, so without the models what is left is a connector to someone else's repository.
The product reads an entire data room and produces diligence memos, responses to request lists, disclosure schedules, cap table reconciliations and clause tables. Without the models, what remains is a file organizer. The company was founded in 2023 around this capability and has no product line without AI.
Citation Accuracy and Hallucination Disclosure
Whether the vendor publishes measured accuracy on citations and assertions, grounds output to primary sources, and says plainly what its system does when it does not know. Legal has a documented public record of fabricated citations reaching filed briefs, so an untested claim of accuracy is not evidence.
The stated design checks every clause in the data room against risk guides written by experts and cites every finding back to its source. Findings are shown against the clause and schedule they rest on, and the company says its check library is written by named practitioners whose reports it publishes separately. No accuracy figure, evaluation, test set, error rate or failure mode statement is published, and there is no accuracy or benchmark page. Article 3.5 of the agreement disclaims any warranty that the Solution will perform without error and makes the customer responsible for verifying the accuracy and validity of all output. The published figures measure throughput, not accuracy.
Every output is stated to link back to its source document. The law firms page lists verification of exact quotes, verified math calculations and references across documents, and exports to Word and Excel carry citations. The product is positioned on verifiable results rather than conversational answers. No accuracy figure, test set, evaluation or hallucination statement is published. Grounding to primary law does not apply to a tool that reads the customer's own documents.
Autonomy and Oversight Model
What the system decides on its own, what a lawyer must approve, and whether the vendor documents where the review point sits. A tool that drafts under review and a tool that files without one are different products and different risks.
Findings are cited to the clause so a reviewer can open the source, the workspace carries review states for each item with named reviewers and timestamps covering reviewed, waiting for review and modified, and the security page commits that every user action is recorded in full audit trails. Article 3.5 of the agreement makes verification of all output the customer's responsibility, and article 4.1.3 requires the customer to cease use immediately and notify Artificieel on observing any deficiency. Nothing states what the agents do unattended, what routes a finding into review as against straight into the report, what happens when a check misfires, or whether generated text is marked as generated. The product is marketed as handling the entire diligence process.
The product runs structured analysis automatically without prompting, producing cited work product a lawyer verifies and delivers, with role based access control and tracking of document status. Results are described as auditable and ready for review before a first call. Nothing published sets a threshold at which an output is treated as final without review, or a route back after a wrong finding beyond the reviewer checking the citation.
Operational and Outcome Evidence
Named, dated evidence that the product works in production at real firms or legal departments. Case studies with figures and identified customers count. Unattributed testimonials and launch announcements do not.
Three testimonials carry full attribution, each linked to its own customer story: Bas Mees, Partner M&A at Rutgers and Posch; Christopher Tournis Gamble, Managing Partner at WAD Capital; and Hans Kayaert, General Counsel at Aikido. Tournis Gamble's account describes work a legal team would take months over completed in a couple of hours. The company's funding release adds Davy Gorselé, managing partner at Quorum. Eleven organizations appear as logos, including Andersen, Van Benthem and Keulen, Vriman, One Peterson, Around Partners, Vybros, 9Corporate and Lighthouse. The site also publishes sixty to eighty percent less manual review, attributed to customers generally, a first pass review of a data room in under twenty minutes, four thousand contracts screened and more than a thousand authored checks. None of those figures is tied to a named firm or dated, and no method is published. The name Hans Kayaert also appears in the company's funding announcement as the representative of Syndicate One, the lead investor.
The law firms page states that client onboarding time was cut by seventy percent, with no attribution. User quotes reproduced by a third party directory praise the product without naming a firm. The seed round announcement states that a syndicate of early users invested. No customer is named alongside a figure.
Privilege and Confidentiality Posture
How client confidences are handled: attorney client privilege and work product treatment, segregation of one client matter from another, whether client data trains any model, and what the vendor commits to in writing rather than in marketing.
Article 10.2 records that Artificieel acknowledges Confidential Information may be covered by the customer's professional secrecy. Article 10.4 commits that the AI models are not trained on the customer's Confidential Information without explicit consent. Article 9.3 allows anonymized and aggregated data to be collected to improve the Solution only if it does not include or reference any User Content, unless the customer expressly authorizes that in writing. Article 9.2 licenses User Content solely to provide the Solution, and the security page says documents, findings and data rooms never leave their tenant, alongside SSO and full audit trails. The data processing policy binds personnel to written confidentiality and limits access to those doing the work. Professional secrecy is acknowledged but given no handling treatment, and no separation by matter inside a customer is described. The confidentiality obligation expires three years after termination.
The security page states that customer data is never used for training, along with encryption in transit and at rest and SOC 2 Type I and II. The law firms page lists role based access control. No customer agreement or terms of service is published, so nothing binds the training statement. The security and law firms pages state no retention or deletion commitment, the model providers behind the platform are not named, and nothing addresses privilege or work product for a tool that handles deal data rooms.
UPL and Professional Responsibility Posture
Whether the vendor is clear that it supplies a tool rather than legal advice, who its audience is, and how it addresses unauthorized practice of law, competence and supervision duties, and jurisdiction limits. ABA Formal Opinion 512 is the reference point. Where the advice line is not the duty a product raises, the axis is read through the nearest professional duty it does raise: judicial conduct rules and the reviewing duty for products sold only to courts, and the duty to bill for time actually spent for products that draft time entries.
The product produces the due diligence report a transaction is priced on. The product and solutions pages present it as a first-pass legal review in under 20 minutes, with checks written and maintained by senior specialists in corporate, employment, IP, real estate and commercial law, every finding cited to the source clause and every review action timestamped. Neither those pages nor the home page, the security page, the terms and conditions, the data processing policy or the subprocessor page carries a statement that Jurimesh or Artificieel is not a law firm or that output is not legal advice, anything on the professional judgment a lawyer must apply before a finding reaches a client, or any jurisdiction limit, supervision or competence language. Article 3.5 requires the customer to verify accuracy and validity, which concerns correctness rather than advice, and articles 4.1.6 and 4.1.7 allocate responsibility for input and regulatory compliance without touching the question. The product classifies contractual risk, flags uncapped indemnities and change of control triggers, and generates the report itself.
The product is marketed to lawyers and deal teams as producing the cited work product they are responsible for delivering. Its web pages do not state that outputs are not legal advice, who should use them, or how the product supports a supervising lawyer's duties. No terms of service exists on the site to carry such a statement. A privacy policy is published as a PDF on the application domain.
AI Governance and Bias Disclosure
Published governance over model behavior: who owns it inside the vendor, what is tested before release, and what is disclosed about disparate output across matter types, parties, or populations.
No responsible AI page, principles statement, accountable owner for model behavior, evaluation regime before release or AI management system such as ISO 42001 is published, and nothing addresses uneven output. The vendor is established in Belgium and sells into EU legal transactions, and nothing published engages the EU AI Act, while its policy set cites the GDPR, the Belgian Privacy Law and the ePrivacy Directive by name. The product's main asset is an authored check library, and nothing published says who writes a check, how it is reviewed, or how a firm would know when one is wrong or out of date. ISO 27001 is an information security standard, not AI governance.
The security page states that systems are continuously audited by trusted third parties for transparency and security. The product cites every output to its source, a control on hallucination rather than a governance disclosure. No responsible AI framework, ISO 42001 or equivalent, accountable owner, description of testing before release or statement about uneven output is published.
AI Safety and Data Stewardship
Retention, deletion, access control, and what happens to prompts and documents after they are processed. Whether the vendor states its subprocessors and its incident practice, or leaves the buyer to assume.
Retention and deletion are stated as periods. On termination live data is deleted within thirty business days and backups overwritten within a further ninety days, after a twenty day export window, and the processing annex keeps account and support data for one year after termination. Access is bounded by SSO, full audit trails of every user action, personnel under written confidentiality agreements, and access limited to those doing the work. Incident practice is committed: notification without undue delay with the information the GDPR requires, and help with the customer's own reporting duty. A versioned subprocessor register is published. Annex II of the data processing policy refers the technical and organizational measures to the trust center, which lists 113 controls publicly and releases its architecture document and policies on request, so no encryption standard, key management or tenancy architecture appears on a page anyone can read.
Role based access control and encryption in transit and at rest are stated. The security page states no retention period or deletion commitment, subprocessor list or practice for notifying incidents. The trust center link on the security page leads back to the security page itself. A privacy policy is published as a PDF on the application domain.
AI Liability and Recourse
What the vendor stands behind contractually when its output is wrong. Indemnities, caps, carve outs, insurance, and whether any of it is published or only reachable through a negotiated agreement.
Article 8.5 commits Artificieel to indemnify, defend and hold the customer harmless for direct damages from its own breach of the agreement and from infringement of third party intellectual property in connection with the Solution. Article 8.2 sets a sequence of remedies rather than a bare cap: renewed performance first, then, where that is no longer possible or reasonable, compensation for direct damage limited to fees paid in the preceding twelve months. The data processing policy mirrors this, with mutual indemnity for breach of the policy or privacy law and the same twelve month ceiling. Against that, article 3.4 provides the Solution as is, article 3.5 disclaims any warranty of error free performance, article 8.1 excludes indirect and consequential loss including data loss, and article 8.3 lists further exclusions. Nothing warrants that a finding is correct, no service credit exists and no insurance position is published.
No liability position is published. The site's navigation and footer carry Security, Contact, Solutions and a privacy policy PDF, and no terms of service, customer agreement or trust center document. The security page describes controls and makes no warranty, indemnity, cap or insurance statement.
Practice Systems Integration Depth
How deeply the product reaches into the systems legal work already lives in: document management such as iManage and NetDocuments, Word and Outlook, contract lifecycle management, matter management, e-billing, and court filing systems.
The integrations page names six connectors: Virtual Vaults, iDeals and Ansarada among VDR platforms, HighQ, and Google Drive and SharePoint for file storage. Documents flow into Jurimesh over a secure API connection once a user connects a data room in the application, so nothing is downloaded and re-uploaded by hand, and Virtual Vaults and iDeals have announced Jurimesh as an AI partner. New connectors are added on customer demand. The platform maps the seller's documents to the buyer's request list and works alongside a firm's existing VDR rather than requiring migration. No field mapping, return flow, trigger condition or permission model is described, no API or developer documentation is published, and nothing addresses document management, matter management or billing systems.
The law firms page lists Google Drive integration, SharePoint and OneDrive sync and local folder sync, along with Word and Excel export with citations. A third party profile states there is no API. Nothing published describes what syncs, in which direction or what a firm must configure, and no document or matter management system is named.
Deployment Model and Data Residency
Where the software runs and where the data sits. Multi tenant cloud, single tenant, private deployment, on premises, and whether region of residence is a published option or an enterprise conversation.
The security page says all data is stored and processed only within the EU on infrastructure certified to ISO 27001. The data processing policy makes that contractual at article 7.1, and article 7.2 sets out the safeguards for any transfer outside the EEA: adequacy decisions, the 2021 standard contractual clauses with a transfer impact assessment, binding corporate rules and certification mechanisms. The subprocessor register names the contracting entity and hosting location for each provider, showing that Google Cloud EMEA, Microsoft Ireland and AWS EMEA SARL all host in the EEA, and the trust center lists AWS, Azure, Google Cloud Platform and ConvertAPI. On tenancy the site offers one sentence, that documents, findings and data rooms never leave the tenant they belong to, without describing the tenancy model, and no dedicated, single tenant or self hosted option is offered at any tier. The architecture document sits in the trust center behind a request access flow.
Marveri runs as a hosted application at an application subdomain, and the security page states encryption and GDPR compliance. No hosting provider, region, residency option or tenancy model is named.
Security Certifications and Trust Center
Independent attestation a buyer can pull without a sales call: SOC 2, ISO 27001, penetration test summaries, a trust center with current reports and named scope rather than a badge image.
ISO 27001 is stated as certified on the home page, the security page and every page footer, and the trust center at trust.jurimesh.com, linked from every footer, lists ISO 27001:2022 with its certificate and statement of applicability, GDPR, and SOC 2 as being implemented rather than achieved. The trust center shows 113 controls and a September 2026 penetration test certificate from Cresco Cybersecurity that opens without a request, while the ISO certificate, the statement of applicability, an architecture document, a letter of commitment and fifteen policies are released through a request access flow with no sales call. The CTO's signed note repeats that the certification is maintained, and the company says regular third party audits and penetration tests validate its controls. A public status page sits alongside. No certification body, certificate number or expiry is published outside the request flow.
The security page states SOC 2 compliance, Type I and Type II, with a badge, along with GDPR compliance, encryption in transit and at rest, and continuous auditing by trusted third parties. The law firms page repeats that Marveri is SOC 2 Type II certified. No auditor, coverage period or report route is published, and the View Our Trust Center link leads back to the security page itself.
Model Supply Chain Disclosure
Which models sit underneath, whose they are, where they run, and whether the vendor commits to telling customers when that changes. A legal buyer inherits every dependency it cannot see.
The vendor's funding release says the platform uses large language models, and the agreement defines the Solution as using artificial intelligence, but no model, version, provider or hosting arrangement for the model layer is named, and no commitment to notify customers of a change is published. The subprocessor register, versioned and dated 20 April 2026, lists eight processors with contracting entity, jurisdiction, nature of processing and safeguard for each: Google Cloud, Microsoft Azure, Amazon AWS, ConvertAPI, HubSpot, Slack, tl;dv and Sentry. Those are cloud infrastructure and business tools. The only AI provider named in the document, Anthropic, appears as the processor inside tl;dv, the vendor's meeting recording tool, not as anything touching a customer's data room.
Marveri describes AI built by machine learning researchers and states that data is never used for training. Its web pages name no model, provider or inference location, and make no commitment to notify customers of changes. A privacy policy is published separately as a PDF.
Commercial Transparency
Whether a buyer can learn what this costs without entering a sales process: published rates, the unit being charged, what sits behind an enterprise tier, and what implementation adds.
There is no pricing page and the only commercial route is a demo request, but the agreement sets out the unit and its arithmetic. The unit is a Transaction, covering the documents for one corporate transaction such as an acquisition, investment or restructuring, and consumed when the analysis completes. Each Transaction covers up to 1,500 documents, with another charged for each further block of 1,500 or part of one. A document over fifty pages counts as one document per fifty pages, and a set under 150 documents is charged at half a Transaction. Use beyond the purchased allocation costs the Transaction price plus twenty five percent, and unused Transactions expire at the end of a term with no carry over or refund. Fees are in euro excluding VAT, invoices are due in thirty days, price changes need two months notice, and annual indexation follows a formula tied to the Agoria wage index. No rate is published.
No pricing information is published at any level. The site offers a demo and a login and carries no pricing page, unit of charge, plan or figure. A third party profile states there is no public pricing.
Firm and Practice Coverage
Who the product is actually built for. AmLaw, midlaw, small firm and solo, in house departments, government and courts, and which practice areas are supported rather than merely claimed.
Law firms, private equity and corporate in house teams each have a dedicated page. Law firms are told diligence can move from a cost center to a profit center, private equity is offered a fast read on a target's risk, and corporate teams are offered diligence in house. Practice depth is narrow by design: transactional diligence for mergers, acquisitions, investments and restructurings. Named customers are M&A practices and investment firms in Belgium and the Netherlands. No jurisdiction is stated for the more than a thousand authored legal checks, nothing says whether a Belgian check applies to a Dutch or German target, and no languages are named for document recognition. No firm or deal size band is given either.
Solutions pages address M&A lawyers, startup and venture lawyers, companies preparing for a deal and companies evaluating targets, naming private equity, investment banks and corporate development. Document coverage spans commercial agreements, IP, board minutes, financing documents and HR materials, with support for more than a hundred languages. The vendor states its focus is M&A and investment diligence only. No jurisdiction is named.
The 12 legal signals, side by side
Recorded rather than graded. These are the questions a practitioner has to answer before a tool touches a client matter, and the answers are taken from public material only.
Client Data in Training
Can material a lawyer puts into this product be used to train a model?
Article 10.4 of the general terms says the AI models are not trained on the customer's Confidential Information unless explicit consent is given. Confidential Information is defined broadly enough to cover the documents and personal data a customer uploads. Article 9.3 allows anonymized and aggregated data to be collected to improve the Solution only if it does not include or reference any User Content, unless the customer authorizes that in writing, which closes the improvement route.
Article 9.2 licenses User Content solely to provide the Solution. The security page says documents never feed or train a model, without the consent exception the agreement contains.
The security page states under the heading Zero Training that customer data is never used for training. No terms of service or customer agreement is published to carry a matching term, and the only legal document is a privacy policy PDF on the application domain. The statement is unqualified and sits outside any agreement.
Prompt and Output Retention
How long does the product keep what a lawyer typed, and can that be set to zero?
Fixed periods are published in the agreement and the data processing policy, and they agree. On termination, customer and personal data in live systems is deleted within thirty business days unless the law requires retention. Backups, which cannot be deleted at once, are overwritten or deleted within a further ninety days. A twenty day export window comes first, with help beyond the standard export charged at the vendor's hourly rates.
The processing annex keeps account and support data for one year after termination and handles diligence documents under the deletion articles. Nothing says how long generated findings, reports or the prompts behind them persist during a live subscription, beyond a general statement that personal data is kept only as long as needed to provide the services.
The security page, the home page and the footer state no retention period for uploaded documents or generated work product. The security page addresses encryption and training only, and no customer agreement is published. A privacy policy is published separately as a PDF. A third party profile notes that the retention policy is not public.
Ethical Walls and Matter Segregation
Does retrieval respect the firm’s ethical walls, or can the model read across them?
The security page says a customer's documents, findings and data room never feed a model, never train a model and never leave the tenant they belong to. Around that sit access controls: single sign on through the customer's identity provider, full audit trails of every user action, and a data processing policy that limits staff access to those doing the work and binds them to written confidentiality agreements. Nothing states whether the platform is single or multi tenant, and no permission or role model inside a customer's workspace is documented, on a product built for collaborative review where a deal team, and on the law firm side possibly conflicting deal teams, work in shared spaces. The trust center lists 113 controls publicly and releases its architecture document and policies only on request.
The law firms page lists role based access control among the platform's features, and the security page describes robust access controls in general terms. Nothing describes how one deal's data room is walled off from another within a firm or how the analysis respects those permissions. No document management system's access model is inherited.
Third Party Request and Subpoena Notice
If someone subpoenas the vendor for a firm’s data, does the firm hear about it first?
The data processing policy commits to notice in two places. Article 8.1 says Artificieel will not disclose or transfer personal data to third parties without the customer's prior permission, except where the law or a court or government decision requires it. In that case it will inform the customer fully, before any disclosure, of its scope and manner. Article 9.1 commits to best efforts to inform the customer as soon as reasonably possible on four triggers, including a request for information, a subpoena, or a request for inspection or audit from a public authority, and its own intention to disclose personal data to such an authority, so notice covers an intention to disclose as well as a demand received.
Article 7.3 adds notice before any transfer required by EU or member state law, unless that law prohibits it on public interest grounds. No transparency report is published.
No customer agreement is published, and the security page, the home page and the footer say nothing about third party demands for customer data or notice to the customer. A privacy policy is published separately as a PDF.
Primary Law Corpus Provenance
Where does the law in this product come from, and does the vendor have the right to use it?
The check library is the main asset, marketed as more than a thousand legal checks and risk guides written by experts and practitioners, and the company publishes separate reports from the people who write the checks Jurimesh runs, so the content is written by people rather than drawn from an external database. No author is identified for any check, no jurisdiction is stated for the library or any part of it, no source material is named, no licensing position is published, and nothing describes how a check is reviewed or how often it is refreshed as the law changes. The models otherwise read the customer's own data room, so no external legal corpus sits behind an output.
Data room documents, not a body of legal content, are what the product reads. It analyzes the customer's own data room and cites its findings to those documents, citing no law.
Good Law Verification
Does the product tell you when the authority it just cited has been overruled?
The product neither retrieves nor cites primary law. Its citations point to clauses and schedules in the customer's own uploaded documents, and its outputs are risk findings, gap analyses and due diligence reports. Nothing published describes how the authored check library is kept current as the law changes.
The product does not retrieve or cite primary law. Its output is diligence work product cited to the customer's own documents, and it has no citator function.
Refusal and Uncertainty Behavior
What does the product do when the answer is not in the corpus?
The home page, the security page and the published policy set describe no abstention path, state for having no answer, or confidence indicator against a finding, and nothing on behavior where a document is unreadable, a clause is ambiguous, or a check has no clear answer on the text. Gap analysis identifies documents missing from the seller's data room, which reports an absence in the material rather than uncertainty in the model's own output.
The interface shows a brief reasoning indicator before an answer, which signals that processing occurred rather than describing how confidence is assessed. Article 3.5 of the agreement makes the customer responsible for verifying all output.
The law firms page states that missing documents, signatures and approvals are flagged and a supplemental request list generated. It also states that figures which do not reconcile across sources are flagged. No confidence signal or abstention behavior for uncertain findings is described.
Fabricated Citation Record
Does a public court record exist addressing fabricated or hallucinated legal citations in output from this product?
The AI Hallucination Cases database kept by Damien Charlotin holds no entry naming Jurimesh or Artificieel BV. Its findings cite clauses in the customer's own data room rather than case law.
The AI Hallucination Cases database kept by Damien Charlotin holds no entry naming Marveri. The product is a diligence tool that cites the customer's own documents rather than case law.
Bar Guidance Alignment
Has the vendor engaged in public with the ethics opinions its buyers are bound by?
Article 10.2 of the general terms records that Artificieel acknowledges Confidential Information may be covered by the customer's professional secrecy, a direct reference to the obligation a European lawyer owes. No bar or law society, code of conduct, ethics opinion or regulator guidance is named. No jurisdiction is identified, although the agreement is governed by Belgian law and the customers span several European bars.
Nothing maps what a firm must do to meet its own supervision and competence duties when an authored check and a machine reading produce a finding that reaches a client. The regulatory material the vendor does publish concerns data protection, citing the GDPR, the Belgian Privacy Law and the ePrivacy Directive by name.
No ethics opinion, bar rule or professional responsibility framework is named on the home page, solutions pages or security page, and no terms of service exists to carry one. The product is built by former transactional lawyers for lawyers.
Billing and Fee Posture
Does the vendor address what happens to the bill when the work takes an hour instead of six?
The law firm pitch is moving due diligence from a cost center to a profit center, a claim about how a firm's economics change when the work compresses. It sits alongside sixty to eighty percent less manual review reported by customers and a first pass data room review in under twenty minutes. No record of work done with AI for each matter is described as available to a firm, and no guidance on fees or disclosure is published.
Nothing addresses what a client is told when the diligence behind a transaction was produced by agents running authored checks.
Law firms are a named buyer segment. The law firms page claims client onboarding time cut by seventy percent and diligence completed in days rather than weeks. Nothing addresses how diligence done with AI is recorded or disclosed on a client's bill. The founder describes diligence as consuming over half of transaction legal budgets.
Outside Counsel Guideline Readiness
Can a firm get this vendor through a client’s AI clause without a bespoke negotiation?
A versioned subprocessor register is published as Annex III to the data processing policy, dated 20 April 2026, listing eight processors with the contracting entity, jurisdiction, nature of processing and transfer safeguard for each. The policy makes maintenance an obligation. The list must be updated whenever a subprocessor changes, with changes marked and dated, and the customer must be notified and may object on reasonable grounds within thirty days, with a route to terminate if no solution is found.
The data processing policy and an annual audit right are published alongside it. The register does not say which model provider sees client content. The only AI company named, Anthropic, appears as the processor inside the vendor's meeting recording tool.
No subprocessor list, model provider list or AI disclosure material for clients is published. The security page states SOC 2 and assurances that data is not used for training without naming any processor. The trust center link leads back to the security page, and no customer agreement or DPA is published. A privacy policy is published as a PDF.
Court Disclosure Support
If a judge’s standing order requires an AI disclosure, can the product produce one?
Every finding is cited back to the clause and schedule it rests on, so the basis of an assertion can be produced. The workspace records review states for each item, with named reviewers and timestamps, so who looked at what and when is captured. The security page commits that every user action is recorded in full audit trails. No model or version is recorded against any finding, nothing marks which part of a report the machine produced and which a reviewer wrote, and no export is described for producing the record to a client, a counterparty or a tribunal. No disclosure template or guidance is published, and the agreement does not address it.
Every output links to its source document, exports to Word and Excel carry citations, and verification of exact quotes and calculations is listed, so each finding comes with a record of its sources. Nothing states that the model used or the human verification step can be exported, and diligence work product is not filed with a court.
The questions both sides leave open
Derived from the records above rather than written, so it cannot favor either vendor. Take these into both conversations and ask each side the same question.
- Primary Law Corpus Provenance
- Good Law Verification
In summary
Jurimesh
Jurimesh, from Artificieel BV of Ghent, Belgium, runs legal due diligence on corporate transactions from data room to report. It classifies each document against the buyer's request list and shows what is missing. It runs more than a thousand authored legal checks, with each finding cited to its clause, and builds a shared report. It connects to data rooms including Ansarada, HighQ and iDeals. According to the AI Legal Index, Jurimesh's published terms and data processing policy keep data in the EU, bar training without consent and commit to notice before disclosure. It is sold per Transaction, each covering one deal's documents, through a demo.
Marveri
Marveri, from Cambridge, Massachusetts, founded in 2023 by a former Morrison Foerster associate and MIT machine learning researchers, is an AI due diligence platform for M&A and venture lawyers and deal teams. It organizes a data room and flags missing signatures and approvals. It produces diligence memos, request list responses, disclosure schedules, cap table tie outs and clause tables, each linked to source with exact quote verification. According to the AI Legal Index, Marveri states SOC 2 Type I and Type II and that customer data is never used for training. It is sold through a demo.
Questions buyers ask
Jurimesh vs Marveri: which is better for M&A due diligence?
It depends on whether the firm needs the contract or the work product. Jurimesh publishes its terms, data processing policy and subprocessor register. Marveri produces a wider range of deal documents, including disclosure schedules and cap table tie outs. European firms that need EU data residency in writing have more to read from Jurimesh. From the AI Legal Index, based on each vendor's own published materials as of September 25, 2026. No vendor pays for placement.
Does Marveri train AI on data room documents?
Marveri's security page says customer data is never used for training. Jurimesh's terms state that its models are not trained on customer information unless explicit consent is given, and bar using customer content for product improvement without written authorization. From the AI Legal Index, based on each vendor's own published materials as of September 25, 2026. No vendor pays for placement.
Which data rooms does Jurimesh work with?
Jurimesh names Ansarada, Virtual Vaults, HighQ and iDeals among virtual data rooms, and Google Drive and SharePoint for file storage, and says it works alongside the data room a firm already uses rather than requiring migration. Marveri connects to Google Drive, SharePoint, OneDrive and local folder sync. From the AI Legal Index, based on each vendor's own published materials as of September 25, 2026. No vendor pays for placement.
How is Jurimesh priced?
Jurimesh sells by the Transaction and sets the rate on an order form. Each Transaction covers one deal's documents up to 1,500, with documents over fifty pages counted per fifty pages, half a Transaction for under 150 documents and a 25 percent surcharge beyond the allocation. Fees are in euro, excluding VAT, and unused Transactions expire at the end of the term. Marveri prices through a demo. From the AI Legal Index, based on each vendor's own published materials as of September 25, 2026. No vendor pays for placement.
Neither vendor paid for inclusion, placement or a grade, and neither reviewed this page before it published. Everything on it comes from public material on the dates shown. How the index grades.
Three points to weigh. Marveri publishes no customer agreement or terms of service, only a privacy policy. No liability position can be read before buying. Jurimesh's security page says documents never train a model, while its terms allow training with explicit consent, and the terms are the binding text. Jurimesh quotes Hans Kayaert as a customer's general counsel, and its own funding release names the same person for its lead investor. Neither vendor reviewed this page.