Lex Machina vs Trellis: how they compare in 2026
Lex Machina and Trellis cover different halves of the American court system, which is why litigators often hold both rather than choosing between them. Lex Machina is federal, spanning all 94 district courts, the 13 courts of appeal and the PTAB. Trellis is state trial, spanning more than 3,000 courts across over 2,500 counties in 45 states. Lex Machina publishes more about itself. Its coverage is stated with its own limits and dated to April 2025, and it commits flatly that customer data is never used to train AI models. It follows a published responsible AI framework naming avoidance of unfair bias, and its governing general terms carry an indemnity and a liability cap. Trellis publishes the thing Lex Machina does not, which is its price. It offers four plans, three carrying monthly and annual figures from 69.95 dollars a month, each with a stated annual content view allowance.
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.
Machine learning builds the asset, but what the buyer pays for is the resulting database. Lex Machina says proprietary technology and AI assisted attorney review turn raw legal documents into data sets and fill gaps in court records, and that its AI analyzes documents, reads signature blocks, updates case data and groups related entities. Without those models the analytics would not exist in usable form. But the product sold is a queryable dataset, valued for coverage and accuracy, with the generative layer arriving as Protégé on top rather than as the product itself.
Machine learning makes the underlying records usable, on a platform whose base offering is search over public court data. The vendor describes Trellis AI as combining its data with advanced language models to produce case assessments that review facts, claims and defenses and suggest likely outcomes, next steps and risks, along with argument drafting. Underneath is a docket search engine over more than 3,000 courts, close to what the company sold at founding, when it indexed California Superior Court records. Remove the models and a searchable trial court database remains.
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.
Accuracy is claimed in superlative terms and measured nowhere. The API section describes the offering as the industry's most accurate litigation data, and the product is positioned on filling gaps in court records that others miss, but no figure supports either claim. Searched the product page in full and located no accuracy rate, no error rate for extraction or entity resolution, no benchmark, no test set and no published evaluation. Nothing addresses what Protégé does when the underlying data does not support an answer to a prompt. What is published instead is provenance and scale: coverage figures carrying an explicit as-of date of April 2025, which tells a reader how current the data is without telling them how right it is.
Precision is asserted in marketing and disclaimed in the agreement. The vendor describes Trellis AI as leveraging advanced language models to deliver precise insights that enhance decision-making and case preparation, and positions the platform on unmatched coverage. The terms of service then disclaim liability for the omission or inaccuracy of any court-provided data and for any content, errors in or omissions from the online services. No accuracy figure, error rate, benchmark, test set or published evaluation is published on any surface read. That gap matters more here than on a drafting tool, because the product's output is factual assertions about how named judges have ruled, and a wrong one is not detectable by a reader.
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.
A real review point is published for the data pipeline, and nothing for the generative layer. AI assisted attorney review is how raw documents become data, putting lawyers between model output and what customers see, and the RELX Responsible AI Framework names human oversight as a principle. Checked 31 Aug 2026, nothing says what the extraction models decide alone, what share of output attorneys review or against what threshold, what happens when an error is found in published analytics, or what oversight applies to Protégé when a user queries the data by prompt.
Autonomy is claimed, and no oversight is published. Trellis AI is described as reviewing case facts, claims and defenses to give insight into likely outcomes, recommended actions and risks. Each assessment is said to offer next steps for case management, settlement strategy and trial preparation, and the product also drafts arguments. Nothing says what the system produces unattended, where a lawyer must review before relying on an assessment, or what happens when an output is wrong. A vendor job listing describes contract attorneys labeling verdict types and monetary awards to ensure data accuracy, which suggests human review in the data pipeline, but a job advert is not product material.
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.
Adoption evidence has a cited basis, and the testimonials thin out on inspection. The vendor says over 90 percent of the largest firms use it, citing the 2025 Law360 Pulse Leaderboard Report and the 2025 AmLaw100 Report, with data dated April 2025. Of three testimonials, one is a named practitioner at a named firm, John Johnson, a partner at Fish & Richardson, with no figure. The second is from an unnamed Chief IP Litigation Counsel at a Fortune 500 company. The third, from Miriam Rivera, former Deputy General Counsel at Google, says what she would do if she were at Google today, which is opinion rather than deployment evidence. No case study with figures was found.
Named users at named institutions, with scale figures that carry no customer. Attributed quotes come from Brian Cassidy, Student Services Librarian at Cleveland State University Law Library, on strategic trial court comparison analytics; Dean Walters, Assistant Director for Content at Harvard Law School, on the platform giving empirical sourcing to what was once anecdote; and Miguel Aristizabal, Partner at Clayton Trial Lawyers. All three are qualitative. The vendor separately states it serves tens of thousands of law firms and litigators daily across 3,000-plus courts in over 2,500 counties spanning 45 states. No figure for what changed at any named customer is published, and no case study with a stated method was found on the surfaces read.
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.
A clear and unqualified training commitment, short of the detail around it. The product page states that LexisNexis never uses customer data to train AI models, which is stated flatly with no carve-out for internal models, external models or aggregates, and adds that identifiable information is removed from AI interactions so performance can be improved without compromising privacy. Robust data retention and deletion policies are asserted, privacy by design is claimed at every stage of systems, products and business processes, and advanced encryption is referenced. Three gaps checked 31 Aug 2026: no retention period is stated, no segregation or access model is described, and privilege and work product are not addressed. The commitments also sit on a product marketing page rather than in any agreement, since the LexisNexis terms were not opened. Note the exposure here differs from a document platform, because what a customer puts in is largely queries and research rather than client files.
The vendor makes an explicit confidentiality claim, and the rest of its material cuts against it. Its blog says data confidentiality is a top priority, that the platform relies only on public filings so users need not enter sensitive client or internal data, and that Trellis handles no personal information or confidential case details. On the other side, the privacy policy describes collecting personal information. It limits disclosure of usage data to third parties except to service the account, enforce the terms, meet obligations to content and technology providers, or as law requires, which is a general assurance rather than a commitment about what the product holds. The knowledge base says judge pages show career history and political affiliation, so the platform does handle identifying information about named public officials. Trellis Envelopes, an in app messaging service for sharing rulings, dockets and documents between users, is a channel for the internal material the blog says users never need to enter. Nothing addresses training on queries or shared material, retention, segregation or access, or privilege and work product, on a product where a litigator's searches reveal strategy.
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.
Nothing published on the advice line was found. The product page, read in full including the data security, privacy and governance section and the footer, has no statement that the analytics are not legal advice, no professional responsibility or ethics page, no bar or ethics guidance such as ABA Formal Opinion 512, and no jurisdiction limits. The product predicts outcomes and forecasts a motion's chances of success, exactly the output a lawyer must judge independently, and the marketing invites that use without addressing it. The risk is lower than for a consumer tool because buyers are lawyers, but nothing is published.
Nothing is published on the advice line. There is no statement that Trellis output is not legal advice, no professional responsibility or ethics page, and no bar or ethics guidance named, including ABA Formal Opinion 512. Nothing addresses a lawyer's competence or supervision duties when relying on an assessment that recommends actions and settlement steps. The terms carry one significant use restriction, but it is regulatory rather than professional: no user may use the data to decide a consumer's eligibility for credit, insurance or a government license or benefit, with consumer defined by the Fair Credit Reporting Act at 15 USC 1681. That addresses the vendor's exposure as a data provider, not the lawyer's duties.
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.
A named governance framework addresses bias explicitly, without a named owner or published results. LexisNexis follows the RELX Responsible AI Framework and lists its commitments: AI that protects privacy, is transparent and explainable, avoids unfair bias, includes human oversight and is designed for real world impact. No person or role is named as accountable for model behavior, no testing before release is described, no evaluation method is published, and no finding on uneven output across case types, courts, parties or populations is disclosed. On a product that profiles named judges and counsel, published bias testing would matter a great deal.
No governance position on model behavior is published. Nothing names an accountable owner, describes testing before release, publishes a responsible AI framework or discloses anything about uneven output. The absence is notable on this product. The platform builds analytical profiles of named judges, and the knowledge base says judge pages show career history and political affiliation alongside ruling tendencies. A product that scores identifiable public officials and shows their political affiliation calls for published bias testing, and none exists.
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.
Principles are published where controls should be. The product page promises privacy by design, protection of customer data wherever it is stored, sent or accessed, advanced encryption, enterprise cloud providers, continuous monitoring, regular third party audits and robust retention and deletion policies, all assurances rather than specifics. Read in full, it gives no retention period, named subprocessor, incident or breach notification practice, encryption standard or access control model. The one concrete statement is that identifiable information is removed from AI interactions. A LexisNexis Trust Center is openly linked at trust.lexisnexis.com; it was not opened and may say more.
A general privacy policy covers the website and account relationship, not what a user does inside the product. The policy says Trellis does not sell customer or user data and limits disclosure of usage data to unaffiliated third parties, and that a user may cancel registration at any time in settings. No security page is published: no encryption standard, access control model, retention period, named subprocessor, breach notification practice or deletion commitment beyond account cancellation. The vendor argues the exposure is low, saying on its blog that the platform relies only on public filings and does not handle personal information or confidential case details. That is a real mitigating point, but it sits uneasily with a privacy policy that describes collecting personal information and with an in app feature for sharing documents between users.
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.
Read: lexmachina.com/terms/, which leads to the LexisNexis product page rather than a Lex Machina agreement, and the LexisNexis General Terms and Conditions effective 5 June 2026, which say they govern any LexisNexis product linking to them; no separate Lex Machina agreement exists. Section 7.1 indemnifies against third party patent, trademark, service mark, copyright or trade secret claims, with five conditions and three remedies, the last being termination with a pro rata refund, and 7.2 makes this the sole remedy. Section 6.3 caps liability at the lesser of actual direct damages or fees paid in the prior twelve months, in place of all other remedies. Section 6.4 excludes consequential damages with four named exceptions. Nothing covers output: section 6.1 disclaims liability for losses from errors or omissions in the services or materials, section 5.2 provides them as is, and section 4.3(a) says AI systems may not be accurate or error free and users must verify. No insurance position is named. For a product sold for case assessment, the errors clause is the one to read first.
Liability is addressed only through a limitation clause, and that clause disclaims precisely the exposure the product creates. The terms of service state that Trellis shall not be liable for any loss, injury, claim, liability or damage of any kind resulting in any way from any content, errors in or omissions from the online services, the unavailability or interruption of the services, the customer's use of them, the loss or corruption of any data, or the omission or inaccuracy of any court-provided data, all to the fullest extent permissible by applicable law. The services may also be enhanced, added to, withdrawn or otherwise changed without notice. No indemnity running to the customer, no warranty on output and no insurance position is published, and the cap was not among the text retrievable. A separate subscription agreement is referenced by the terms and is not published, so the full allocation may sit there.
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.
Integrations are named and all of them are inside the parent's own portfolio. The page states that Lexis+ with Protégé, CourtLink, Law360 and CaseMap+ AI have integrations with Lex Machina and complement it, and Protégé itself is embedded in the product. An API is offered and described as a route for customers to build their own solutions using the underlying litigation data, linked to a community article. What is not published is any integration into the systems litigation work otherwise lives in: no document management, no matter management, no billing, no filing system and nothing outside the LexisNexis family. Nor is there documentation an implementer could use, since neither the API article nor any developer index was opened, and nothing on the page describes what any integration moves or in which direction.
One real integration route, documented at a high level, and nothing else. Trellis publishes an API with its own support documentation, described as providing access to state and federal court data that was previously siloed and fragmented and as available for integrating Trellis directly into a customer's workflow. That is a genuine route for a firm building its own tooling. What is not published is integration into the systems litigation work otherwise lives in: no document management, no matter management, no practice management, no filing system and no named third-party connector of any kind. Nor was implementer-level documentation reached, since only the API overview was retrievable and it describes availability rather than what the endpoints return or how authentication and rate limits work.
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.
Nothing is published on where the software runs or where customer data sits. The product page, read in full including its data security, privacy and governance section, says only that LexisNexis partners with trusted enterprise cloud providers, naming no provider, region, jurisdiction or tenancy model. Nothing addresses residency options or processing location, or separates storage from processing. The LexisNexis Trust Center was not opened and may say more.
Nothing published on where the software runs or where customer data sits. No hosting provider is named, no tenancy model is stated, no region or residency option is offered, and nothing distinguishes processing from storage. The single geographic statement located runs the other way and is a restriction rather than an option: the terms of service state that, to comply with local privacy, data protection and other laws, the customer may not access Trellis outside the United States. That tells a buyer where they may use the product, not where their data lives.
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.
A trust center exists, and the product page names no standard. It says LexisNexis systems meet the highest standards for information security and that the company has regular third party audits to keep industry leading certifications, and links to trust.lexisnexis.com. No certification is named, so a reader cannot tell whether that means SOC 2, ISO 27001 or something else, and no auditor, period, scope or report route appears. The trust center is openly linked and is a SafeBase style portal; it was not opened, so whether reports are released on registration or only through sales is unknown. Assurance language without a named standard is weaker than a badge without scope.
No independent security attestation is published in vendor material. No certification, auditor, scope, coverage period or penetration test is named, and there is no trust center or portal. A third party blog says the company follows SOC 2 security standards, but nothing from the vendor confirms it. A different, unrelated company also trading as Trellis, at trelliscare.app, publishes a detailed healthcare security page covering HIPAA, business associate agreements and zero data retention; it is not this vendor.
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.
Nothing is published about the model supply chain a customer inherits. The product page, read in full, names Protégé as the AI assistant and calls the extraction technology proprietary, but identifies no model provider, model or version, architecture, processing location for the model layer or subprocessor list, and commits to no notice of changes. Naming a branded assistant and proprietary technology does not say what sits underneath.
Nothing published about the model supply chain a customer inherits. The vendor's own product announcement states that Trellis AI leverages its data foundation along with advanced language models, which acknowledges that language models are involved and identifies none of them. Searched the home page, that announcement, the API documentation, the knowledge base index, the terms of service and the privacy policy: no provider is named, no model or version is identified, no architecture is described, no processing location is given, no subprocessor list exists, and nothing commits the vendor to notifying customers when any of it changes. The privacy policy's reference to obligations to content and technology providers acknowledges third parties in the chain without naming one.
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.
No pricing information is published at any level, including the unit of charge. Searched the product page in full on 31 Aug 2026, including its on-page navigation covering features, audience, resources, testimonials and related products. There is no pricing page, no tier structure, no per-seat or per-practice-area unit, no volume banding and no statement of what implementation adds. Every call to action is a demo request, and the demo form is the only route offered. Nothing published would let a prospective buyer form any view of cost before entering a sales process, which is notable on a product sold to segments as different in scale as AmLaw firms, solo practitioners, law schools and state government.
Pricing is published for part of the range, and the firm tier is withheld. The vendor's knowledge base page on subscription tiers, read, sets out four plans. Three have monthly and annual rates: Personal at $69.95 a month or $649.95 a year, Research at $129.95 a month or $1,099.95 a year, and Research and Judge Analytics at $199.95 a month or $1,999.95 a year. Each comes with an annual allowance of content views, 240 on Personal and 900 on the two Research tiers. All three cover a single state, and Judge Analytics separates the top published tier. The fourth plan, Law Firm and Academia, has no price and leads to a sales form. Documents are charged separately: some are included, some can be requested free within a monthly limit in Los Angeles and Cook counties, and others are bought, with a deferred billing add-on for enterprise document purchases. The tier a serious firm would buy is the one without a number, and nothing says what implementation or onboarding adds.
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.
Coverage is stated in detail on both who it serves and what it covers. Eight audiences are named: large law firms, small law firms, corporate legal departments, insurers, courts and judges, state and local government, federal government, and law schools, including government and court use. Coverage is equally precise and states its limits: all 94 federal district courts, the 13 courts of appeal, the PTAB and specialty venues, with state coverage described as enhanced state courts rather than complete and a separate pool of 18 million state cases supporting party analytics only; appeals analytics limited to federal civil cases filed since 2012; motion metrics limited to more than 40 motion types; and the whole claim dated as of April 2025. Published litigation reports on patent, class action and administrative law show practice depth.
Coverage is quantified precisely and the audience is segmented in detail, with the boundaries stated only in part. The vendor publishes coverage as more than 3,000 courts across over 2,500 counties spanning 45 states, and the API documentation and plans navigation add federal circuits, bankruptcy courts and the Supreme Court. Audience segmentation runs to role rather than just firm type, with published pages for associates, partners, knowledge management and marketing or business development inside law firms, plus in-house legal, and the customer evidence adds academic law libraries. Practice coverage is expressed concretely through motion type and legal issue pages and through published treatises and primers on how common motions are handled at trial court level. Two limits are stated: access is restricted to the United States by the terms of service, and the data may not be used for Fair Credit Reporting Act eligibility determinations. What is not stated is which of the 45 states are covered to what depth, and the state-only positioning used in much of the marketing sits awkwardly beside the federal coverage the API page claims.
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?
The product page states plainly that LexisNexis never uses customer data to train AI models, with no exception for internal or third party models or aggregated data. It adds that identifiable information is removed from AI interactions so performance can be improved without compromising privacy, which shows that interaction data is used for improvement in deidentified form. The commitment recorded here is a published policy statement rather than a contract term.
No located term or policy says whether user queries, saved research or documents shared through the platform train any model. The privacy policy says Trellis does not sell customer or user data and limits disclosure of usage data to unaffiliated third parties, which is a different question. The product announcement confirms that advanced language models are used, without saying what they are trained on. The terms of service and privacy policy could be read only in fragments, so a complete read of either may say more.
Also checked the home page, the Trellis AI announcement, the API documentation and the knowledge base index on 31 Aug 2026.
Prompt and Output Retention
How long does the product keep what a lawyer typed, and can that be set to zero?
Retention is acknowledged without a period. The product page states that LexisNexis maintains robust data retention and deletion policies, and separately that identifiable information is removed from AI interactions, which addresses the identifiability of prompt data without addressing how long it is kept. Searched the page in full on 31 Aug 2026 and located no retention window for queries, prompts or generated output, no customer-configurable setting and no zero-retention option. The LexisNexis Trust Center and privacy policy are both linked and were not opened.
No located material says how long search queries, saved research, case assessments or documents shared through the platform are kept. The privacy policy covers canceling a registration in account settings, which is an account action rather than a retention period, and no deletion timeline follows from it. No retention setting the customer controls and no zero retention option was located. Both legal documents could be read only in fragments, so a complete read may say more.
Searched the home page, features page, product announcement, API documentation, knowledge base index, blog, terms of service and privacy policy on 31 Aug 2026.
Ethical Walls and Matter Segregation
Does retrieval respect the firm’s ethical walls, or can the model read across them?
Research trails, the material at risk on this product, have no described separation between customers. No public material addresses segregation between customers or access control within a customer organization, and no tenancy or permission model is described. The question lands differently on this product than on a document platform: Lex Machina is a shared analytics database built from public court records rather than a repository of a customer's own matter files, so the material at risk is the customer's queries and research trails rather than client documents. Nothing published addresses how those are separated either.
No boundary is described around search histories or the rulings users share through the platform. No public material addresses separation between customers or access control within a subscribing firm, and no tenancy or permission model is described. The product is a shared database of public records rather than a repository of client files, so the material at risk is the firm's own research activity and anything shared internally.
Two features make that concrete and unaddressed: search histories reveal litigation strategy, and Trellis Envelopes is described in the knowledge base as an in-app messaging service for sharing rulings, dockets and documents between users, with no published statement of who inside or outside a firm can see what.
Third Party Request and Subpoena Notice
If someone subpoenas the vendor for a firm’s data, does the firm hear about it first?
The commitment sits in the LexisNexis General Terms and Conditions effective 5 June 2026. Section 12.1(b)(v) allows compelled disclosure but requires the receiving party to give advance notice so the other can seek a protective order. The duty is mutual and covers what matters here, since Subscriber Confidential Information in 12.1(d) includes client or customer names, work product and other nonpublic proprietary information.
No transparency report or count of requests was found on the terms page, product page or LexisNexis Trust Center as of 31 Aug 2026. The commitment is in the parent's general agreement, not anything Lex Machina publishes under its own name.
The privacy policy addresses disclosure without addressing notice. It states that Trellis does not disclose customer data about a user's activity to unaffiliated third parties except as necessary to service the account, to enforce the terms of use, to meet obligations to content and technology providers, or as required by law. The final limb permits compelled disclosure and carries no commitment to tell the customer it has happened, no undertaking to seek a waiver where notice is prohibited, and no minimization obligation.
No transparency report was located on 31 Aug 2026. Whether a firm learns that its research history has been demanded therefore rests with the vendor.
Primary Law Corpus Provenance
Where does the law in this product come from, and does the vendor have the right to use it?
Sources are identified court by court: all 94 federal district courts, the 13 courts of appeal, the PTAB, specialty venues, a set of enhanced state courts, and a further 18 million state cases for party analytics. Scale is 45 million documents across more than ten million cases, dated as of April 2025. No rights basis is published for any of the material, whether license, public record or PACER terms, and no update schedule or lag is given beyond that single date.
The data is public court records, identified by scale and jurisdiction: more than 3,000 courts in over 2,500 counties across 45 states, with federal circuit, bankruptcy and Supreme Court material through the API. A document on rights does exist. The terms of service say a Public Records Policy governs use of the services and is incorporated into any subscription agreement. That policy was not read on 31 Aug 2026, so what it says about the basis for using the records is unknown, and it may say more. No update cadence or data date was located.
Good Law Verification
Does the product tell you when the authority it just cited has been overruled?
Shepard's sits with the parent, not with Lex Machina. Nothing addresses whether authority surfaced through the platform is checked for subsequent history, and no citator or treatment signal is claimed for Lex Machina itself. The product reports what courts did rather than whether a proposition remains good law, so the question is adjacent rather than central. Worth recording that the parent operates Shepard's Citations and that the sister product Lexis+ is described elsewhere as running citations through it, but no such check is claimed for Lex Machina on its own page.
Appeals analytics do surface reversal rates and outcomes from rehearings and Supreme Court decisions, which is case-outcome data rather than a currency check on cited authority.
Trial court rulings, the platform's core material, are generally not citable precedent, and they come with no currency signal. Nothing addresses whether authority surfaced through the platform is checked for subsequent history, and no citator or treatment signal is claimed. The question sits oddly on this product: Trellis surfaces state trial court rulings, which are generally not citable precedent, and its own value proposition is empirical rather than doctrinal, showing how a named judge has ruled rather than what the law is. A user relying on a trial court ruling as persuasive authority would get no currency signal from the platform.
Refusal and Uncertainty Behavior
What does the product do when the answer is not in the corpus?
Protégé has no described way to say the data does not support a prompt. No explicit no-answer or abstention path is documented and no confidence or grounding score was located. Nothing states what Protégé does when a prompt asks for analytics the underlying data does not support, which matters on a product whose own coverage statement is bounded, with state courts described as enhanced rather than complete and appeals data starting in 2012.
A user prompting for a state judge outside the enhanced set has no published indication of whether the system will say so.
Thin data for a given judge, county or motion type produces no described warning in a case assessment. No explicit no-answer or abstention path is documented and no confidence or grounding score was located. Nothing states what a case assessment returns when the underlying data is thin for a given judge, county or motion type, which matters on a product whose coverage is explicitly partial at 45 of 50 states and uneven across more than 2,500 counties.
A user cannot tell from published material whether a sparse judge profile reflects a judge who rules a certain way or a court Trellis has not fully ingested.
Fabricated Citation Record
Does a public court record exist addressing fabricated or hallucinated legal citations in output from this product?
No court order, opinion or disciplinary record naming this product has been located as of 31 Aug 2026. Instrument searched: the AI Hallucination Cases database maintained by Damien Charlotin, which tracks decisions worldwide where a court addressed hallucinated AI content and records the tool implicated where known, searched on the product name alongside 2026 sanctions trackers and trade press summaries. This is a statement about the public record on the date shown rather than a clearance, and it is bounded by what that database covers.
The product returns structured analytics about real dockets rather than generating citations to authority, so its output does not ordinarily take the form of a citation in a brief.
No court order, opinion or disciplinary record naming this product has been located as of 31 Aug 2026. Instrument searched: the AI Hallucination Cases database maintained by Damien Charlotin, which tracks decisions worldwide where a court addressed hallucinated AI content and records the tool implicated where known, searched on both the product name and the company name Trellis Research, alongside 2026 sanctions trackers and trade press summaries.
Searching on this name required care, because at least one unrelated company trades as Trellis; nothing returned related to this vendor. This is a statement about the public record on the date shown rather than a clearance, and it is bounded by what that database covers.
Bar Guidance Alignment
Has the vendor engaged in public with the ethics opinions its buyers are bound by?
A corporate responsible AI framework stands where professional conduct guidance would be. No engagement with any bar or ethics guidance was located, including ABA Formal Opinion 512 and any state bar material. What is published in its place is a corporate AI governance framework, the RELX Responsible AI Framework, which addresses the vendor's own development practices rather than the professional obligations binding the lawyers who use the product.
Notably absent given that the platform profiles named judges and is marketed to courts and judges themselves as a customer segment.
The Fair Credit Reporting Act is the one regulatory framework in view, and lawyers' professional obligations sit outside it entirely. No engagement with any bar or ethics guidance was located, including ABA Formal Opinion 512 and any state bar material. The only regulatory framework the vendor engages with in published material is the Fair Credit Reporting Act, invoked in the terms of service to prohibit using the data for consumer eligibility determinations, which governs the vendor's position as a data provider rather than the professional obligations of the lawyers using it.
Billing and Fee Posture
Does the vendor address what happens to the bill when the work takes an hour instead of six?
The product keeps no per matter record of AI assisted work for fee purposes and offers no guidance on billing or client disclosure. The vendor does not lead on time saved: its value claims concern case strategy, risk management, outcome prediction, pitching for work and choosing outside counsel. It is marketed to corporate legal departments as a tool for assessing outside counsel, the other side of the billing question, and nothing addresses what a firm should disclose about its own use.
Public materials focus on research time saved and better work: writing better motions faster by using prior rulings, spending less time on academic research and more on strategy, and a named customer describing the platform as saving countless hours. The blog adds that companies can monitor litigation without frequent updates from costly outside counsel. No record of AI assisted work by matter for fee purposes and no guidance on billing, fees or client disclosure was located. Searched those pages plus the features page, knowledge base index, terms of service and privacy policy on 31 Aug 2026.
Outside Counsel Guideline Readiness
Can a firm get this vendor through a client’s AI clause without a bespoke negotiation?
A trust center is linked openly from the product page at trust.lexisnexis.com and is the stated route to security, privacy and compliance material, which is a real access route rather than a sales conversation. Nothing a firm could forward to a client was located on the product surface itself: no subprocessor list, no model provider identified, no consent or notification material, and no named certification. The training commitment that a firm would most want to pass on, that customer data is never used to train AI models, is published and quotable.
The trust center was not opened on 31 Aug 2026, so whether it fulfills on registration or requires a sales conversation was not established; this reflects the material reachable on the product page.
Language models are confirmed in use, yet no provider is named for a firm to pass on to a client. Nothing that would support a client-side disclosure obligation was located: no subprocessor list, no model provider identified despite the vendor confirming that advanced language models are in use, no trust center or portal, no named certification, and no client-facing consent or notification material. The privacy policy refers to obligations to content and technology providers, which acknowledges third parties in the chain without naming any of them or describing what they receive.
Court Disclosure Support
If a judge’s standing order requires an AI disclosure, can the product produce one?
Court disclosure, AI use certification and a producible verification record all go unaddressed for this product. No model is identified anywhere, so which system produced a given output could not be established from vendor material, and no export designed for that purpose is described. The product supplies analytics that inform strategy rather than text that enters a filing, so a judicial standing order is less likely to bite on its output directly, though a brief citing its statistics would still leave the lawyer certifying figures whose provenance the platform does not package for disclosure.
Nothing was located on court disclosure, AI use certification or producing a verification record. No model is identified, so which system produced an assessment cannot be established, and no export for that purpose is described. That matters here because the product helps draft arguments, and its judge statistics are the kind of figure a lawyer might put in a brief, leaving the certifying lawyer to reconstruct sources the platform does not provide.
Searched the home page, features page, product announcement, API documentation, knowledge base index, blog, terms of service and privacy policy on 31 Aug 2026.
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.
- UPL and Professional Responsibility Posture
- Deployment Model and Data Residency
- Model Supply Chain Disclosure
- Ethical Walls and Matter Segregation
- Good Law Verification
- Refusal and Uncertainty Behavior
- Bar Guidance Alignment
- Court Disclosure Support
Which one fits
Choose Lex Machina if
- Your matters are federal and you want the coverage stated with its edges. Lex Machina publishes all 94 federal district courts, the 13 courts of appeal, the PTAB and specialty venues, describes its state coverage as enhanced state courts rather than complete, bounds appeals analytics to federal civil appeals filed since 2012, and carries an explicit as of date of April 2025 on the whole coverage claim.
- You want to know how the data was made. Lex Machina describes proprietary machine learning combined with attorney review: AI reads filings, extracts findings, damages awards, resolutions and timelines, reads signature blocks to identify and group firms, attorneys and parties, and fills gaps the docket leaves open, with lawyers checking the output before it becomes data, across modules covering state court motion metrics, timing events, legal findings and class action analytics.
- You need positions you can point a committee at. Lex Machina states that customer data is never used to train AI models, follows the RELX Responsible AI Framework whose published commitments include avoiding unfair bias and human oversight, and its governing general terms carry an intellectual property indemnity with named conditions and remedies alongside a stated liability cap.
Choose Trellis if
- Your cases are in state trial courts, where the record has never been searchable. Trellis aggregates dockets, rulings, motions and filed documents from more than 3,000 courts across over 2,500 counties in 45 states and makes them searchable in one place, with judge analytics covering ruling tendencies, career history and evidentiary preferences, verdict data, daily reports on new filings and customizable alerts.
- You want to know the price before you commit. Trellis publishes four plans, three with figures: Personal at 69.95 dollars a month or 649.95 a year, Research at 129.95 or 1,099.95, and Research with Judge Analytics at 199.95 or 1,999.95, each with a stated annual content view allowance of 240 or 900 and single state coverage, with the law firm and academia tier routing to sales.
- You want guidance alongside the data. Trellis publishes treatises and primers on how common motions and legal issues are actually handled at trial court level, sitting next to the rulings themselves, and offers an API providing access to state and federal court data for a firm building its own tooling.
In summary
Lex Machina
Lex Machina is a litigation analytics platform that turns raw court records into structured data about how judges, courts, counsel, parties and expert witnesses have behaved. It combines proprietary machine learning with attorney review: AI extracts findings, damages and timelines, and lawyers check the output before it becomes data. According to the AI Legal Index, its case rests on coverage. It publishes all 94 federal district courts, the 13 courts of appeal and the PTAB, states its state coverage as enhanced rather than complete, and dates the whole claim to April 2025. It publishes no accuracy figure, no model provider, no hosting statement and no price.
Trellis
Trellis is a legal research and analytics platform built around state trial court records, the layer of the United States court system that has historically been the least searchable. It aggregates dockets, rulings, motions and filings from more than 3,000 courts across over 2,500 counties in 45 states. It adds judge analytics, verdict data, daily filing reports and treatises on how motions are handled at trial level. The AI Legal Index records published figures for three of its four subscription plans, from 69.95 dollars a month, each with a stated content view allowance. Trellis publishes no security attestation, no model provider and no AI governance position.
Questions buyers ask
Lex Machina vs Trellis: which one do you need?
Often both, which is why they are compared rather than chosen between. Lex Machina is federal, covering all 94 district courts, the 13 courts of appeal and the PTAB, with state coverage it describes as enhanced rather than complete. Trellis is state trial, covering more than 3,000 courts across 2,500 counties in 45 states, the layer of the system that has historically been least searchable. From the AI Legal Index, based on each vendor's own published materials as of September 3, 2026. No vendor pays for placement.
What does each cost?
Trellis publishes figures for three of its four plans, from 69.95 dollars a month or 649.95 a year for Personal up to 199.95 or 1,999.95 for Research with Judge Analytics. Each carries an annual content view allowance and single state coverage, with document access charged separately in part. The firm and academia tier carries no figure. Lex Machina publishes no price, range, tier or unit of charge at any level, and every route is a demo request. From the AI Legal Index, based on each vendor's own published materials as of September 3, 2026. No vendor pays for placement.
How is the underlying data made?
Lex Machina describes machine learning reading filings and extracting findings, damages, resolutions and timelines, with attorney review checking the output before it becomes data. It states coverage of more than ten million cases, dated to April 2025. Trellis aggregates and structures dockets, rulings and filings from state trial courts and applies language models on top for case assessments. Neither publishes an accuracy figure for the extraction each depends on. From the AI Legal Index, based on each vendor's own published materials as of September 3, 2026. No vendor pays for placement.
Do either publish how accurate the analytics are?
Neither does. Lex Machina describes its API offering as the industry's most accurate litigation data and publishes no rate, benchmark or test set behind it. Trellis describes precise insights from advanced language models, and its terms of service separately disclaim liability for the omission or inaccuracy of court provided data. On products whose output is factual assertions about how named judges have ruled, a wrong one is not visible to the reader, which is what makes the absence matter. From the AI Legal Index, based on each vendor's own published materials as of September 3, 2026. No vendor pays for placement.
What do Lex Machina and Trellis both leave unpublished?
Neither names the model or the provider behind its AI layer. Neither publishes a hosting region, a tenancy model or a residency option. Neither states that its output is not legal advice or engages any bar or ethics guidance, which both products invite by returning predicted outcomes, recommended actions and settlement guidance to lawyers. And neither publishes a retention period for what a user searches, which on a litigation product reveals strategy. From the AI Legal Index, based on each vendor's own published materials as of September 3, 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.
Both products build analytical profiles of named judges, and neither publishes any evaluation of them. Lex Machina states that it follows a framework whose commitments include avoiding unfair bias, and discloses no testing, method or result. Trellis publishes no governance position at all. Its knowledge base states that judge pages carry career history and political affiliation alongside ruling tendencies. Two documents on the Trellis property also describe its data differently. A vendor blog states that the platform relies solely on publicly available filings and does not handle personally identifiable information or confidential case details. The privacy policy describes collecting personal information, and the product ships an in app feature for sharing rulings, dockets and documents between users. Neither vendor reviewed this page.