Lex Machina vs Trellis: how they compare in 2026

The machine learning is the mechanism that manufactures the asset, on a product whose value to the buyer is the resulting database. Lex Machina states that proprietary technology and AI-assisted attorney review converts raw legal documents into comprehensive data sets and fills gaps in court records, and that proprietary AI analyses documents including reading signature blocks, updating case data and grouping related entities. Without those models the structured analytics would not exist in usable form. What holds this at B rather than A is that the thing sold is a queryable dataset rather than model output: a buyer pays for coverage and accuracy of the analytics, and the generative layer arrives as Protégé sitting over the top rather than as the product itself.

Machine learning is what makes the underlying record set usable, on a platform whose base offering is search over public court data. The vendor describes Trellis AI as combining its data foundation with advanced language models to produce case assessments that examine facts, claims and defences and return potential outcomes, recommended actions and risk factors, alongside argument drafting and generation. Underneath that sits a docket search engine over 3,000-plus courts, which is 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, which is the B band rather than the A.

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 on 31 Aug 2026 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 was located on any surface read on 31 Aug 2026. 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.

A real review point is published for the data pipeline and nothing is published for the generative layer. The product describes AI-assisted attorney review as the method by which raw documents become data, which places qualified lawyers in the loop between model output and what a customer sees, and the RELX Responsible AI Framework the vendor commits to includes human oversight as a named principle. That is a written commitment with a real review surface. What is absent, checked 31 Aug 2026, is the rest of the control structure: nothing states what the extraction models decide alone, what proportion of output an attorney reviews or against what threshold, what happens when an error is found in published analytics, or what oversight applies to Protégé when a user reaches the data through a prompt.

Autonomy is claimed and no oversight mechanism is published. Trellis AI is described as examining case facts, legal claims and defences to deliver insights into potential outcomes, recommended actions and risk factors, with each assessment delivering actionable intelligence and recommended next steps to inform decisions on case management, settlement strategy and trial preparation, alongside argument drafting and generation. Nothing published states what the system produces unattended, where a lawyer must review before relying on an assessment, or what happens when an output is wrong. Searched the home page, the vendor's product announcement, the API documentation, the knowledge base index, the terms of service and the privacy policy on 31 Aug 2026. A vendor recruitment listing describes contract attorneys labelling verdict types and monetary awards through custom software to ensure data accuracy, which suggests human review in the data pipeline, but a job advert is not product material and does not carry a grade.

Real adoption evidence with a cited basis, and testimonials that thin out under inspection. The adoption claim is unusually well footnoted for this market: trusted by over 90 per cent of the largest firms, with the basis given as the 2025 Law360 Pulse Leaderboard Report and the 2025 AmLaw100 Report and the underlying data dated to April 2025. Against that, only one of the three testimonials is a named practitioner at a named firm, John Johnson, a partner at Fish & Richardson, and his quote carries no figure. The second is attributed only to a Chief IP Litigation Counsel at an unnamed Fortune 500 company. The third is from Miriam Rivera, described as former Deputy General Counsel at Google, and is framed hypothetically, saying what she would do if she were at Google today, which is a statement of opinion rather than evidence of a deployment. No case study with figures for what changed was located.

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 was located, and no case study with a stated method was found on the surfaces read on 31 Aug 2026.

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 own material sits against it. The claim, published on the Trellis blog, is that data confidentiality is a top priority, that the platform relies solely on publicly available legal filings, that this eliminates the need for users to input sensitive client or internal data, and that Trellis does not handle personally identifiable information or any confidential case details. That is a real mitigating position and it is why this is not lower. Three things on the other side, all from vendor material. The privacy policy describes collecting personal information and limits disclosure of usage data to unaffiliated third parties except as necessary to service the account, enforce the terms, meet obligations to content and technology providers, or as required by law, which is a general assurance rather than a commitment on what the product holds. The knowledge base states that judge pages carry career history and political affiliation, so the platform does handle identifying information about named individuals, albeit public officials rather than customers. And Trellis Envelopes is described as an in-app messaging service for sharing rulings, dockets and documents between users, which is a channel for exactly the internal material the claim says users never need to input. The graded position is the documented one: no statement on whether user queries or shared material train any model, no retention period, no segregation or access model, and no privilege or work product treatment, on a product where what a litigator searches reveals strategy. Checked 31 Aug 2026.

Nothing published on the advice line was located. Searched the product page in full on 31 Aug 2026, including the data security, privacy and governance section and the footer. No statement that the analytics do not constitute legal advice, no professional responsibility or ethics page, no bar or ethics guidance named including ABA Formal Opinion 512, and no jurisdiction limits. The product predicts outcomes and forecasts the chances of success of a motion, which is squarely the kind of output a lawyer must exercise independent judgement over, and the marketing invites exactly that use without addressing it. Exposure is lower than for a consumer-facing tool because the buyer is a lawyer, but the axis asks what the vendor has published and the answer is nothing. The LexisNexis general terms were not opened, so this is rebuttable.

Nothing published on the advice line. Searched the home page, the vendor product announcement, the knowledge base index, the terms of service and the privacy policy on 31 Aug 2026. No statement that Trellis output does not constitute legal advice, no professional responsibility or ethics page, no bar or ethics guidance named including ABA Formal Opinion 512, and nothing addressing a lawyer's competence or supervision duties when relying on a case assessment that returns recommended actions and settlement guidance. The terms do carry one significant published use restriction, but it is regulatory rather than professional: no user may use the data to determine a consumer's eligibility for credit, insurance or a government licence or benefit, with consumer defined by reference to the Fair Credit Reporting Act at 15 USC 1681. That addresses the vendor's exposure as a data provider, not the lawyer's obligations.

A named governance framework that addresses bias explicitly, short of a named owner and any published result. The vendor states that LexisNexis follows the RELX Responsible AI Framework and enumerates its commitments: that its AI protects privacy, is transparent and explainable, avoids unfair bias, includes human oversight, and is designed for real-world impact. Naming bias as a governed dimension puts this ahead of most of the pull, where bias goes unmentioned. What is missing is everything downstream of the principle: no individual or role inside the vendor is identified as accountable for model behaviour, no pre-release testing regime is described, no evaluation method is published, and no finding about uneven output across case types, courts, parties or populations has been disclosed. On a product that profiles named judges and named counsel, published bias testing would matter more here than almost anywhere else on this index.

No governance position for model behaviour was located. Searched the home page, the vendor's Trellis AI product announcement, the API documentation, the knowledge base index, the terms of service and the privacy policy on 31 Aug 2026. Nothing names an accountable owner, describes pre-release testing, publishes a responsible AI framework, or discloses anything about uneven output. The absence is conspicuous on this product in particular: the platform builds analytical profiles of named individual judges, and the knowledge base states that judge pages carry career history and political affiliation alongside ruling tendencies. A product that scores identifiable public officials and surfaces their political affiliation is the clearest case on this index for published bias testing, and none exists.

Principles are published where controls should be. The product page offers privacy by design applied at every stage, protection of customer data wherever stored, sent or accessed, advanced encryption, enterprise-grade cloud providers, continuous monitoring, regular third-party audits and robust data retention and deletion policies. Every one of those is an assurance rather than a specification. Searched the page in full on 31 Aug 2026 and located no retention period, no named subprocessor, no incident or breach notification practice, no encryption standard and no access control model. The one concrete statement is that identifiable information is removed from AI interactions. A LexisNexis Trust Center is linked openly at trust.lexisnexis.com and is credited here as a genuine access route rather than scored against the vendor; it was not opened, so this grade is rebuttable on its contents.

A general privacy policy covers the website and account relationship without addressing what happens to what a user does inside the product. The policy states that 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 through settings. Searched the home page, features page, product announcement, API documentation, knowledge base index, blog, terms of service and privacy policy on 31 Aug 2026 and located no security page of any kind: no encryption standard, no access control model, no retention period, no named subprocessor, no incident or breach notification practice, and no deletion commitment beyond account cancellation. The vendor does argue the exposure is low, stating in its own blog that the platform relies solely on publicly available filings and does not handle personally identifiable information or confidential case details, which is a real mitigating position but sits in tension with a privacy policy that describes collecting personal information and with an in-app feature for sharing documents between users.

Written 31 Aug 2026 as an R7 amendment; left unwritten in the original build because the governing agreement had not been opened and section 8 puts this axis's evidence solely there. Surfaces read 31 Aug 2026: lexmachina.com/terms/, which resolves to the LexisNexis product page rather than to any Lex Machina agreement, and the LexisNexis General Terms and Conditions effective 5 June 2026. The scope connector is the GTC's own opening line, which states that the terms govern use of any LexisNexis product or service linking to them, together with the absence of any separate Lex Machina agreement in market. What is published is real and specific. Section 7.1 gives an indemnity against third-party claims of patent, trademark, service mark, copyright or trade secret infringement, with five named conditions and three remedies, the last being termination with a pro-rata refund, and 7.2 makes it the sole and exclusive remedy. Section 6.3 caps aggregate liability at the lesser of actual direct damages or fees paid in the preceding twelve months, which is more restrictive than the plain twelve-month cap common in this corpus, and states that those damages are in lieu of all other remedies. Section 6.4 excludes consequential damages with four named carve-outs. What keeps it off A is that the indemnity reaches infringement and nothing reaches output: section 6.1 disclaims liability for loss resulting in any way from errors or omissions in the Online Services or Materials, section 5.2 provides them as is with all other warranties disclaimed, and section 4.3(a) states that AI systems may not be accurate or error-free and that users are responsible for verifying. No insurance position is named. On a litigation analytics product sold for case assessment, the error-and-omission disclaimer is the clause a buyer should 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 was located, and the cap was not among the text retrievable on 31 Aug 2026. A separate subscription agreement is referenced by the terms and was not located, so the full allocation may sit there.

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 was not located on 31 Aug 2026 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 was not located on 31 Aug 2026 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.

Nothing published on where the software runs or where customer data sits. Searched the product page in full on 31 Aug 2026, including the data security, privacy and governance section. The only statement touching infrastructure is that LexisNexis partners with trusted, enterprise-grade cloud providers, which names no provider, no region, no jurisdiction and no tenancy model. No multi-tenant or single-tenant statement, no residency option, no processing location, and nothing distinguishing where data is stored from where it is processed. The LexisNexis Trust Center is the reachable route to any of this and was not opened, so the grade is rebuttable on its contents; nothing on the product surface a buyer reads first answers the question.

Nothing published on where the software runs or where customer data sits. Searched the home page, the product announcement, the API documentation, the knowledge base index, the terms of service and the privacy policy on 31 Aug 2026. 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.

A trust centre exists and the product page names no standard at all. The security section states that LexisNexis systems meet the highest standards for information security, that the company undergoes regular third-party audits to maintain industry-leading certifications, and links to a trust centre at trust.lexisnexis.com. No certification is identified by name anywhere on the page, so a reader cannot tell whether that means SOC 2, ISO 27001 or something else, and no auditor, coverage period, scope or report route appears. The trust centre is openly linked and is credited as a genuine access route under the retrieval rules; it was not opened on 31 Aug 2026 and is a SafeBase style portal, so whether it fulfils on registration or requires a sales conversation was not established. This sits at C because assurance language without a named standard is weaker than a badge with no scope.

No independent security attestation was located in any vendor material. Searched the home page, the features page, the vendor's Trellis AI product announcement, the API documentation, the knowledge base index, the vendor blog, the terms of service and the privacy policy on 31 Aug 2026, and ran a targeted search for a Trellis security or trust page. No certification is named, no auditor identified, no scope or coverage period given, no penetration test referenced and no trust centre or portal exists. A third-party blog asserts that the company follows SOC 2 security standards, but no vendor-published material corroborates it and an unverified secondary source cannot support this axis. One trap worth recording for any future grader: a different and 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 and must not be read across.

Nothing published about the model supply chain a customer inherits. Searched the product page in full on 31 Aug 2026. Protégé is named as the AI assistant delivering generative analytics and the extraction technology is described as proprietary, but no model provider is identified, no model or version is named, no architecture is described, no processing location is given for the model layer, no subprocessor list exists, and nothing commits the vendor to notifying customers when any of it changes. Referring to proprietary technology and a branded assistant without saying what sits underneath is the same shape recorded against Paxton AI in pull 1 and graded the same way here.

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 on 31 Aug 2026: 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.

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.

Written 31 Aug 2026 as an R7 amendment. The original build left this unwritten rather than graded D, because trellis.law/plans was bot-blocked and the only figures available came from third-party directories that contradicted each other. That hold was correct: D would have asserted no pricing is published at any level, and the vendor publishes a great deal. The figures were recovered from Trellis's own knowledge base at support.trellis.law/what-are-the-different-tiers, fetched 31 Aug 2026, which sets out four subscription plans. Three carry published 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. The unit is stated as well as the price, each tier carrying an annual content view allowance, 240 on Personal and 900 on the two Research tiers, with single state coverage on all three and Judge Analytics as the feature that separates the top published tier. The fourth plan, Law Firm and Academia, has no figure and routes to a sales contact form. Document access is a separate axis of charge, with some documents included in the subscription, some requestable at no additional cost in Los Angeles and Cook counties within a monthly limit, and others purchasable, and an enterprise deferred billing add-on exists for document purchases. That is real pricing across part of the range with the enterprise tier withheld, which is the B band. It falls short of A because the firm-level tier a serious buyer would actually purchase is the one without a number, and because nothing states what implementation or onboarding adds.

The most complete coverage statement read in this pull, on both who it serves and what it covers. The audience is enumerated across eight segments rather than gestured at: large law firms, small law firms, corporate legal departments, insurers, courts and judges, state and local government, federal government, and law schools. That includes the government and court use that this axis asks about and that almost nothing else in the pull addresses. Coverage is stated with equal precision and, unusually, with its limits: all 94 federal district courts, the 13 courts of appeal, the PTAB and specialty venues, but state coverage described as enhanced state courts rather than complete, with a separate pool of 18 million state cases supporting party analytics only; appeals analytics bounded to federal civil cases filed since 2012; motion metrics bounded to more than 40 motion types; and the whole coverage claim carrying an explicit as-of date of April 2025. Practice areas are evidenced through published litigation reports covering patent, class action and administrative law rather than merely claimed.

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 product page states flatly that LexisNexis never uses customer data to train AI models, with no carve-out for internal models, third-party models or aggregated data, which is a cleaner formulation than the qualified versions recorded elsewhere in this pull. It adds that identifiable information is removed from AI interactions so that performance can be improved without compromising privacy, which discloses that interaction data is used for improvement in de-identified form. No matching term was located in any agreement, since the LexisNexis terms of service were not opened on 31 Aug 2026, so the commitment recorded here is a published policy statement rather than a contractual one.

Read the terms of service and the privacy policy through the search index on 31 Aug 2026, together with the home page, the vendor's Trellis AI product announcement, the API documentation and the knowledge base index. No located term or policy addresses whether user queries, saved research or documents shared through the platform are used to train any model, either way. The privacy policy commits that Trellis does not sell customer or user data and limits disclosure of usage data to unaffiliated third parties, which is a different question. The announcement confirms that advanced language models are in use without saying what they are trained on. Both documents were retrieved in fragments rather than in full, so this value is rebuttable on a complete read of either.

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, so this value is rebuttable on either.

Searched the home page, features page, product announcement, API documentation, knowledge base index, vendor blog, terms of service and privacy policy on 31 Aug 2026. No located material states how long search queries, saved research, case assessments or documents shared through the platform are retained. The privacy policy addresses cancellation of a registration through account settings, which is an account action rather than a retention window, and no deletion timeline follows from it. No customer-configurable retention setting and no zero-retention option was located. Both legal documents were retrieved in fragments through the search index rather than in full, so this is rebuttable on a complete read.

Searched the product page in full on 31 Aug 2026, including the data security, privacy and governance section. No public material addresses segregation between customers or access control within a customer organisation, 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.

Searched the home page, the product announcement, the knowledge base index, the terms of service and the privacy policy on 31 Aug 2026. 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.

Written 31 Aug 2026 as an R7 amendment, on the LexisNexis General Terms and Conditions effective 5 June 2026, which was not opened in the original pass. Section 12.1(b)(v) carves compelled disclosure out of the confidentiality obligation but conditions it: the receiving party must give advance notice so the disclosing party can seek a protective order limiting or preventing disclosure to third parties. The obligation is mutual and it reaches the material that matters here, since Subscriber Confidential Information is defined at 12.1(d) to include client or customer names, work product and other non-public proprietary information. No transparency report or count of requests received was located on the terms page, the product page or the LexisNexis Trust Center, which is what separates this from the top value. Note that the commitment sits in the parent's general agreement rather than in 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 minimisation 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.

The corpus is identified court system by court system rather than described by jurisdiction alone: all 94 federal district courts, the 13 courts of appeal, the PTAB, specialty venues, a set of described enhanced state courts, and a further 18 million state cases supporting party analytics. Scale is quantified at 45 million customer-facing documents across more than ten million cases, and the whole claim carries an explicit as-of date of April 2025, which is more provenance discipline than most of this market shows. What is not stated is the rights basis: no licence, public record or PACER terms position is published for any of the underlying material, and no update cadence or lag is given beyond the single as-of date.

The corpus is public court records and is identified by scale and jurisdiction: more than 3,000 courts across over 2,500 counties in 45 states, with federal circuit, bankruptcy and Supreme Court material added through the API. Unusually for this pull, a rights-basis document exists: the terms of service state that a Public Records Policy governs use of the services and is incorporated by reference into any subscription agreement, alongside the privacy policy. That policy was not retrieved on 31 Aug 2026, so what it says about the licence or public domain basis for the underlying records could not be established, and no update cadence or date was located anywhere. Recorded at named-no-licence on that basis; a full read of the Public Records Policy could move it.

Searched the product page in full on 31 Aug 2026. 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.

Searched the home page, the vendor's product announcement, the API documentation, the knowledge base index, the terms of service and the privacy policy on 31 Aug 2026. 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.

Searched the product page in full on 31 Aug 2026. 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.

Searched the home page, the Trellis AI product announcement, the API documentation, the knowledge base index, the terms of service and the privacy policy on 31 Aug 2026. 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.

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.

Searched the product page in full on 31 Aug 2026, including the data security, privacy and governance section and the footer. 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.

Searched the home page, features page, product announcement, API documentation, knowledge base index, vendor blog, terms of service and privacy policy on 31 Aug 2026. 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.

Searched the product page in full on 31 Aug 2026 and located no per matter record of AI-assisted work intended for fee purposes and no published guidance on billing, fee or client disclosure treatment. Unusually for this pull, the vendor does not lead on time saved either: the published value claims are about decision quality and business development, covering case strategy, risk management, outcome prediction, pitching for work and selecting outside counsel. The product is in fact marketed to corporate legal departments as a tool for assessing and managing outside counsel, which is the other side of the billing question, and nothing addresses what a firm should disclose about its own use.

Public materials are framed around research time removed and better work product: writing better motions in less time by using prior rulings, spending less time on academic legal research and more on strategic insight, and a named customer describing the platform as saving countless hours and streamlining the firm's work. The vendor blog adds that corporate customers can monitor litigation without frequent updates from costly outside counsel. Searched those surfaces plus the features page, knowledge base index, terms of service and privacy policy on 31 Aug 2026 and located no per matter record of AI-assisted work intended for fee purposes and no published guidance on billing, fee or client disclosure treatment.

A trust centre 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 centre was not opened on 31 Aug 2026, so whether it fulfils on registration or requires a sales conversation was not established, and the value reflects the material actually reachable on the product page.

Searched the home page, features page, product announcement, API documentation, knowledge base index, vendor blog, terms of service and privacy policy on 31 Aug 2026. 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 centre 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.

Searched the product page in full on 31 Aug 2026 and located nothing addressing court disclosure, AI-use certification or the production of a verification record. 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.

Searched the home page, features page, product announcement, API documentation, knowledge base index, vendor blog, terms of service and privacy policy on 31 Aug 2026 and located nothing addressing court disclosure, AI-use certification or the production of a verification record. No model is identified anywhere, so which system produced a given assessment could not be established, and no export designed for that purpose is described. The gap has practical bite here: the product supports argument drafting and generation, and its judge statistics are the kind of figure a lawyer might put in a brief, leaving the certifying lawyer to reconstruct provenance the platform does not package.