Everlaw vs Relativity: how they compare in 2026

The models are the engine of a core capability layered on a platform that would function without them. Everlaw is an end to end ediscovery system covering upload, processing, search, review, production and trial preparation, and that platform existed and sold before the generative layer arrived in autumn 2024. What the models drive is substantial rather than peripheral, which is why this is not a C: Coding Suggestions automating first pass review, Deep Dive answering natural language questions across millions of documents, Review Assistant, Writing Assistant, custom extractions and deposition analysis. Fifth B on this axis. Worth recording for a reader that ediscovery has used machine learning for predictive coding long before generative AI, so the underlying platform was never model free, but the reviewable document system stands without the generative layer graded here.

The models are the engine of a core capability layered on a platform that would function without them. RelativityOne is an end to end ediscovery system covering legal hold, preservation, collection, processing, review, production and analytics, and it has sold for two decades. The aiR suite of five generative products sits on top of that and drives substantial capability rather than peripheral features, which is why this is not a C. Worth recording precisely because it distinguishes this record from the AI native vendors: technology assisted review has existed in this platform for years, so the generative layer is the newest of several model based capabilities rather than the first, and third party analysis characterises the aiR features as evolutionary within that lineage. Sixth B on this axis.

Grounding is real and documented with the retrieval architecture described, short of published figures. The mechanism is stated concretely: Deep Dive is powered by a vector database holding embeddings of the customer's own documents, answers are synthesised from facts extracted from specific documents, and results cite source documents with access to the underlying materials so users can verify outputs when checking their work. The vendor states plainly that it has taken steps to reduce hallucinations. Coding Suggestions is claimed to deliver recall and precision that rivals eyes on review, which uses the right metrics for the task and is the correct frame for first pass review quality. Searched the product pages, the AI Assistant framework page, the support knowledge base and the vendor blog on 29 Aug 2026 and located no numeric recall or precision figure, no accuracy rate, no test set, no published evaluation methodology and no independent benchmark participation. A claim to rival eyes on review with no measurement attached is exactly the kind of claim this axis exists to mark.

Grounding is real and documented through an explainability mechanism, short of published measurement. The vendor states that aiR for Review surfaces impactful content backed by transparent rationale and that aiR for Privilege explains every decision, so each output carries a stated basis a reviewer can inspect against the document rather than a bare classification. The suite is described as designed to be transparent, reviewable and defensible, with safeguards derived from published AI Principles. That is a documented verification surface tied to specific documents. Searched the aiR product pages, the artificial intelligence overview, the corporate data solutions pages and the learning centre on 29 Aug 2026 and located no accuracy figure, no precision or recall number, no hallucination rate, no test set, no published evaluation methodology and no independent benchmark participation. A partner published case study describes predictions as highly accurate, which is a customer's characterisation rather than a measurement and was not treated as evidence.

A real published commitment with a documented verification surface, short of thresholds. Control is the first of three stated generative AI principles, alongside Confidence and Transparency, Privacy and Security. The verification path is described rather than asserted: the assistant cites source documents in its results for users to reference when checking their work so they can ensure accuracy and verify outputs, and Deep Dive provides access to underlying source materials. Architectural limits reinforce it: no link following or web browsing, and every task runs as a one time generation request rather than a persistent agent. Not located as of 29 Aug 2026: what proportion of a first pass review Coding Suggestions is expected to decide unaided, any threshold at which a document routes to a human, and what the vendor commits to when an output is wrong. That last matters here because the vendor claims automation rivalling eyes on review, which is a claim about replacing a human step rather than assisting one.

A real published commitment with documented review surfaces and an explicit control philosophy, short of published thresholds. The vendor's AI Principles state the aim of technology that is clear, fair and gives customers the utmost control, and the aiR suite is described as transparent, reviewable and defensible. The review surface is concrete: every decision carries a rationale, and aiR for Privilege predictions are positioned to guide counsel's second pass review rather than to replace it. The vendor also invests in operator competence in a way no other record here does, running a certification programme covering generative AI and individual aiR products, with published guidance on building, testing and trusting prompts. Third party analysis states the product does not make autonomous privilege designations and that a human reviewer still makes every privilege call, which corroborates the position without being vendor material. Not located as of 29 Aug 2026: a published threshold at which a document routes to a human, and what the vendor commits to when an output is wrong.

Adoption figures stand where deployment outcomes would go. Published and specific: EverlawAI Assistant was developed with input from nearly 3,000 users, more than 150 customers use the generative features, and the platform is used by numerous United States government agencies including United States attorneys' offices. Those are real adoption numbers rather than logo walls, and the government adoption is corroborated by the FedRAMP and GovRAMP authorisations, which are matters of public record. But adoption is not outcome. Searched the product pages, the vendor blog, the newsroom and the support knowledge base on 29 Aug 2026 and located no named customer paired with figures and a date, no case study a reader could assess, and no measured result from any deployment.

Named customers and a specific, checkable adoption figure, short of vendor published outcome numbers. Adoption is quantified precisely rather than vaguely: 192 of the Am Law 200 firms and more than 110 legal service provider partners use RelativityOne, which is a figure a reader can test against the published Am Law list. Named organisations are published as aiR success stories including Alvarez and Marsal, Cimplifi and Gilbert and Tobin, with Alvarez and Marsal stated to have used aiR for Review, aiR for Privilege and aiR for Case Strategy on a single complex matter. Quantified results exist but sit in partner published material rather than vendor material: a Relativity Gold Partner published a case study covering 30,000 documents analysed with aiR for Privilege and 610 hours saved in privilege review, with auto generated log descriptions replacing a manual process. That is a named scope, a named figure and a described method, and it is recorded here as partner published rather than treated as the vendor's own evidence.

The most complete confidentiality posture on the index, and the only one where an outside authority has tested the generative features specifically. All three limbs are addressed in terms a buyer can read in advance. Training: the vendor states data submitted and responses received are not used to train models across customers and are not shared between customers, and separately that the enterprise language models it uses adhere to a zero data retention policy under which data is used solely to generate a response, deleted on completion, and never used to train their models. Segregation: stated between customers, and reinforced architecturally because the models hold no persistent knowledge of a customer's case. Retention: disclosed honestly and at the right level of detail, including the part most vendors would omit, that the vector database powering Deep Dive stores numerical embeddings created from customer documents and that this storage is necessary for retrieval. Privilege is named directly rather than implied: the vendor describes environments where sensitive case data, privileged communications and personally identifiable information must be safeguarded, and FedRAMP authorisation of the generative features means a third party assessment organisation has tested that claim. Short of nothing material on the three limbs; the one gap, no stated retention period for the vector database, is recorded on the retention signal row.

Substantive published commitments including one control no other record on this index discloses, short of the training and retention limbs. The distinctive element is specific and consequential: the vendor states it has opted out of the abuse and harmful content monitoring offered by Microsoft, so that no unauthorised users have access to raw inputs or outputs. Abuse monitoring is the standard route by which provider staff may review customer prompts, and opting out of it is the single most concrete confidentiality decision disclosed anywhere in this pull. Alongside it: a privileged access management solution and a classification schema dictating how confidential data is handled, and a vendor risk management team reviewing Azure's security and privacy posture at least annually to validate controls are operating effectively. Two gaps hold this off an A. No statement on whether customer content may be used to train models was located, either at the vendor or provider layer. No retention or deletion terms were located. Both matter for a platform holding entire document universes for live matters.

The audience is professional and the position is unstated. Users are law firms, corporate legal departments and government legal teams including United States attorneys' offices, with no consumer surface located, and the product is a document review platform rather than an advice tool, so the advice line question arises less sharply here than for a research or drafting product. Searched the product pages, the AI Assistant framework page, the support knowledge base and the vendor blog on 29 Aug 2026 and located no published position on advice versus tooling, no treatment of competence or supervision duties, and no jurisdiction limits. Recorded at C because the position is inferable from what the product is rather than published.

The audience is professional and the position is unstated. Users are law firms, corporate legal departments, government and regulatory response teams, and legal service providers, with no consumer surface located, and the product is a review and investigation platform rather than an advice tool, so the advice line question arises less sharply than for a research or drafting product. Searched the aiR product pages, the artificial intelligence overview, the data solutions pages and the learning centre on 29 Aug 2026 and located no published position on advice versus tooling, no treatment of competence or supervision duties as professional obligations, and no jurisdiction limits. Worth recording as adjacent rather than as credit: the vendor runs a substantial user certification programme, which addresses operator competence as a commercial and training matter rather than as the professional duty it also is.

Third A on this axis and the first earned on bias disclosure rather than certification alone. The vendor publishes a named AI Governance Framework as a standing document, structured on three stated generative AI principles of Control, Confidence, and Transparency, Privacy and Security. The framework describes specific governance decisions with their reasoning: third party technologies are evaluated from technical, legal, privacy and security perspectives before adoption, subprocessors including each language model are vetted with the appropriate internal teams, tasks run as one time generation requests, and link following and web browsing are excluded to keep the system closed loop, with the stated rationale that a model holding no persistent case knowledge cannot have it exfiltrated by prompt or data injection. Independent validation exists and is specific to the AI: a third party assessment organisation tested the generative features for FedRAMP authorisation, with an attestation letter, as part of the 2025 annual assessment. And the vendor makes the bias disclosure almost nobody on this index makes, stating plainly that if the case materials contain bias or toxicity that may be reflected in the output. Not located: pre release testing results, and a named individual owner of model governance.

A published governance framework with real substance, short of testing results, a named owner and any bias disclosure. Relativity AI Principles are published as a standing document and the vendor states they guide everyday decision making toward technology that is clear, fair and gives customers the utmost control, with aiR safeguards described as inspired by them. The vendor states directly that it recognises the value AI can create along with its risks and commits to processes that are thoughtful, disciplined and trusted, which is an acknowledgement of risk rather than an unqualified capability claim. Governance is also operationalised through vendor risk management, with an annual in depth review of the underlying platform provider's security and privacy posture. Not located as of 29 Aug 2026: an AI management certification such as ISO 42001, published pre release testing results, a named accountable owner for model governance, and anything on uneven output across matter types, parties or populations. The word fair appears in the principles without any published work behind it, which is the gap this axis exists to mark.

Substantive published policy covering most of the ground, on an unusually broad certification base. Published: zero data retention at the language model layer with data deleted on completion, a closed loop architecture excluding link following and web browsing, one time generation requests rather than persistent agents, subprocessor vetting including each language model, in region AI processing for United Kingdom and European customers, and a certification set spanning FedRAMP Moderate, GovRAMP Moderate, SOC 2 Type 2, ISO 27001:2013, Cyber Essentials Plus, GDPR, HIPAA and UK G-Cloud. The vendor also discloses that the platform is developed and operated by United States citizens with its research and development team in California, which is a personnel sovereignty statement few vendors make. Not located as of 29 Aug 2026: a retention period or deletion control for the vector database that stores customer document embeddings, a published subprocessor list as distinct from a statement that subprocessors are vetted, and an incident or breach notification practice.

CORRECTED 29 Aug 2026 during the trust portal sweep. Previously graded C because access control appeared to be the only well covered element. That reading came from marketing surfaces without reaching the dedicated trust site, and understated the published controls. Now located and published: customer managed encryption keys, so a customer can hold its own key material; Customer Lockbox, a default on control restricting the vendor's own system administrators from accessing customer workspaces unless the customer explicitly grants it, which is a strong and specific limit on vendor side access; client domains providing data separation between clients; Security Center, a monitoring application shipped to customers; SIEM integration giving customers access to their own security logs; and round the clock monitoring by the named in house security team, Calder7. Previously recorded and still standing: privileged access management, a classification schema for confidential data, the opt out from Microsoft abuse and harmful content monitoring, and annual vendor risk review of Azure. That is a substantive published policy covering most of what this axis asks. Not located as of 29 Aug 2026: a stated retention period or deletion control for customer data, prompts or aiR outputs, and a named subprocessor list. Those two absences are what hold this at B rather than A.

Searched the product pages, the AI Assistant framework page, the support knowledge base, the vendor blog and the newsroom on 29 Aug 2026. No published indemnity, liability cap, carve out, warranty on output or insurance position was located, and no customer terms of service or master agreement was located on the surfaces reached. Recorded as a pure absence on those surfaces. Rebuttable, and worth noting the shape: this vendor publishes more than almost anyone on this index about how its AI is governed and secured, and nothing about who bears the loss when an output is wrong. Governance and recourse are different questions and only the first is answered.

Searched the aiR product pages, the artificial intelligence overview, the corporate and data solutions pages and the learning centre on 29 Aug 2026. No published indemnity, liability cap, carve out, warranty on output or insurance position was located, and no customer agreement or master terms was located on the surfaces reached. Recorded as a pure absence on those surfaces. The shape is worth naming for this vendor specifically: aiR for Privilege exists to reduce the risk of inadvertent production of privileged material, which is among the most consequential errors in litigation, and nothing published addresses who bears the loss if a privileged document is produced on the strength of an AI prediction. Rebuttable with one link.

Searched the product pages, the AI Assistant framework page, the support knowledge base entry point and the vendor blog on 29 Aug 2026. No integrations page was reached, no named connector for document management, practice management or productivity tools was located, and no API documentation was located. Recorded as an absence on the surfaces reached rather than as a finding about the product. Two things a reader should weigh. First, the platform is deliberately end to end, covering upload through production and trial preparation, so it substitutes for a workflow chain rather than connecting into one, which reduces how much integration it needs. Second, this axis was not the focus of the research pass and an ediscovery platform of this scale almost certainly documents data ingestion routes somewhere. Rebuttable with a single link and flagged for revisit.

Real integrations exist, are named individually, and target the systems evidence actually lives in. Named as out of the box integrations for collections: Microsoft, Google, Slack and Box, with in place preservation from what the vendor calls the top productivity platforms, which is the integration that matters most for defensible legal hold. A published .NET Platform API for RelativityOne is documented through the learning programme, and the platform is explicitly built for shared working across internal teams, outside counsel and service providers, with more than 110 legal service provider partners in the ecosystem. Not located as of 29 Aug 2026: a consolidated integrations index page, per integration documentation of what moves in which direction and what an administrator configures, and legal document management connectors such as iManage or NetDocuments.

Deployment model is stated clearly with real residency options, short of the full picture. Cloud native delivery is stated throughout, and residency is offered concretely rather than described: in region AI processing is available for United Kingdom and European customers, introduced explicitly to meet data security and sovereignty requirements and keep data within local boundaries, with the vendor confirming the same zero retention and no training terms apply as in the United States implementation. Government deployment is authorised separately under FedRAMP Moderate and GovRAMP Moderate in the United States and G-Cloud in the United Kingdom. Personnel location is also disclosed, the platform being developed and operated by United States citizens with research and development in California. Not located as of 29 Aug 2026: a tenancy model, a named hosting provider, an enumerated region list, and any statement of where processing happens for customers outside the named regions.

The hosting platform is named and residency is not addressed. RelativityOne is stated to be built on Microsoft Azure, and the vendor adds a substantive point about that choice, that it is the same platform chosen by global regulators, alongside an annual vendor risk review of Azure's security and privacy posture. Naming the hosting provider and evidencing ongoing oversight of it is more than several records here manage. Searched the aiR pages, the artificial intelligence overview, the data solutions pages and the learning centre on 29 Aug 2026 and located no named regions, no customer selectable residency, no tenancy model, and no statement of where processing happens as distinct from where data is stored. For a platform serving 192 of the Am Law 200 across international matters, published residency options would be expected and none was located on the surfaces reached.

The strongest certification position on the index, and the only one where the generative AI features themselves have been independently authorised. FedRAMP Moderate Authorization and GovRAMP Moderate Authorization are held, and critically the generative features were included in the 2025 FedRAMP Annual Assessment where a third party assessment organisation independently tested and endorsed the authorisation, with an attestation letter issued. FedRAMP is a continuous authorisation regime with annual reassessment rather than a point in time report, and its status is a matter of public record rather than a vendor claim, which is a materially stronger form of evidence than any private attestation elsewhere on this index. Alongside it: SOC 2 Type 2, ISO 27001:2013, Cyber Essentials Plus, GDPR and HIPAA compliance, and UK G-Cloud status. The vendor is stated to be the first ediscovery provider to have its full portfolio of generative AI features FedRAMP authorised. Short only of a published report request route or trust portal, which was not located on the surfaces reached.

CORRECTED 29 Aug 2026 during the trust portal sweep. Previously graded C on the finding that the vendor claimed several industry certifications without naming any. That was wrong. It came from reading product and solutions marketing pages without reaching the dedicated trust site at relativity.com/trust and its compliance and privacy page, where the certifications are named individually and with versions. Published there: ISO/IEC 27001:2022 certification, ISO/IEC 27018:2019 certification, FedRAMP Moderate ATO, HIPAA compliance, IRAP assessed at Protected, a SOC 2 Type II report, a SOC 3 report, and a Cloud Security Alliance CAIQ, alongside a published request route for compliance certificates. Naming ISO 27001 at the 2022 revision and 27018 at 2019 is precise, and the set is unusually broad, spanning the US federal authorisation, the Australian government assessment at Protected level, a healthcare framework and a standardised cloud control questionnaire. A SOC 3 report is a public summary report, which is a materially more open disclosure than SOC 2 alone. The vendor also names its in house security team, Calder7, publishes a Security Center monitoring application to customers, and offers SIEM integration giving a customer access to their own security logs. Short only of a published coverage period, report date and named auditing firm, none of which was located as of 29 Aug 2026.

The architecture and the terms binding it are disclosed clearly while the components are not named. Published: enterprise grade large language models from what the vendor calls highly reputable AI service providers, operating under a zero data retention policy with data deleted on completion and never used to train their models, plus a vector database storing embeddings to power retrieval, plus a statement that all subprocessors including each language model are vetted with the appropriate internal teams and that technology is chosen on the quality of the foundation models and on providers' willingness to support the vendor's data commitments. A buyer therefore knows the shape of the chain and the commercial terms governing it, which is more than most disclose. Searched the product pages, the framework page, the support knowledge base and the newsroom on 29 Aug 2026 and located no named model provider, no named vector database provider, no published subprocessor list, and no commitment to notify customers when the supply chain changes.

The provider is identified, where the models run is stated, and the terms binding the relationship are described, short of naming the models themselves. Microsoft Azure is named as the platform the product is built on, and the vendor's disclosure about opting out of Microsoft's abuse and harmful content monitoring identifies Microsoft as the party that would otherwise have had access to raw inputs and outputs, which locates the generative processing in the Microsoft stack more precisely than most vendors manage. The relationship is governed rather than assumed: a vendor risk management team performs an in depth review of Azure's security and privacy posture at least annually to validate controls are operating effectively. Not located as of 29 Aug 2026: which specific models serve which aiR product, any subprocessor list beyond the platform provider, and any commitment to notify customers when the model supply chain changes.

Checked the product pages, the vendor blog, the newsroom and the support knowledge base entry point on 29 Aug 2026. No pricing page was located, no rate is published, no unit of charge is stated and no tier structure appears on the surfaces reached. Access to the generative features specifically is directed through a customer success manager or a request for a call, which is a sales gated route. No free trial or self serve entry point was located, and no third party pricing figure was located either.

A real commercial term is published without a rate, and the term itself is unusual enough to record. The vendor states that aiR for Review and aiR for Privilege are included in the standard pricing and packaging for RelativityOne, so a buyer learns that two of the five generative products carry no separate charge, which is a meaningful commercial fact and one almost no vendor on this index discloses about its AI features. Flexible pricing models are referenced without being enumerated. Searched the aiR pages, the data solutions pages and the corporate pages on 29 Aug 2026 and located no rate, no unit of charge, no tier structure, and no published packaging for the remaining aiR products, aiR for Case Strategy, aiR for Data Breach Response and aiR Assist. Recorded at C on the strength of the published inclusion statement.

Segment coverage is described with substance and the public sector position is unusually well defined. Segments named: law firms, corporate legal departments and government agencies at federal, state and local level, with United States attorneys' offices named specifically. Use cases are enumerated concretely for the public sector, covering litigation, investigations and Freedom of Information Act requests, and for the private sector covering document review, evidence based writing, coding and deposition analysis. Practice scope is clear and consistently stated as ediscovery, investigation and litigation end to end from data upload through production to trial preparation, with no claim to transactional or advisory capability. Geographic coverage extends to the United Kingdom and Europe with in region processing. Not located as of 29 Aug 2026: firm size segmentation, industry breakouts, and any statement of what the platform is not built for.

Segment and practice coverage is described with substance and quantified where it can be. Segments named: law firms with a stated 192 of the Am Law 200, corporations, government, and more than 110 legal service provider partners, with the platform explicitly designed as shared working space across internal teams, outside counsel and providers. Practice coverage is enumerated by product rather than claimed broadly, five aiR products each with a distinct purpose spanning document review, privilege, case strategy, data breach response and assistance, plus legal hold, preservation, collection, processing, production and analytics. Regulatory response is named down to the agency: EPA, DOJ, FDA, SEC and third party subpoenas. Not located as of 29 Aug 2026: firm size segmentation below the Am Law tier, jurisdictional or language coverage, and any statement of what the platform is not built for.

The question is answered at both layers, which few records here manage. At the vendor layer the support documentation states that the data a customer submits and the responses they receive are not used to train models across customers and are not shared between customers. At the provider layer the published AI governance framework states that the enterprise language models used adhere to a zero data retention policy, under which data sent for processing is used solely to generate a response, is deleted on completion, and is never used to train their models. The vendor sets out the consequence rather than leaving it implied: because customer data does not train or fine tune the models, the models hold no inherent proprietary knowledge of a case that could be exfiltrated through prompt or data injection. Recorded at policy never on the strength of both statements together.

Searched the aiR product pages, the artificial intelligence overview, the corporate and data solutions pages and the learning centre on 29 Aug 2026. No located material states whether customer content may be used to train models, either at the vendor layer or by the underlying platform provider. Recorded as silent under the rule that a value is never inferred from the absence of a contradiction, and specifically not inferred from the published opt out of Microsoft abuse and harmful content monitoring, which stops provider personnel accessing raw inputs and outputs and is a different question from whether anything trains on them. Notable as an absence given how specific this vendor is elsewhere about its data handling decisions.

Retention is disclosed honestly at both layers with a period stated at neither. At the model layer the answer is zero: data sent to the language models is deleted on completion and is not retained once the request finishes. At the platform layer the vendor volunteers what most would omit, that a vector database stores numerical embeddings created from customer documents and that this storage is necessary for retrieval to work. Disclosing that derived representations of client documents persist, and explaining why, is a materially more candid answer than the zero retention headline alone would give. Searched the framework page, the support knowledge base, the product pages and the newsroom on 29 Aug 2026 and located no retention period for the vector database, no customer control over it, and no deletion commitment for embeddings when a case closes. Recorded at disclosed without a period on that basis.

Searched the aiR product pages, the artificial intelligence overview, the corporate and data solutions pages and the learning centre on 29 Aug 2026. No public material on these surfaces states how long prompts, aiR outputs or rationales are retained, whether a customer controls the window, or whether deletion is available. The gap has a particular edge for this product: aiR generates a rationale for every decision and auto generated privilege log descriptions, so the system produces a substantial body of derived commentary about a customer's documents, and nothing located governs how long that commentary persists.

Segregation between customers is asserted and segregation within a customer is not addressed. The vendor states that data submitted and responses received are not shared between customers, and the architecture supports that claim, since the models hold no persistent knowledge across requests. What was not located, after searching the framework page, the support knowledge base and the product pages on 29 Aug 2026, is any published detail on how separation is enforced between users, teams or matters inside a single customer, which is where walls actually operate. That question has real weight for this product type: an ediscovery platform holds the whole document universe for a matter, and a firm running two adverse matters needs to know retrieval cannot cross between them. No document management integration was located whose permissions could be inherited. Recorded at claimed but not documented.

CORRECTED 29 Aug 2026 during the trust portal sweep. Previously recorded at claimed but not documented, on the basis that internal controls were asserted without customer side segregation being described. That was wrong: the product documentation describes two distinct mechanisms and describes them concretely. Client domains provide a secure way to isolate users, workspaces, groups and matters by client, with data separation such that only certified partners have access across their own clients, and client domain admins administer within that boundary. Separately, Customer Lockbox restricts the vendor's own system administrators from accessing customer workspaces unless the customer explicitly grants it, enabled by default, with system admins additionally required to belong to a group within a workspace to reach it. Isolation by matter is stated explicitly, which is the level a firm facing product needs, and vendor side access is constrained by a default on control rather than a policy promise. Recorded at own model documented rather than the positive value because the product operates its own permission structure rather than inheriting a document management system's access model at query time, and because no material was located stating that aiR retrieval and generation respect those boundaries per user when the AI runs across a workspace. Ethical walls are still not named as such. Previously recorded and still standing: privileged access management and a classification schema governing vendor handling of confidential data.

Searched the framework page, the support knowledge base, the product pages, the vendor blog and the newsroom on 29 Aug 2026, and no published customer agreement or data processing agreement was reached. No clause committing to notify a customer of a government or law enforcement request for their data was located, and no transparency report was located. Worth recording as context rather than as an answer: this vendor serves United States government agencies including attorneys' offices under FedRAMP and GovRAMP authorisation, which makes the question of what happens when a government requests data from the platform a live one for its private sector customers, and nothing located addresses it.

Searched the aiR product pages, the artificial intelligence overview, the corporate and data solutions pages and the learning centre on 29 Aug 2026, and no published customer agreement or data processing agreement was reached. No clause committing to notify a customer of a government or law enforcement request for their data was located, and no transparency report was located. Worth recording as context: the vendor markets consolidated regulatory response across EPA, DOJ, FDA, SEC and third party subpoenas, so its customers use the platform precisely to manage government demands for their own data, and what happens when a government instead demands data from the platform is unaddressed on the surfaces reached.

No primary law corpus is identified because the product does not hold one. Retrieval runs entirely against the customer's own case documents, uploaded into the platform and indexed as embeddings in a vector database, so the corpus is the evidence in the matter and its provenance is the discovery process itself. Searched the product pages, the framework page and the support knowledge base on 29 Aug 2026 and located no vendor supplied legal corpus, no licence basis and no update cadence, and none would be expected. Same architectural shape as the contract platforms on this index, where the absence describes the product design rather than a disclosure failure.

No primary law corpus is identified because the product does not hold one. Retrieval and analysis run against the customer's own collected evidence, ingested through legal hold, preservation and collection from named enterprise sources, so the corpus is the document universe for the matter and its provenance is the discovery process. Searched the aiR pages, the artificial intelligence overview and the data solutions pages on 29 Aug 2026 and located no vendor supplied legal corpus, no licence basis and no update cadence, and none would be expected. One adjacent capability was considered and not treated as a corpus: the platform can carry coding decisions, compliance workflows and privilege calls from prior matters into new ones, which reuses the customer's own past work product rather than any vendor held material.

Searched the product pages, the framework page, the support knowledge base and the vendor blog on 29 Aug 2026. No material was located addressing whether authority carries a treatment signal or whether subsequent history is checked, and no commercial citator licence was located. Noted for context: this is an ediscovery and investigation platform whose corpus is the evidence in a matter rather than published case law, so a citator is outside its design entirely, notwithstanding that the Writing Assistant produces evidence based writing which may in practice sit alongside legal authority drawn from elsewhere.

Searched the aiR product pages, the artificial intelligence overview, the data solutions pages and the learning centre on 29 Aug 2026. No material was located addressing whether authority carries a treatment signal or whether subsequent history is checked, and no commercial citator licence was located. Noted for context: this is an ediscovery and investigation platform whose corpus is collected evidence rather than published case law, so a citator is outside its design entirely, including for aiR for Case Strategy, which builds argument from the document record rather than from authority.

Searched the framework page, the support knowledge base, the product pages and the vendor blog on 29 Aug 2026. No published material describes what the product does when the documents do not support an answer, and no explicit no answer path or confidence signal exposed to the user was located. Two adjacent statements were considered and not treated as satisfying this signal. The vendor says it has taken steps to reduce hallucinations, which is an assertion about frequency rather than a described behaviour. And answers cite source documents so a user can verify them, which supports checking an answer that was given rather than telling a user when the corpus contained nothing. For a product whose central claim is that Deep Dive answers questions across millions of documents, what it says when the evidence is absent is a live question and is unaddressed.

Searched the aiR product pages, the artificial intelligence overview, the data solutions pages and the learning centre on 29 Aug 2026. No published material describes what the product does when the evidence does not support a determination, and no explicit no answer path was located. Two adjacent features were considered and not treated as satisfying this signal. Every aiR decision carries a rationale, which explains a determination that was made rather than declining to make one. And aiR for Privilege produces a prioritised queue surfacing high probability privileged documents, which is ranking by confidence rather than an abstention path, and no confidence threshold exposed to the user was located. For a privilege product the question of what happens on a genuinely ambiguous document is the sharpest version of this signal and it is unaddressed.

No court order, opinion or disciplinary record naming this product has been located as of 29 Aug 2026. Instrument searched: the AI Hallucination Cases database maintained by Damien Charlotin, which tracks court decisions worldwide involving AI generated hallucinated content and records the AI tool implicated where it is known. Also checked published 2026 sanctions summaries and secondary sanctions trackers. The entries located name filers, and in some rows other products, rather than this one. This is a statement about the public record on the date shown and not a clearance. Note the exposure differs from a research tool: this product generates from case evidence rather than from case law, so its characteristic failure would be a mischaracterised document or a fact unsupported by the record rather than an invented citation, and that failure mode is far less likely to be catalogued in a hallucination database.

No court order, opinion or disciplinary record naming this product has been located as of 29 Aug 2026. Instrument searched: the AI Hallucination Cases database maintained by Damien Charlotin, which tracks court decisions worldwide involving AI generated hallucinated content and records the AI tool implicated where it is known. Also checked published 2026 sanctions summaries and secondary sanctions trackers. The entries located name filers, and in some rows other products, rather than this one. This is a statement about the public record on the date shown and not a clearance. Note the exposure differs from a research tool: this product analyses collected evidence rather than generating citations to authority, so its characteristic failure would be a wrong privilege call or a mischaracterised document rather than an invented case, and neither would ordinarily surface in a hallucination database.

Searched the product pages, the AI governance framework page, the support knowledge base, the vendor blog and the newsroom on 29 Aug 2026. No engagement with any named ethics opinion or bar guidance was located, including ABA Formal Opinion 512 and state bar guidance. The vendor publishes a substantial AI governance framework and a set of generative AI principles, which govern its own conduct rather than engaging with the professional responsibility rules binding the lawyers who use it. Also not located: engagement with the Federal Rules of Civil Procedure or with case law on technology assisted review, which would be the natural professional touchstone for an ediscovery product using AI for first pass review.

Searched the aiR product pages, the artificial intelligence overview, the data solutions pages, the certification programme pages and the learning centre on 29 Aug 2026. No engagement with any named ethics opinion or bar guidance was located, including ABA Formal Opinion 512 and state bar guidance. Also not located, and more surprising for this vendor: any engagement with the Federal Rules of Civil Procedure or with the substantial body of case law on technology assisted review and defensible process, which is the natural professional touchstone for an ediscovery platform marketing defensibility. The vendor publishes AI Principles governing its own conduct and a certification programme establishing operator proficiency, neither of which engages with the professional rules binding its users.

Savings are claimed and nothing is published on the client's side of the equation. The vendor's framing is throughput rather than hours: automating first pass review with recall and precision that rivals eyes on review, streamlining processes, eliminating manual tasks, and answering questions across millions of documents in seconds. Automating first pass review is the single largest displaceable cost in litigation support and the claim is squarely about replacing billable human review time. Searched the product pages, the framework page, the support knowledge base and the vendor blog on 29 Aug 2026 and located no per matter record of AI assisted work intended for fee purposes, and no guidance on billing, fee or client disclosure treatment. Recorded at savings claims only.

Savings are claimed and quantified in partner material with nothing published on the client's side of the equation. The vendor's framing is cost and time reduction: automating privilege review to increase productivity and reduce cost, identifying impactful content in substantially less time, and reducing cost and response time across matters by carrying prior coding decisions forward. A Relativity Gold Partner published a case study recording 610 hours saved in privilege review on a 30,000 document analysis. Privilege review is billed work, and 610 hours is a large number in that context. Searched the aiR pages, the data solutions pages and the learning centre on 29 Aug 2026 and located no per matter record of AI assisted work intended for fee purposes, and no guidance on billing, fee or client disclosure treatment. Recorded at savings claims only.

Substantial diligence material is published openly and the specific artifact this signal names is not. Available without a sales conversation: a named AI governance framework describing the architecture and the terms binding the model providers, a support knowledge base answering the training question directly, and a certification set including FedRAMP Moderate and GovRAMP Moderate authorisation whose status is a matter of public record, alongside SOC 2 Type 2, ISO 27001, Cyber Essentials Plus and G-Cloud. A firm could evidence a great deal from that. What was not located as of 29 Aug 2026 is a subprocessor list: the vendor states it thoroughly vets all subprocessors including each language model, which is a statement about process rather than a disclosure of who they are, and no model provider is named anywhere located. No client facing consent or notification pack was located either. Recorded at on request on the strength of the published material falling short of the named artifacts.

Some genuinely useful material is published and the artifacts this signal names are not. Available: identification of Microsoft Azure as the platform, the disclosure that the vendor opted out of Microsoft abuse and harmful content monitoring so provider personnel cannot reach raw inputs and outputs, and a statement that Azure's security and privacy posture is reviewed in depth at least annually. A firm could forward the abuse monitoring point usefully, since it answers a question client AI clauses increasingly ask. But searched the aiR pages, the artificial intelligence overview, the data solutions pages and the learning centre on 29 Aug 2026 and located no subprocessor list, no named security certification, no published data processing agreement, and no client facing consent or notification pack. Recorded as not addressed because no assembled material exists to point a client to.

Several elements of a record are available, assembled from the product's design rather than offered as a disclosure artifact. Outputs cite the source documents they rest on, with access to the underlying materials, so what was relied on is traceable per answer, and an ediscovery platform necessarily maintains production and review histories at document level. Two elements are missing: no single per document export covering model used, sources retrieved and human verification together was located, and no model is named in published material so the model used could not be stated. Recorded at partial record. Noted for a reader: courts have engaged with technology assisted review in ediscovery for well over a decade, so this is one of the few product categories on the index where a defensibility record has an established judicial context, and the vendor does not connect its AI disclosure material to it.

The most complete disclosure material of any ediscovery record here, assembled from product features rather than offered as a single artifact. Every aiR decision carries a rationale, so the basis for each determination is recorded per document rather than reconstructed afterwards. The vendor states full audit trails and documented data governance across the platform, and describes the aiR suite as designed to be transparent, reviewable and defensible, with defensibility a stated design goal rather than a marketing adjective. aiR for Privilege generates privilege log descriptions automatically, which is a court facing artifact produced as a by product of the AI work itself. Two elements are missing: no single per document export combining model used, sources retrieved and human verification was located, and no model is named in published material so the model used could not be stated. Recorded at partial record on that basis.