Relativity vs Reveal: how they compare in 2026
Relativity and Reveal both cover ediscovery from legal hold through review to production. Relativity publishes more evidence, and the gap between them is mostly evidence rather than capability. Relativity publishes a checkable adoption figure and names its integrations. Reveal relies on a service provider partner account and a single attributed testimonial where deployment evidence should be. But Reveal owns two things Relativity does not. Private deployment is a real published option rather than a cloud only product with a footnote, and Reveal describes deep integration with a customer's own authentication controls, logging frameworks and enterprise security policies. Its AI is also customer trained by design, with a concrete control model: build a model as easily as creating a tag, train it in AI selected batches, evaluate after each round. If you need the platform inside your own perimeter, that decides it.
At a glance
All 15 axes, side by side
The same grid applied to every vendor in the index, graded from public sources. Hover a grade to see what the letter means on that axis.
AI Centrality
How much of the product is actually AI. Whether the machine learning is the mechanism the buyer is paying for or a feature layered onto conventional software, and whether the vendor is specific about which is which.
The platform began as an ediscovery system and still runs legal hold, collection, processing, review and production without generative AI. aiR Review, Privilege, Case Strategy, Data Breach Response and Assist sit on that base and drive its review and analysis work. Older model based features remain in use, among them the Prioritized Review classifier that learns from reviewers' coding, conceptual analytics on latent semantic indexing and sentiment analysis. Every AI capability is included with a committed data volume, and the platform is now sold under the aiR name.
NexLP and its data science team became Reveal AI, Brainspace brought visual analytics and concept search, and Reveal 11 was released as the integration of Brainspace, Reveal AI and Reveal Review into one platform. Capability spans supervised and unsupervised learning, deep learning and natural language processing, with a library of more than 30 reusable prebuilt models, custom models customers train, predictive scoring, concept clustering, translation and transcription, sentiment analysis, and generative review on aji, the review engine generally available since 30 September 2025, alongside the earlier Ask feature. The review tool was assembled around the models, and most of the product depends on them.
Citation Accuracy and Hallucination Disclosure
Whether the vendor publishes measured accuracy on citations and assertions, grounds output to primary sources, and says plainly what its system does when it does not know. Legal has a documented public record of fabricated citations reaching filed briefs, so an untested claim of accuracy is not evidence.
A study by Redgrave LLP compared aiR Review with a managed first pass review using active learning on about 45,000 documents from a public data set. Responsiveness turned on federal pharmaceutical compliance rather than topic, and a subject matter expert set the ground truth through a blind review of a random sample. aiR Review reached 88 percent recall against 64 percent, with an elusion rate of 1 percent against 3 percent. It also flagged more documents, at 29 percent precision against 39 percent, a trade off the study sets out. aiR Review used about 18 hours of attorney time against about 1,123, and the full report gives the method and statistics. Each aiR Review call cites the document passages it relies on.
aji, Reveal's generative review engine, gives reasoning inside the document for every generative rating and citations in the text on positively rated documents, which the documentation frames as opening the AI black box. It also ships a two stage measurement workflow. A Calibration Review compares the reviewing attorney's own coding decisions against generative ratings on the same sample and reports an Agreement Rate, and a low rate sends the user back to refine the definition. An optional Validate stage then runs against a larger random set drawn outside the calibration sample to confirm the ratings hold. Every figure is produced by the customer on its own matter and none is published. No vendor accuracy figure, precision or recall number for the prebuilt models, aji or Ask, hallucination rate, test set or independent benchmark is published. Reveal calls its predictive scores accurate without publishing a number.
Autonomy and Oversight Model
What the system decides on its own, what a lawyer must approve, and whether the vendor documents where the review point sits. A tool that drafts under review and a tool that files without one are different products and different risks.
aiR Review explains why each document matters and cites the passages behind each call. aiR for Privilege predicts whether documents may be privileged, identifies the people involved and drafts a privilege log description. Prioritized Review learns from reviewers' coding decisions to rank what people see next and can run beside aiR Review. In the Redgrave LLP study, a subject matter expert reviewed a blind sample to set the ground truth before revisiting the documents where he and aiR disagreed. No threshold is published at which a document must go to a person, and no commitment covers what happens when an aiR call is wrong.
Much of the AI is trained by the customer. A user builds custom models as easily as creating a tag, trains them through batches the AI selects, evaluates effectiveness after each training round, and uses interactive simulations to decide when training is sufficient. That leaves the decision on whether a model is good enough with the customer and gives them the instrument to make it. Predictive scores prioritize review rather than replacing it, and Ask returns supporting materials alongside its narrative answer. Nothing published sets a threshold at which a model is fit to rely on, says what Ask does unaided, or states what Reveal commits to when an output is wrong.
Operational and Outcome Evidence
Named, dated evidence that the product works in production at real firms or legal departments. Case studies with figures and identified customers count. Unattributed testimonials and launch announcements do not.
JND, a Relativity partner, ran aiR Review on a corporate client's production and reports more than 750 hours and $85,000 saved, with project time cut by nearly 90%. JND checks aiR's calls on a random sample of about 400 documents and computes recall and precision. Microsoft's legal department reports average recall above 96% and precision of 71% on an antitrust matter, review costs down 45% to 60% and $1.6 million in projected annual savings. Lowenstein Sandler and Baker McKenzie are quoted on claiR. The customer stories carry no date.
A service provider partner publishes an account of the full platform in use, covering legal hold, processing, early case assessment, review, Reveal AI and Brainspace, and an attributed practitioner testimonial appears on the vendor site. Corporate history is documented with checkable specifics, including a $250m investment from K1 Investment Management and a sequence of named acquisitions. None of that is a deployment outcome. No named customer paired with figures and a date, no case study with a method that can be assessed, and no adoption count appears on the platform pages, the why Reveal page, the blog or the resources index.
Privilege and Confidentiality Posture
How client confidences are handled: attorney client privilege and work product treatment, segregation of one client matter from another, whether client data trains any model, and what the vendor commits to in writing rather than in marketing.
Discrete permissions control what each user reaches, and Lockbox bars administrators, Relativity staff among them, from customer data without the customer's say. Data is encrypted at rest and in transit, with customer managed keys available. Customer data processed by aiR does not train or improve third party models, and AI processing runs in the published location for each instance region. Nothing addresses training of Relativity's own models, retention periods or the handling of privileged material inside aiR.
Reveal publishes a security and data privacy white paper addressing the Ask feature and the use of generative AI, behind a form. It commits to the confidentiality and integrity of customer data, backed by ISO 27001 and SOC 2 Type 2 certification. Private deployment inside the firewall is offered as an architectural answer to confidentiality, letting an organization keep discovery data inside its own perimeter with integration to internal authentication and logging. Granular audit logging and role based access controls are published. Reveal's own buyer guidance lists whether documents are used to train third party AI models among the questions to put to a vendor, and its public pages state no position of its own on that. Retention terms are not published either.
UPL and Professional Responsibility Posture
Whether the vendor is clear that it supplies a tool rather than legal advice, who its audience is, and how it addresses unauthorized practice of law, competence and supervision duties, and jurisdiction limits. ABA Formal Opinion 512 is the reference point. Where the advice line is not the duty a product raises, the axis is read through the nearest professional duty it does raise: judicial conduct rules and the reviewing duty for products sold only to courts, and the duty to bill for time actually spent for products that draft time entries.
Law firms, corporate legal departments, government agencies and legal service providers use the platform, and no consumer product is offered. aiR reviews and analyzes a matter's documents rather than advising clients on the law. No position is published on advice versus tooling, on competence and supervision duties or on jurisdiction limits.
Users are law firms, corporate legal and compliance teams, government and alternative legal service providers, with no consumer offering. The product analyzes collected evidence rather than giving advice, so the question of where advice begins arises less sharply than for a research or drafting tool. The platform pages, the why Reveal page, the blog and the Academy pages publish no position on advice versus tooling, no treatment of competence or supervision duties as professional obligations, and no jurisdiction limits, although translation and transcription across multiple languages imply work across borders. Reveal Academy runs user certification courses across platform functions, which addresses operator competence as a commercial matter rather than as a professional duty.
AI Governance and Bias Disclosure
Published governance over model behavior: who owns it inside the vendor, what is tested before release, and what is disclosed about disparate output across matter types, parties, or populations.
Under its AI Principles, Relativity commits to building AI for defined legal data tasks, to explaining how its models are trained and what each one looks at, and to testing models for bias. Where bias appears, the principles commit it to pause, consider mitigations, document its decisions and tell customers each model's appropriate use. The principles also state plainly that AI systems, Relativity's included, can give wrong results or contribute to biased decisions. aiR systems are updated regularly to improve quality, accuracy and consistency, so their output can change over time. No AI management certification, named accountable owner or bias test result appears.
Reveal states three pillars of an AI pledge, Trust, Knowledge and Security, and publishes guidance on choosing generative AI tools. That guidance lists questions for a vendor: whether documents train third party models, how access controls and audit logging work, how data residency is handled and what certifications a vendor holds. No AI governance framework, AI management certification such as ISO 42001, named owner of model governance, testing regime before release, or account of uneven output across matter types, parties or populations appears on the platform pages, the why Reveal page, the blog or the trust center. Reveal ships more than 30 prebuilt models that categorize documents by human behavior, including harassment and discrimination, and publishes nothing on how those models were validated or how they behave across populations.
AI Safety and Data Stewardship
Retention, deletion, access control, and what happens to prompts and documents after they are processed. Whether the vendor states its subprocessors and its incident practice, or leaves the buyer to assume.
By default, Relativity encrypts data at rest and in transit, and customer managed keys give the customer control of its encryption key. Lockbox keeps administrators, Relativity's own included, out of customer data unless the customer grants access, and customers can audit and revoke that access in real time. Security Center, built by the Calder7 security team, maps logins, flags users without two factor authentication and sends alerts on unusual events. No retention period, deletion schedule for prompts and aiR output, or subprocessor list is published.
Reveal publishes ISO 27001 certification with a 2025 audit completed and the certified products listed, SOC 2 Type 2 with documentation available to enterprise clients, CSA STAR and GDPR compliance, granular audit logging and role based access controls built into the platform, end to end encryption, support for data residency requirements in regulated jurisdictions, and private deployment inside the customer's firewall. A SafeBase trust center at a stable URL carries the security posture and a route to request documentation. Its published control set includes defined and implemented breach notification processes and policies reviewed at least annually. No retention period or deletion control for customer data, prompts or Ask outputs is stated, and no subprocessor list is named.
AI Liability and Recourse
What the vendor stands behind contractually when its output is wrong. Indemnities, caps, carve outs, insurance, and whether any of it is published or only reachable through a negotiated agreement.
No customer agreement, master terms or service level terms are published, so indemnity, liability caps, warranties and insurance for aiR output cannot be read before contracting. AI processing locations and aiR Unit fees are set by each customer's agreement, and Relativity may rate limit or queue AI features at its discretion. aiR for Privilege predicts privilege and drafts log descriptions, and nothing published allocates the loss if a privileged document is produced on its call.
No indemnity, liability cap, carve out, warranty on output or insurance position is published, and no customer agreement or master terms is published. Reveal does say it works to make costs clear and predictable, framing the problem as an unexpected bill reaching a partner or client, which concerns commercial risk to the customer rather than liability for output.
Practice Systems Integration Depth
How deeply the product reaches into the systems legal work already lives in: document management such as iManage and NetDocuments, Word and Outlook, contract lifecycle management, matter management, e-billing, and court filing systems.
Collection connectors reach Microsoft 365, Google Workspace, Slack, Claude Enterprise, ChatGPT Enterprise and Gemini, with new data sources added regularly. An MCP server lets assistants such as Claude, Gemini, Copilot and ChatGPT set up workspaces, organize case data and manage matters in natural language. Open APIs and the App Hub extend the platform at no extra license cost, and Gavel brings drafting into Microsoft Word. What each connector moves, in which direction, and what an administrator configures are not described.
The main published statement concerns private deployment, where the architecture is said to allow deep integration with internal authentication controls, logging frameworks and enterprise security policies. That is integration at the identity and audit layer rather than the file layer. Processing handles more than 900 file formats, which reduces the ingestion problem a connector would otherwise solve. No integrations index, no named connector for collection sources such as Microsoft, Google, Slack or Box, and no API documentation appears on the platform pages, the why Reveal page, the blog or the resources index.
Deployment Model and Data Residency
Where the software runs and where the data sits. Multi tenant cloud, single tenant, private deployment, on premises, and whether region of residence is a published option or an enterprise conversation.
Instance regions for Relativity aiR span the United States, Canada, Brazil, the UK, Ireland, France, Germany, the Netherlands, Switzerland, the UAE, South Africa, India, Australia, Japan, Hong Kong, Singapore and South Korea. All of them run on Microsoft Azure. Generative AI processing can run outside an instance's own region, and Relativity publishes where it runs for each one. French, German, Irish, Dutch, Brazilian and South African instances process within the EU Data Boundary, UK and UAE instances in the UK, and Hong Kong, Japanese, Singaporean and Korean instances in Japan. The United States, Canada, Switzerland, Australia and India process in country. aiR Government runs on Azure Government, and all of its customer data is processed in the continental United States. The processing map may shift as model availability changes.
Three deployment models are published: cloud, hybrid, and private deployment inside the firewall, where discovery data stays within the organization's own perimeter. Private deployment is an area of active investment rather than a legacy option. Reveal announced it had expanded that investment by 50 percent on stated enterprise demand, and published material on how it works, covering secure infrastructure, scalable performance, consistent compliance inside the firewall and integration with internal authentication and logging. Data residency is addressed directly as support for residency requirements in regulated jurisdictions, and Reveal's own buyer guidance tells readers to insist on data locality options because data sometimes must stay within a region or country. The available cloud regions and the tenancy model for the hosted option are not published.
Security Certifications and Trust Center
Independent attestation a buyer can pull without a sales call: SOC 2, ISO 27001, penetration test summaries, a trust center with current reports and named scope rather than a badge image.
ISO/IEC 27001:2022 and ISO/IEC 27018:2019 certifications, a SOC 2 Type II report, a SOC 3 report and the Cloud Security Alliance CAIQ are listed, with a request route for compliance certificates. aiR Government holds a FedRAMP Moderate authorization, the platform is assessed under IRAP at Protected, and Relativity states HIPAA compliance. Calder7, Relativity's in house security team, builds the Security Center monitoring application that customers use. Report dates and auditing firms are not published.
Reveal names ISO 27001, SOC 2 Type 2, CSA STAR and GDPR compliance. The trust center announces completion of the ISO 27001 audit for 2025 and lists the products certified, which shows what the certificate covers. A SafeBase trust center at a stable URL offers a self serve route to request security documentation, and SOC 2 Type 2 documentation is stated as available to enterprise clients. That is a request flow rather than a sales gate. The SOC 2 audit coverage period, the auditing firm for either certification and the ISO 27001 certificate expiry are not published.
Model Supply Chain Disclosure
Which models sit underneath, whose they are, where they run, and whether the vendor commits to telling customers when that changes. A legal buyer inherits every dependency it cannot see.
aiR runs on GPT-4o, GPT-4.1 mini, GPT-5 mini, GPT-5.1 and GPT-5.2, all through Microsoft Azure services. Which model or combination serves a task depends on the aiR system's purpose and on model capability and availability. Inference locations are published for every instance region, and customer data processed by aiR does not train or improve third party models. Relativity notes that those locations may change, and it promises no notice to customers before the models or locations change.
Models are built in house through the Reveal AI lineage from NexLP, and there is a proprietary model library, custom models trained by customers and Brainspace analytics, all owned rather than rented. What sits behind the Ask generative feature is not published. The platform pages, the why Reveal page, the Ask resource page description, the blog and the trust center name no large language model or provider, say nothing about where generative processing runs, list no subprocessors, and make no commitment to notify customers when the supply chain changes. Reveal's own buyer guidance raises whether documents train third party AI models, which implies third party models are involved without saying whose.
Commercial Transparency
Whether a buyer can learn what this costs without entering a sales process: published rates, the unit being charged, what sits behind an enterprise tier, and what implementation adds.
Pricing runs on a committed data volume, paid as you go or locked in on a one or three year commitment. aiR Review, Privilege, Case Strategy, Assist and claiR come with that volume, with no separate AI licenses and no per user fees. APIs, the App Hub and the MCP server carry no extra license cost, and specialized solutions are added as needed. Some aiR products consume aiR Units or carry fees set in the customer's contract. No rate per gigabyte or per unit is published.
No pricing page is published, no rate is published, no unit of charge is stated and no tier structure appears. Every commercial path ends in a demo request. One published commercial statement addresses the buying experience without giving a figure: Reveal states it works with customers to make costs as clear and predictable as possible, framing the problem explicitly as the unexpected bill a practitioner has to take to a partner or client. That is an acknowledgment of the cost predictability problem in ediscovery rather than a disclosure of price.
Firm and Practice Coverage
Who the product is actually built for. AmLaw, midlaw, small firm and solo, in house departments, government and courts, and which practice areas are supported rather than merely claimed.
Law firms, corporations, government agencies and legal service providers are the stated buyers, with 193 of the Am Law 200 among them and customers in more than 30 countries. Solution pages cover ediscovery, investigations, second requests, FOIA, data subject access requests, data breach response, early case intelligence and contracts, and plaintiff firms have a page of their own. aiR Government serves agencies under a FedRAMP Moderate authorization. What the platform is not built for, and which languages aiR handles, are not stated.
Practice scope runs across the EDRM from preservation and collection through processing, early case assessment, review, analytics and production, with more than 900 file formats, translation and transcription across multiple languages, and an interface that can be localized. Investigation types are listed concretely through the model library, with more than 30 prebuilt models for recurring matters and harassment and discrimination named specifically. Segments addressed include law firms, corporate legal and compliance, and alternative legal service providers, and customers may run work themselves or through a provider. Segmentation by firm size, jurisdictional coverage stated as such, adoption figures and any statement of what the platform is not built for are not published.
The 12 legal signals, side by side
Recorded rather than graded. These are the questions a practitioner has to answer before a tool touches a client matter, and the answers are taken from public material only.
Client Data in Training
Can material a lawyer puts into this product be used to train a model?
Customer data processed by aiR systems is not used to train or improve third party models or services, which covers the GPT models aiR reaches through Microsoft Azure. Training of Relativity's own models is not addressed, and Prioritized Review learns from reviewers' coding decisions. No published agreement carries a training term.
Nothing on the trust center, the platform pages, the why Reveal page, the blog or the resources index states whether customer content may be used to train models, either by Reveal or by any third party model provider. Reveal's own published buyer guidance lists whether documents are used to train third party AI models among the questions that separate a serious tool from a risky one, and those pages do not answer that question about Reveal. A dedicated security and data privacy white paper on the Ask generative feature sits behind a form submission.
Prompt and Output Retention
How long does the product keep what a lawyer typed, and can that be set to zero?
aiR Review writes a rationale and citations for every document, and aiR for Privilege drafts log descriptions, so each matter builds up AI generated commentary on its documents. No retention period, customer setting or deletion control is published for that output or for prompts.
Nothing on the trust center, the platform pages, the why Reveal page, the blog or the resources index states how long documents, Ask prompts or generated narrative answers are retained, whether a customer controls the window, or whether deletion is available. An organization that chooses private deployment inside its firewall holds its own discovery data inside its own perimeter and sets retention by controlling the store; for hosted customers no retention terms are stated.
Ethical Walls and Matter Segregation
Does retrieval respect the firm’s ethical walls, or can the model read across them?
Discrete permissions inside the platform govern what each user reaches, so a firm keeps matter access aligned in Relativity aiR rather than inheriting a document management system's rules. Lockbox blocks administrators, including Relativity staff, from customer data unless the customer grants access, and that access can be audited and revoked in real time. Ethical walls are not named as such.
Granular audit logging and role based permissions are built into the platform architecture, access controls and encryption are configured within customer environments, and private deployment integrates with the organization's own authentication controls and logging frameworks, bringing the firm's identity model to the platform. Reveal's buyer guidance also raises shared multi tenant infrastructure as a question to put to any vendor, and private deployment is its answer.
No published material states that retrieval or AI model application enforces those permissions for each user at query time, and no legal document management integration is published whose access model could be inherited. Ethical walls are not named as such.
Third Party Request and Subpoena Notice
If someone subpoenas the vendor for a firm’s data, does the firm hear about it first?
No customer agreement, data processing agreement or transparency report is published, so no commitment to notify a customer of a government or law enforcement request for its data can be read.
No customer agreement or data processing agreement is published. No clause committing to notify a customer of a government or law enforcement request for their data, and no transparency report, appears on the platform pages, the blog, the resources index or the trust center. The trust center's published control set includes defined and implemented processes for security breach notification, which addresses telling a customer about a compromise rather than about a lawful demand.
Primary Law Corpus Provenance
Where does the law in this product come from, and does the vendor have the right to use it?
aiR draws on the customer's own collected documents rather than a vendor supplied library of law, so no legal content source, license or update cycle applies.
The product holds no primary law corpus. Retrieval and analysis run against the customer's own collected evidence, so for that material the provenance is the discovery process itself. The AI Model Library is different. More than 30 prebuilt models are shipped ready to identify content and categorize documents by human behavior, and a pretrained model necessarily encodes whatever it was trained on. The platform pages, the why Reveal page, the blog and the resources index say nothing about what those models were trained on, whose data was used, on what rights basis, or how the models are validated or updated.
Good Law Verification
Does the product tell you when the authority it just cited has been overruled?
aiR works on a matter's collected documents rather than published case law, and claiR answers from that same data with direct citations to it. No treatment signal or later history check applies to legal authority.
Reveal is an ediscovery and investigations platform whose data is collected evidence rather than published case law. Nothing on the platform pages, the why Reveal page, the blog or the resources index addresses whether authority carries a treatment signal or whether subsequent history is checked, and no commercial citator license is named. Its outputs classify and summarize documents in a matter rather than citing authority.
Refusal and Uncertainty Behavior
What does the product do when the answer is not in the corpus?
aiR Review explains and cites each call, and aiR for Privilege ranks likely privileged documents by its prediction. No path is described for declining a call when a document does not support one, and no confidence threshold is shown to the user.
Nothing on the platform pages, the why Reveal page, the blog, the resources index or the trust center describes what Ask does when the evidence does not support an answer, and no explicit path for declining to answer is published. Ask returns a narrative answer with supporting materials, which lets a reader check an answer that was given but does not say when nothing supported one. Predictive scoring exposes model confidence for each document to prioritize review, which is a ranking signal on classification rather than a way to abstain on a generative answer, and the two work on different parts of the product.
Fabricated Citation Record
Does a public court record exist addressing fabricated or hallucinated legal citations in output from this product?
The AI Hallucination Cases database maintained by Damien Charlotin, which records court decisions worldwide that address hallucinated AI content and the tool involved where known, has no entry naming Relativity or aiR. aiR works on a matter's own documents and cites the passages behind each call rather than citing case law.
The AI Hallucination Cases database maintained by Damien Charlotin, which records the AI tool implicated where it is known, records no case naming Reveal, and published 2026 sanctions summaries and secondary trackers name none either. Their entries name filers, and in some rows other products. This is a statement about the public record rather than a finding about the product. Reveal analyzes collected evidence rather than generating citations to authority, so its characteristic failure would be a mischaracterized document or a wrong behavioral classification rather than an invented case, and neither would ordinarily surface in a hallucination database.
Bar Guidance Alignment
Has the vendor engaged in public with the ethics opinions its buyers are bound by?
No named ethics opinion, such as ABA Formal Opinion 512, or state bar guidance is engaged. The AI Principles commit Relativity to openness about its AI so that customers can defend their own processes.
The platform pages, the why Reveal page, the blog, the resources index and the Academy pages engage with no named ethics opinion or bar guidance, including ABA Formal Opinion 512 and state bar guidance. Nor do they engage with the Federal Rules of Civil Procedure or with the case law on technology assisted review and defensible process, which bears directly on how predictive coding is used in discovery. Reveal Academy trains users on the platform but does not address professional duties.
Billing and Fee Posture
Does the vendor address what happens to the bill when the work takes an hour instead of six?
In customer stories, JND reports cutting a corporate client's costs by nearly 60%, and Microsoft projects $1.6 million a year. The Redgrave LLP study recorded about 18 attorney hours against 1,123 for the same review. No per matter record of AI assisted work exists for billing, and nothing addresses how a firm bills or discloses that work to clients.
Reveal's published claims center on reaching relevant content faster, prioritizing review through predictive scores and reducing time to insight. Separately, Reveal says it works with customers to make costs clear and predictable, framing the problem as the unexpected bill a practitioner has to take to a partner or client. That engages with what the client ultimately pays, but it concerns the vendor's own invoice rather than the firm's bill.
The platform pages, the why Reveal page, the blog and the resources index show no record of AI assisted work for each matter intended for fee purposes and no guidance on billing, fees or disclosure to clients.
Outside Counsel Guideline Readiness
Can a firm get this vendor through a client’s AI clause without a bespoke negotiation?
ISO 27001 and 27018 certifications, SOC 2 Type II and SOC 3 reports and the CAIQ are available through a request route, and the models and processing locations behind aiR are published. No subprocessor list, data processing agreement or client notice pack is published.
Available without a sales conversation: a SafeBase trust center at a stable URL with the security posture and a documentation request flow, and named current certifications, including ISO 27001 with the 2025 audit and certified product list, SOC 2 Type 2 with documentation for enterprise clients, CSA STAR and GDPR. The trust center entry point, the platform pages and the blog show no subprocessor list, no statement naming which model providers see client content, and no consent or notification pack for clients.
Court Disclosure Support
If a judge’s standing order requires an AI disclosure, can the product produce one?
Every aiR Review call explains why the document matters and cites the passages behind it, and aiR for Privilege drafts privilege log descriptions. Relativity names the models aiR uses and the processing location for each region. No single export per document brings together the model used, the passages relied on and the human review.
Granular audit logging is built into the platform architecture, Ask returns supporting materials with each narrative answer so the basis of a generative output can be traced, and the custom model workflow records training rounds and effectiveness evaluations, which documents how a classifier reached its state. These are platform features rather than a disclosure document. No export for each document combining the model used, the sources retrieved and human verification is published, and the model behind the generative feature is not named.
Predictive coding and technology assisted review have an established judicial record in ediscovery, and Reveal's AI disclosure material does not connect to it.
The questions both sides leave open
Derived from the records above rather than written, so it cannot favor either vendor. Take these into both conversations and ask each side the same question.
- AI Liability and Recourse
- Prompt and Output Retention
- Third Party Request and Subpoena Notice
- Primary Law Corpus Provenance
- Good Law Verification
- Refusal and Uncertainty Behavior
- Bar Guidance Alignment
Which one fits
Choose Relativity if
- You need scale and an interoperable working space. Relativity states that 193 of the Am Law 200 use it, and more than 80 certified partners work in the same platform as internal teams and outside counsel.
- Collection has to reach the systems evidence lives in. Relativity names Microsoft, Google, Slack and Box as out of the box integrations, with in place preservation for defensible legal hold, and publishes a platform API.
- You want to know what the AI is charged for. Relativity includes aiR Review, Privilege, Case Strategy and Assist in a committed data volume, with no per user fees.
Choose Reveal if
- The platform has to run inside your own perimeter. Reveal publishes private deployment as a real option, with integration described into your own authentication, logging and enterprise security policies.
- Your matters run in many languages and formats. Reveal handles more than 900 file formats with translation and transcription across multiple languages and a localizable interface.
- You want to train the classifier yourself and see when it is good enough. Reveal's model is customer trained by design: build a model as easily as creating a tag, train through AI selected batches, evaluate after each round and use simulations to decide when training is sufficient.
In summary
Relativity
Relativity operates Relativity aiR, formerly RelativityOne, on Microsoft Azure for law firms, corporations, government and legal service providers. It covers legal hold and preservation, collection, processing, review, production and analytics, with Relativity aiR as its generative suite. According to the AI Legal Index, 193 of the Am Law 200 use it, alongside more than 80 certified partners. Its integrations are named individually and target the systems evidence lives in, including Microsoft, Google, Slack and Box with in place preservation, alongside a documented platform API. Its AI products come with a committed data volume rather than separate licenses.
Reveal
Reveal is an AI powered ediscovery and investigations platform covering the full EDRM, from legal hold and processing through early case assessment, review, analytics and production. It handles more than 900 file formats and was assembled through acquisition, including Mindseye, NexLP and Brainspace. According to the AI Legal Index, Reveal publishes private deployment as a real option, with integration described into a customer's own authentication controls, logging frameworks and enterprise security policies. Its control model is concrete because much of the AI is customer trained: users build models as easily as creating a tag and evaluate effectiveness after each training round.
Questions buyers ask
Relativity vs Reveal: which ediscovery platform is stronger?
Relativity publishes more evidence, driven by checkable adoption figures and named integrations. Reveal's decisive advantage is deployment, with private deployment a real published option. If the platform must run inside your own perimeter, that outweighs Relativity's lead on evidence. From the AI Legal Index, based on each vendor's own published materials as of August 30, 2026. No vendor pays for placement.
Can Reveal be deployed on premise or in a private cloud?
Yes. The vendor states that its private deployment architecture allows deep integration with a customer's internal authentication controls, logging frameworks and enterprise security policies, which the AI Legal Index records as integration at the identity and audit layer rather than the file layer. Relativity is delivered as Relativity aiR, formerly RelativityOne, on Microsoft Azure.
Which one has more customers?
Relativity states that 193 of the Am Law 200 use its platform, with more than 350,000 users and over 80 certified partners. Reveal's deployment evidence is thinner, resting on a service provider partner's published account of the platform in use and an attributed practitioner testimonial. Its corporate history includes a checkable $250 million investment. From the AI Legal Index, based on each vendor's own published materials as of August 30, 2026. No vendor pays for placement.
Do Relativity and Reveal publish accuracy figures for their AI?
Relativity publishes a Redgrave LLP study in which aiR Review reached 88 percent recall against 64 percent for active learning, while Reveal publishes no headline accuracy number. Relativity states that aiR for Review surfaces content backed by transparent rationale and that aiR for Privilege explains every decision, so each output carries an inspectable basis. Reveal's aji engine produces in document reasoning for every generative rating with in text citation, paired with a customer side measurement loop. From the AI Legal Index, based on each vendor's own published materials as of August 30, 2026. No vendor pays for placement.
What do Relativity and Reveal both leave unpublished?
Neither publishes a position on liability or recourse when the AI makes a wrong privilege call or misses responsive material. Neither publishes a full rate, though Relativity prices on a committed data volume that already carries its AI products. And neither publishes a professional responsibility position, which matters less for review platforms than for research tools since both analyze collected evidence rather than giving advice. From the AI Legal Index, based on each vendor's own published materials as of August 30, 2026. No vendor pays for placement.
Neither vendor paid for inclusion, placement or a grade, and neither reviewed this page before it published. Everything on it comes from public material on the dates shown. How the index grades.
How Reveal was assembled matters for diligence. Reveal was built through acquisition, with Mindseye for processing, NexLP for data science and Brainspace among the components, and Logikcull and Brainspace are now inside Reveal rather than sold separately. That is normal in this market, and it means capability can differ by component, particularly where a feature predates the acquisition. Reveal's citation accuracy record covers aji, its generative review engine, generally available since 30 September 2025, as well as the Ask feature. Neither vendor reviewed this page.