Everlaw vs Relativity: how they compare in 2026
The incumbent against the cloud native challenger, and they are closer than the market positioning suggests. Relativity leads on reach. It publishes an adoption figure a reader can actually test, 193 of the Am Law 200, names its collection integrations into Microsoft, Google, Slack and Box, and documents a platform API. It also names the GPT models behind aiR, publishes where AI processing runs for each instance region, and sells on a committed data volume with the AI products inside it. Everlaw's counter is that its confidentiality posture has been tested on the generative features specifically by an outside authority, and that it is the platform government litigators actually run. Both are defensible. The tie breaker is usually procurement.
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 covers upload, processing, review, production and trial preparation, and was sold before the generative layer arrived in 2024. The models drive a core layer on top of it. Coding Suggestions automates first pass review, Deep Dive answers natural language questions across matters of more than 20 million documents, and Review Assistant and Writing Assistant summarize, extract and draft. Predictive Coding and Clustering apply machine learning that predates the generative features. Everlaw now presents itself as the evidence layer for legal AI, serving outside AI tools through its integrations, while the document review platform still runs without the generative layer.
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.
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.
Everlaw AI cites source documents in its results so users can verify output, and Deep Dive answers draw on facts extracted from specific documents, retrieved from a vector database of the customer's own documents. Everlaw says grounding curtails hallucinations without removing them. Coding Suggestions gives tiered recommendations with a justification for every document, and the AI product page says it can exceed human review performance. No accuracy rate, recall or precision figure, test set or evaluation method from Everlaw itself is published. The 98.2% recall figure comes from one customer's layered workflow combining keyword culling, Coding Suggestions and Predictive Coding.
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.
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.
Control is the first of Everlaw's generative AI principles, beside Confidence and Transparency, Privacy and Security. Users can opt in or out of Everlaw AI, and an icon marks every generative feature. Everlaw AI cites the source documents in its results so users can check their work, and each statement in a draft is followed by the document meant to support it, one click away. The customer terms require the customer to evaluate output for accuracy, including through human review, before relying on it. Coding Suggestions gives tiered coding recommendations with a justification for every document. In the Dinsmore & Shohl study, attorneys validated its output against a 120 document ground truth set. No threshold at which a document must go to a person, and no limit on what Coding Suggestions decides unaided, is published.
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.
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.
Dinsmore & Shohl's study, published on 24 March 2026, describes a review of 26,000 contracts after a client's cyber incident. Partner Jennifer Mitchell and ediscovery director Peter Pepiton first culled about 20,000 drafts and duplicates. They then ran Coding Suggestions against attorney written criteria, validated it on a 120 document ground truth set, and finished with Predictive Coding. Recall rose from about 92% on the first Coding Suggestions pass to 98.2% after formal statistical validation. The home page adds stories from an unnamed Am Law 200 firm and an unnamed Fortune 500 company. Government agencies use Everlaw under its FedRAMP Moderate and GovRAMP authorizations.
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.
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.
The customer terms bar Everlaw from using inputs or outputs to train or fine tune a generative AI model for general use through the service, and inputs and outputs remain case materials the customer owns. The enterprise language models follow a zero data retention policy, using data only to generate a response and deleting it on completion. Data is not shared between customers, and the models hold no persistent knowledge of a customer's case that a data injection could extract. The vector database behind Deep Dive keeps numerical embeddings created from customer documents, which retrieval needs. Everlaw describes its environments as built for sensitive case data, privileged communications and personal information. No retention period is stated for the embeddings.
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.
UPL and Professional Responsibility Posture
Whether the vendor is clear that it supplies a tool rather than legal advice, who its audience is, and how it addresses unauthorized practice of law, competence and supervision duties, and jurisdiction limits. ABA Formal Opinion 512 is the reference point. Where the advice line is not the duty a product raises, the axis is read through the nearest professional duty it does raise: judicial conduct rules and the reviewing duty for products sold only to courts, and the duty to bill for time actually spent for products that draft time entries.
The buyers are law firms, corporate legal departments and government legal teams, with no consumer product. The customer terms require the customer to review output for accuracy before relying on it and disclaim any warranty on AI output. Everlaw is a document review and case building platform rather than an advice tool, and it publishes nothing on advice versus tooling, on competence or supervision duties, or on jurisdiction limits.
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.
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.
Everlaw publishes an AI Governance Framework built on three generative AI principles: Control, Confidence, and Transparency, Privacy and Security. Third party technologies are evaluated from technical, legal, privacy and security perspectives before adoption, and accuracy assessments inform which language models Everlaw uses. Users can opt in or out of Everlaw AI, and the framework sets out protections against data injection and exfiltration. On bias, the framework says Everlaw cannot manage bias inside third party foundation models and does not use prompt based mitigation, because that could mask bias in the case documents themselves. No accountable owner of AI governance is named and no testing results are published.
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.
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.
Everlaw's language models follow a zero data retention policy, so data sent for processing is used only to generate a response and deleted on completion. Everlaw itself keeps customer data only as long as the customer stores it on the platform. A Pinecone vector database stores embeddings of customer documents to power Deep Dive. The data processing addendum commits to notice of a verified security incident without undue delay, with information as it becomes known and steps to contain and remediate. Subprocessors are listed with function and country, and additions or replacements are notified at least 10 days ahead. Termination instructs Everlaw to delete customer data within a commercially reasonable timeframe. Customers choose the AWS instance where data is stored and processed. No period is stated for deletion after termination or for keeping the embeddings behind Deep Dive.
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.
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.
Everlaw's Customer Terms of Service, last updated 1 October 2025, carry an indemnity from Everlaw against third party claims that the service infringes intellectual property rights. Claims that Everlaw AI Assistant output infringes are excluded, along with customer negligence, breach, alteration and combination with other products. Each party's liability is capped at the fees paid or payable in the 12 months before the liability arises, and indirect and consequential damages are excluded. Everlaw's cap for an unpaid subscription is $100. Everlaw warrants only compliance with applicable law. It makes no warranty on the accuracy, completeness or reliability of AI output, and the customer must review output before relying on it. The terms state that Everlaw is not an insurance company and point the customer to its own insurance for losses outside Everlaw's express obligations.
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.
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.
Everlaw connects to the AI tools legal teams already use, naming Claude, ChatGPT Enterprise, Google Gemini, Microsoft Copilot, Harvey, Legora and Thomson Reuters as integration partners, each described in a line. Claude can retrieve and analyze case materials directly, and ChatGPT Enterprise uses Everlaw as an evidence layer. API and MCP connectors let firms and partners build custom tools on Everlaw as the evidentiary system of record. Cloud connectors collect data straight from Microsoft 365, Zoom and Google Workspace. No public page sets out which objects each integration moves, in which direction, or what a firm must configure.
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.
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.
Everlaw runs on Amazon Web Services, and customers choose the instance where data is stored and processed: United States, United Kingdom, EU in Germany, Canada or Australia. AI processing follows the instance. Google and OpenAI process in the EU for UK and EU customers, and OpenAI processes in Canada for the Canadian instance, under the same zero retention and no training terms. Government agencies run on Everlaw's FedRAMP Moderate and GovRAMP authorized environments. The transparency report says Everlaw is subject to United States law and that data may also be subject to the laws of the chosen instance's country. No tenancy model is described.
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.
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.
Everlaw holds FedRAMP Moderate and GovRAMP Moderate authorizations, SOC 2 Type 2 and SOC 3 reports, ISO 27001:2022, ISO 27017 and ISO 27018 certification, Cyber Essentials Plus, and CCPA and GDPR compliance. Its security and compliance portal at trust.everlaw.com lists the SOC 2 Type II report with a 2026 bridge letter and a penetration test executive summary. It also lists a HIPAA HITECH assessment, a CAIQ dated May 2026, a SIG Lite and the ISO statement of applicability. Reports open through an access request on the portal. The portal also carries an architecture diagram, the AI governance framework and a 2026 disaster recovery test summary.
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.
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.
Everlaw's subprocessor list, updated 2 October 2026, names Amazon Web Services and Google as cloud and AI service providers, OpenAI as an AI service provider and Pinecone for the vector database behind Deep Dive. It gives countries of processing by instance, with Google and OpenAI processing in the EU for UK and EU customers and OpenAI in Canada for the Canadian instance. The integrations page says Google Gemini powers retrieval and context engineering. The data processing addendum commits to notice at least 10 days before a subprocessor is added or replaced, with 15 days to object. The AI governance framework says the language models follow zero data retention terms and that accuracy assessments inform which models Everlaw uses. No model or version is named for any feature.
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.
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.
Everlaw's pricing page lists what the platform includes rather than what it costs. Cloud connectors, processing at one million documents an hour, transcription, redactions, productions, Predictive Coding, Clustering, timelines, transcript analysis, onboarding, training, standard data migration and unlimited user licenses are included. Coding Suggestions, Deep Dive, machine translation and custom extractions run on credits, with single document use free of credits. An intake portal with case management and compliance reporting is an add on. No figure, tier or unit of charge for the platform is published, and pricing runs through a demo request.
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.
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.
Everlaw sells to law firms, corporate legal departments and government agencies at federal, state and local level. Public sector use cases include litigation, investigations and Freedom of Information Act requests. Private sector use cases include document review, early case assessment, legal holds, internal investigations, data privacy, depositions and trial preparation. Its practice scope is ediscovery, investigation and litigation from data upload through production to trial, with no claim to transactional or advisory work. Regional instances run in the United States, United Kingdom, Germany, Canada and Australia. Segmentation by firm size, industry breakouts and any statement of what the platform is not built for are not published.
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.
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?
Under its Customer Terms of Service, Everlaw will not use inputs or outputs to train or fine tune a generative AI model for general use through the service. Inputs and outputs are case materials the customer owns. The AI governance framework says the enterprise language models follow zero data retention terms, deleting data on completion and never using it to train their models. Predictive Coding learns from the coding decisions within a customer's own matter.
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.
Prompt and Output Retention
How long does the product keep what a lawyer typed, and can that be set to zero?
Data sent to the language models is deleted on completion and not kept after the request. Everlaw keeps customer data only as long as the customer stores it on the platform, and does not retain deleted data unless the law requires it. The Pinecone vector database behind Deep Dive stores numerical embeddings created from customer documents, which retrieval needs. Termination instructs Everlaw to delete customer data within a commercially reasonable timeframe, and no period is stated for the embeddings or for deletion after termination.
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.
Ethical Walls and Matter Segregation
Does retrieval respect the firm’s ethical walls, or can the model read across them?
Everlaw says data submitted and responses received are not shared between customers, and the models hold no persistent knowledge across requests. Nothing published sets out how separation is enforced between users, teams or matters inside one customer, where walls operate. A firm running two adverse matters on the platform has no published description of whether Deep Dive retrieval can cross between 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.
Third Party Request and Subpoena Notice
If someone subpoenas the vendor for a firm’s data, does the firm hear about it first?
Everlaw's data processing addendum says that if it must respond to a request for customer data, it will promptly notify the customer and provide a copy of the request unless legally prohibited. Its government request transparency report adds that Everlaw reviews each request, may seek to narrow or object to it, and emails the customer before disclosing. Notice is withheld only where the law prohibits it, after an account takeover or in an emergency, and is given once a prohibition lifts. United States authorities must use a subpoena, court order, warrant or national security request.
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.
Primary Law Corpus Provenance
Where does the law in this product come from, and does the vendor have the right to use it?
No primary law corpus sits behind Everlaw. Retrieval runs against the customer's own case documents, uploaded into the platform and indexed as embeddings in a vector database, so the source material is the evidence in the matter. No vendor supplied legal collection, license basis or update cadence applies.
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.
Good Law Verification
Does the product tell you when the authority it just cited has been overruled?
Everlaw's corpus is the evidence in a matter rather than published case law, so its output cites documents, not authority. Writing Assistant drafts arguments from the evidence, and any legal authority in that writing comes from elsewhere. No treatment signal or check of subsequent history is described.
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.
Refusal and Uncertainty Behavior
What does the product do when the answer is not in the corpus?
Everlaw does not describe what the product does when the documents do not support an answer, and shows no confidence signal for an answer. It says grounding curtails hallucinations, and answers cite source documents so a user can check them. Deep Dive answers questions across matters of more than 20 million documents, and its behavior when the evidence is absent is not described.
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.
Fabricated Citation Record
Does a public court record exist addressing fabricated or hallucinated legal citations in output from this product?
Damien Charlotin's AI Hallucination Cases database, which tracks court decisions worldwide on hallucinated AI content, has no entry naming Everlaw. Everlaw generates answers and drafts from a matter's own evidence rather than from case law.
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.
Bar Guidance Alignment
Has the vendor engaged in public with the ethics opinions its buyers are bound by?
Everlaw names no ethics opinion or bar guidance, including ABA Formal Opinion 512. Its AI governance framework and generative AI principles govern its own conduct rather than the professional duties of the lawyers who use it. It also does not connect its AI features to the Federal Rules of Civil Procedure or to case law on technology assisted review.
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.
Billing and Fee Posture
Does the vendor address what happens to the bill when the work takes an hour instead of six?
Everlaw's savings claims are about throughput: Coding Suggestions automates first pass review with results it says can exceed human review, and Deep Dive answers questions across very large matters in seconds. Everlaw sells to law firms, where that review time is billed to clients. Nothing published addresses how AI assisted review is recorded or billed, or whether clients are told.
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.
Outside Counsel Guideline Readiness
Can a firm get this vendor through a client’s AI clause without a bespoke negotiation?
A subprocessor list, updated 2 October 2026, names AWS, Google, OpenAI, Pinecone, Zendesk and Datadog with function and country. Everlaw's customer terms, data processing addendum and government request transparency report sit beside it. The trust portal carries SOC 2 and SOC 3 reports, a penetration test summary, a CAIQ and a SIG Lite behind an access request. Security, endpoint detection and response tools are left off the public list and are available by email.
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.
Court Disclosure Support
If a judge’s standing order requires an AI disclosure, can the product produce one?
Each Everlaw AI result cites the source documents behind it, and an ediscovery platform keeps review and production histories at document level, so what an answer relied on can be traced. No export per document records the model used, the sources retrieved and the human check together, and no model is named for any feature. Courts have long engaged with technology assisted review in ediscovery, and Everlaw does not connect its AI disclosure material to that case law.
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.
The questions both sides leave open
Derived from the records above rather than written, so it cannot favor either vendor. Take these into both conversations and ask each side the same question.
- Primary Law Corpus Provenance
- Good Law Verification
- Refusal and Uncertainty Behavior
- Bar Guidance Alignment
Which one fits
Choose Everlaw if
- Confidentiality assurance has to be externally tested rather than asserted. An outside authority has tested Everlaw's generative features specifically.
- Your work is government litigation, investigations or Freedom of Information Act response. Everlaw names federal, state and local agencies including United States attorneys' offices, with the public sector use cases enumerated rather than implied.
- You want the generative architecture described. Deep Dive runs on a vector database of embeddings of your own documents, with answers synthesized from facts extracted from specific documents and citations back to the source.
Choose Relativity if
- Scale and interoperability decide it. Relativity states that 193 of the Am Law 200 use it, counts more than 80 certified partners, and is designed as shared working space across internal teams, outside counsel and providers.
- You need in place preservation and collection from the systems evidence actually lives in. Relativity names Microsoft, Google, Slack and Box as out of the box integrations and publishes a platform API, where Everlaw's cloud connectors cover Microsoft 365, Zoom and Google Workspace.
- You want to know the commercial shape before the call. Relativity sells on a committed data volume, pay as you go or on a one or three year commitment, with the AI products inside it.
In summary
Everlaw
Everlaw is a cloud ediscovery, investigation and litigation platform sold to law firms, corporate legal departments and government agencies. Its customer terms bar using inputs or outputs to train a generative model for general use, its language models run under zero data retention, and it publishes its subprocessor list and a government request transparency report. It connects to Claude, ChatGPT Enterprise, Gemini, Copilot, Harvey and Legora, offers API and MCP connectors, and collects from Microsoft 365, Zoom and Google Workspace. Its pricing page lists inclusions and AI credits without a figure.
Relativity
Relativity operates Relativity aiR, formerly RelativityOne, on Microsoft Azure, covering legal hold and preservation, collection, processing, review, production and analytics, with five generative products. According to the AI Legal Index, 193 of the Am Law 200 use it, as do more than 80 certified partners. Relativity names collection connectors for Microsoft 365, Google Workspace and Slack and documents a platform API. Pricing runs on a committed data volume that includes the AI products and charges no per user fees.
Questions buyers ask
Everlaw vs Relativity: which should we buy?
Both publish their security and AI terms in depth. Relativity states that 193 of the Am Law 200 use its platform, names its collection integrations and documents a platform API. Everlaw publishes its customer terms, data processing addendum and subprocessor list, connects to the main AI assistants through API and MCP connectors, and holds FedRAMP Moderate and GovRAMP authorizations. Matters routed through outside counsel and service providers often already sit in Relativity. From the AI Legal Index, based on each vendor's own published materials as of August 30, 2026. No vendor pays for placement.
Which one do most large law firms use?
Relativity states that 193 of the Am Law 200 use its platform, and it counts more than 80 certified partners. Everlaw publishes a dated customer study instead: Dinsmore & Shohl reached 98.2% recall reviewing 26,000 contracts with Coding Suggestions and Predictive Coding. Everlaw also serves federal, state and local government agencies. From the AI Legal Index, based on each vendor's own published materials as of August 30, 2026. No vendor pays for placement.
Which is stronger on data privacy?
Each is strong in a different way. An outside authority has tested Everlaw's generative features specifically, and Everlaw states that submitted data is not used to train models across customers. Relativity states it has opted out of Microsoft's abuse and harmful content monitoring, closing the usual route by which model provider staff can access raw inputs and outputs. From the AI Legal Index, based on each vendor's own published materials as of August 30, 2026. No vendor pays for placement.
Does Relativity charge extra for its AI features?
No. Relativity includes aiR Review, Privilege, Case Strategy, Assist and claiR with a committed data volume and sells no separate AI licenses. No rate is published by either vendor. 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 Everlaw and Relativity both leave unpublished?
Neither stands behind AI output when it makes a wrong privilege call or misses responsive material. Everlaw's terms cap liability at 12 months of fees and exclude AI output from its infringement indemnity, and Relativity publishes no customer agreement. Neither publishes a full rate, and neither publishes a professional responsibility position, though the advice line matters less for review platforms than for research and drafting tools. 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.
Everlaw's 98.2% recall figure comes from one customer's layered workflow, and Relativity's recall figures come from a study Redgrave LLP ran and Relativity published, so neither is a benchmark run on both products. Neither vendor reviewed this page.