Lawdify vs Relativity: how they compare in 2026
Lawdify places a case knowledge system behind its drafting and analysis tools. Raw files become parties, dates, issues and evolving chronologies that Lawri can use in later tasks. The AI Legal Index highlights the link from that case context into Word, Outlook and Excel as a practical feature for teams working across correspondence, submissions and project spreadsheets. Instructions can also arrive by email with attached evidence. Relativity aiR combines collection, processing, review and production with case strategy tools. Its strategy workflow extracts facts, organizes chronologies and develops witness outlines connected to potential exhibits. The AI Legal Index highlights the ability to classify facts as helpful or harmful when assembling a narrative from a large record. Both can support factual case preparation. Lawdify foregrounds a multilingual disputes assistant and office workflows, while Relativity embeds strategy within a broader environment for discovery, investigations and other legal data work.
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.
Lawdify is built around AI interpretation of evidence and the work that follows from it. Case Brain organizes parties, dates and issues from uploaded material, while Lawri uses that context to answer questions and prepare drafts. Expertise Engineering turns the team's own methods into reusable skills, and Smart Tables run specialized tasks as the dispute develops. Email instructions can start several agents and return their results to the sender. These functions depend on models understanding and generating content, rather than adding a conversational feature to an older records system. File storage and exports support the service, but the case intelligence is the product being sold.
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.
Evidence Check is designed to test whether a proposition has supporting material in the case file. Lawri can inspect case context, and the research interface presents authorities with relevance and application descriptions. The legal product also offers factual checking and argument strength assessment. Its visible examples do not establish a reproducible accuracy test, a citation status check or a consistent route from every generated assertion to an open source passage. The terms describe output as probabilistic and place evaluation with the user. These are useful verification intentions and review functions, with the detailed grounding method and measured failure behavior still unspecified.
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.
The legal workflow gives the lawyer a first draft and a visible editing surface. In Word, proposed revisions appear with track changes so the user can review and accept individual edits. The terms also require evaluating probabilistic output for the intended use, including expert human review. Email agents can process attachments and reply with results, while specialized skills automate steps in case preparation. The distinction between a draft and professional judgment is therefore supported by an actual review mechanism. A threshold that stops an agent, a mandatory approval before consequential action and a recovery route after an incorrect result are not specified.
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.
Lawdify identifies deployed use as well as product demonstrations. Its September 2026 account describes Zurich Insurance Malaysia moving from a three month proof of concept into a multiyear deployment. That is a named operational relationship in the insurance business, rather than a measured result for every legal workflow. The legal service also presents customer experience around evidence organization and case preparation. The broader claims about faster settlements, reduced loss ratios and fraud detection are not accompanied by a method tying each figure to a named deployment. The available customer evidence supports practical use without establishing a reproducible estimate of legal team productivity.
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 February 2026 terms expressly exclude customer documents, legal files and case materials from model training and product improvement. They distinguish that material from anonymized, aggregated operational usage metrics. Customer information is subject to confidentiality obligations, and the hosting provisions describe logical separation between customer environments. Deletion after termination includes backups and a certificate with logs. These are explicit commitments for material entrusted to the service. The confidentiality section also uses pilot terminology, while specific treatment of legal privilege, internal matter walls and retention by individual model providers remains unclear. The promise covers more than a general assurance that information is secure.
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.
Lawdify's legal workspace is aimed at law firms and internal legal teams handling contentious work. Its terms distinguish the application from legal advice and say that it does not create a lawyer and client relationship. The AI output provision describes generated content as probabilistic and calls for evaluation appropriate to the use case, including expert human review. Word editing and evidence checks support that professional review in the product. The legal service also reaches construction claims teams and other specialists. A jurisdiction by jurisdiction account of permitted professional use, competence obligations and limits for people who are not lawyers is not provided.
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.
Lawdify identifies an AI Governance Framework and a prompt injection testing report in its trust center. Access to those materials requires a request, so their detailed controls and results are not available in the open view. The privacy policy recognizes a right to human review of certain automated decisions where applicable, and the service makes commitments about customer data use and human evaluation of output. These establish elements of a responsible AI position. They do not reveal who owns model governance, what release testing covers or whether uneven performance across languages and legal settings has been measured. The restricted framework's contents remain unknown.
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.
The service combines encryption in storage and transit with authentication, access logging and logical separation of customer environments. Its terms set deletion procedures for removed material and terminated accounts, including a deletion certificate and associated logs after service exit. A trust center names cloud and supporting service providers, while the privacy policy describes security monitoring, backups and incident response procedures. These provisions address how client material is handled beyond ordinary website privacy. The incident section does not set a customer notification timetable or describe the response workflow in detail, and the complete path through underlying AI providers is not mapped for each processing operation.
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.
The terms exclude warranties of accurate or reliable output and place professional evaluation with the user. They limit consequential losses and require the customer to defend Lawdify against several categories of claim arising from use or breach. The liability limitation contains exceptions for matters including fraud, intellectual property and confidentiality obligations, but its cap is internally inconsistent: the written amount corresponds to $100 while the parenthetical figure is $10. A specific promise to defend the customer against an AI output claim is not included. The result is a conventional allocation of risk away from the supplier, with an unresolved ambiguity in the stated monetary limit.
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.
Lawdify connects its case information to Microsoft Word, Outlook and Excel. The Excel application has a concrete setup path: sign in to a Lawdify account using email verification, select a case or matter, then use the chat panel or custom functions. LAWDIFY.ASK returns an answer in a cell, while LAWDIFY.EXTRACT pulls information from case material into the spreadsheet. The application can read and change the workbook and send information over the internet. A Lawdify subscription is required. This describes both the movement of case information into an existing work surface and the configuration needed to use it, beyond a list of compatible applications.
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.
Customer data is hosted in Singapore by default, with other jurisdictions available by arrangement where the cloud infrastructure supports them. The security offering describes regional support across Asia, the European Union and the United States. The terms also describe logical separation of customer environments, and the current certification scope identifies the legal platform on Azure and Google Cloud Platform. These details make the default hosting location and the existence of regional alternatives concrete. They do not provide a complete list of selectable processing regions, explain every AI inference location or map storage and processing differences to subscription tiers. Residency therefore remains partly specified.
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.
Prescient Security issued Lawdify's ISO/IEC 27001:2022 certificate on February 13, 2026. Certificate 122744, Issue 2, is valid through February 25, 2028, subject to satisfactory surveillance. Its scope covers the information security management system supporting the Legal AI Analyst platform hosted on Azure and Google Cloud Platform, including engineering, development, information security, legal and finance functions. The certificate is available directly in the trust center, with the issuer, dates and scope visible. Separate audit and penetration testing reports require access approval. The certificate establishes a current independent management system attestation; it does not certify the correctness of generated legal analysis.
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.
Case Brain combines graph and vector databases to connect contract information with evidence and follow changes in a dispute. Lawri, specialized skills and the email agents operate on that case context rather than treating each request as an isolated conversation. Google Cloud Platform, Microsoft Azure and AWS support the infrastructure, with the legal platform's certification scope specifically covering Azure and Google Cloud Platform hosting. The foundation models and their versions are not individually identified. Nor is there a stated customer notification mechanism for model substitutions. The architecture is partly explained, while the full chain of model operators remains unspecified.
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.
The current terms distinguish recurring self service subscriptions from enterprise arrangements governed by an order form. Additional processing pages and capacity for AI tasks can carry separate charges, so both the recurring structure and usage expansion are visible. Prices are presented during registration or contracting rather than in an open rate table. Cancellation deadlines differ between self service and enterprise subscriptions, and fees are generally nonrefundable. An earlier founder submission gave an annual range without identifying the currency, which does not establish a current comparable price. Implementation charges and the amount charged for each additional processing unit are also not separately stated.
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.
The legal platform serves law firms and internal legal teams preparing disputes, with construction and infrastructure claims prominent in its founder account. Claims consultants, expert witnesses and contractors also use the same kind of evidence preparation workflow. Support includes APAC languages and communication formats such as WhatsApp, WeChat and KakaoTalk, alongside email archives, spreadsheets and handwritten material. The company separately sells insurance claims assessment, including bodily injury work. That combination gives a specific picture of the intended settings and evidence types. An exhaustive jurisdiction and practice coverage table, exclusions for particular legal tasks and firm size limits are not supplied.
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?
The February 2026 terms explicitly prohibit using customer documents, legal files and case material to train models or improve products and services. Only anonymized, aggregated usage metrics, such as feature use and error rates, may support platform improvement. That exception concerns operational telemetry, rather than the substance of client matters. The privacy policy also excludes customer personal data from generalized public model training.
Reusable skills within the customer's workspace are a product function; they do not override the express restriction on training from customer content.
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?
Account data remains subject to the privacy policy's service, legal and business purposes. The terms set a 30 day period after a user deletes customer material and a similar exit period before resources and backups are removed, subject to applicable requirements. A deletion certificate and logs follow service termination. These are deletion event rules, rather than a fixed lifetime for every prompt and answer during an active subscription. A customer selected zero retention option and a separate schedule for AI provider copies are not described.
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?
The hosting terms describe logical separation between customer environments, with authentication and access controls. The product also organizes information into cases and lets the Excel user select a matter. Those features do not specify how conflicting matters inside one firm are separated during retrieval. Inheritance of source system permissions, enforcement for each user and the treatment of shared skills across restricted matters are not described.
Customer isolation therefore has an express basis, while the behavior of ethical walls within an individual firm's workspace remains unspecified.
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?
The confidentiality terms require prompt written notice of a compelled disclosure demand to the extent practicable, allowing the disclosing party to seek a protective order at its own expense. The receiving party also agrees to cooperate with those efforts. For a broad regulatory request not aimed at the customer, the agreement permits compliance with prompt notice where the regulator allows it. Disclosure may still proceed when legally required.
These provisions establish a notice commitment with stated qualifications, without a transparency report describing requests actually received.
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?
Legal research is described as covering cases, statutory material and other legal databases, alongside analysis of the customer's own evidence. The interface illustrates relevance and application summaries, but does not identify a complete set of source publishers, licensing arrangements or jurisdiction specific update schedules. The insurance offering separately refers to proprietary injury and settlement intelligence, which does not establish the rights supporting the legal research collection.
Customer uploaded evidence and external legal authority have different provenance, and the latter's acquisition and licensing terms remain unspecified.
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?
Lawri can check facts against the case file, and Evidence Check examines whether evidence exists and supports a proposition. Those functions concern factual support. The legal research feature includes smart citations, case relevance and statutory analysis, but does not identify a licensed citator or explain its own subsequent treatment process. A case appearing relevant is different from confirming that it remains usable authority after later decisions.
The product's general verification functions therefore do not establish an automated good law check for authorities used in a draft.
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?
The interface includes a prompt optimizer, factual checking and evidence support checks. The terms warn that model output is probabilistic and should receive expert evaluation. None of those descriptions specifies a refusal path when the available material cannot support an answer. A stop condition, a visible uncertainty threshold and the response presented after grounding fails are not defined for Lawri or the email agents.
The question is how the product handles insufficient support before delivering a result, separate from the lawyer's opportunity to review it afterward.
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?
No matching entry was found in the AI Hallucination Cases database for Lawdify, Lawri, Case Brain or Smart Tables as of October 16, 2026. The check covered the company name and the named legal product components rather than treating a general account of AI misuse as an incident involving this service. This result records the absence of a matching case in that database on the stated date. It does not measure citation accuracy, establish that every generated draft is reliable or remove the need for professional verification.
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?
The legal workspace supports lawyers through first drafts, evidence analysis and tracked edits, and its terms retain expert review and disclaim legal advice. Those provisions do not connect the workflow to a named bar opinion or a jurisdiction specific framework for competence, confidentiality and supervision. The company's mention of inclusion in a government AI guide is a product listing rather than an explanation of professional obligations.
No mapped set of bar requirements accompanies the described legal workflow or its use by claims specialists who are not lawyers.
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?
Lawdify presents faster case preparation and less repetitive evidence work as benefits for disputes teams. Its agents can examine files, produce first drafts and bring case information into spreadsheets, which may change the time spent on a matter. The legal workflow does not include a record separating AI effort from lawyer time for a client invoice. Guidance on passing through productivity gains, recording review time or explaining AI use in an engagement is also not supplied. The cost argument remains an efficiency claim rather than a billing method.
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?
The named service providers are Google Cloud Platform, Microsoft Azure, AWS, Cloudflare, GitHub and Vanta, covering hosting, monitoring, version control and compliance. Customers can also inspect the current terms, privacy policy and ISO certificate, while additional assurance materials require an access request. This provides identifiable suppliers for part of a client's vendor review. A full model provider inventory and a prepared client disclosure or consent package are not presented with the list.
The available material therefore supports supplier identification without establishing every fact needed for a particular client's AI clause.
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?
The Excel connection retains chat history organized over time, with controls to revisit, rename and delete conversations. Word drafting uses tracked changes, and the legal workspace can export organized bundles for filings. These preserve parts of the work and review context. They do not form a single export identifying the model used for each passage, all retrieved sources and the lawyer who verified them. A court specific disclosure or verification certificate is not described. The available record is partial and should not be treated as an automatically completed filing declaration.
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 Lawdify if
- The case team wants Lawri to act on a persistent understanding of the dispute.
- Office based work requires case information inside spreadsheets, email and drafts.
- APAC evidence formats and multilingual records are central to preparation.
Choose Relativity if
- Collection, processing, review and production must run in one legal data environment.
- Witness outlines should connect key facts to potential exhibits.
- The team needs to organize helpful and harmful facts within a broader case strategy workflow.
In summary
Lawdify
Lawdify turns mixed case evidence into reusable context for assistants and Microsoft workflows.
Relativity
Relativity aiR combines discovery operations with factual analysis, chronologies and witness preparation.
Questions buyers ask
What does Relativity add beyond review?
Its Case Strategy offering develops summaries, chronologies and witness outlines, and connects documents with testimony to help organize the case narrative. From the AI Legal Index, based on each vendor's own published materials as of October 16, 2026. No vendor pays for placement.
Can Lawdify process email evidence?
Yes. It supports email formats, extracts attachments and relevant metadata, and also lets users initiate tasks by sending an email instruction. From the AI Legal Index, based on each vendor's own published materials as of October 16, 2026. No vendor pays for placement.
Are these only tools for construction disputes?
No. Lawdify has a construction disputes focus in its founder account, but its current legal product serves broader contentious work. Relativity supports multiple forms of litigation and legal data analysis. From the AI Legal Index, based on each vendor's own published materials as of October 16, 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.
The comparison concerns Lawdify's legal workspace and Relativity's discovery and strategy functions. Lawdify's insurance settlement module is a separate offering.