Everlaw vs Lawdify: how they compare in 2026
Everlaw combines evidence processing, review and production with tools for understanding and presenting a case. Its AI suite includes cited answers through Deep Dive, coding recommendations, summaries and a Writing Assistant. The AI Legal Index highlights the ability to keep those activities around one evidentiary record, including when outside AI applications connect to it. A team can review material and then use the same organized sources in case preparation. Lawdify's legal workspace emphasizes extracting a working account of the dispute from mixed evidence. Case Brain, Smart Tables and Lawri support chronologies, issue analysis and drafts, with particular attention to APAC languages and communication formats. The AI Legal Index highlights Lawdify's Microsoft Excel connection for examining project data alongside the broader case file. Everlaw offers a broader discovery lifecycle and an open evidence platform; Lawdify brings a dispute preparation assistant into the tools and records used by the matter team.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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
- The same platform should support processing, review, production and trial preparation.
- Review teams need coding recommendations alongside predictive coding and conceptual clustering.
- External AI tools should work against the existing evidentiary system of record.
Choose Lawdify if
- APAC languages and formats such as WeChat or KakaoTalk are prominent in the evidence.
- The team needs a shared case knowledge system for evolving claims and responses.
- Spreadsheet analysis should draw on the evidence gathered for the matter.
In summary
Everlaw
Everlaw connects evidence processing, AI review and trial preparation in an open discovery platform.
Lawdify
Lawdify organizes dispute evidence and applies it through Lawri, Smart Tables and Microsoft tools.
Questions buyers ask
Can Everlaw draft from the case record?
Yes. Writing Assistant helps develop arguments and narratives grounded in the matter's evidence. It sits alongside the platform's review and analysis functions. From the AI Legal Index, based on each vendor's own published materials as of October 16, 2026. No vendor pays for placement.
Does Lawdify support traditional evidence preparation?
Its legal offering includes OCR, metadata extraction, email processing, document inventories and bundles for export, as well as generative assistance. From the AI Legal Index, based on each vendor's own published materials as of October 16, 2026. No vendor pays for placement.
What differs about their connection to other tools?
Everlaw presents an open evidence platform that external AI applications can use. Lawdify presents dedicated Word, Outlook and Excel connections to Case Brain. 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.
This page addresses evidence and case preparation. Everlaw's discovery operations and Lawdify's separate insurance product have additional functions beyond that overlap.