Lawdify vs Reveal: how they compare in 2026
Reveal supports a matter from preservation and collection through analysis, review and production. Its generative functions answer questions in ordinary language and connect the result to supporting material in the evidence set. The AI Legal Index identifies that path from an answer to source text as useful for investigators who need to validate a finding immediately. Conceptual retrieval and reusable AI models sit within its wider discovery offering. Lawdify concentrates its legal product on preparing and developing a dispute. It organizes raw evidence into Case Brain, applies specialized skills through Lawri and generates drafts using the team's own context. The AI Legal Index identifies Smart Tables and connected Excel work as relevant where claims depend on structured project records as well as narrative evidence. Their overlap is evidence analysis, with different surrounding workflows. Reveal supplies the broader discovery environment, while Lawdify carries case information into a shared workspace and familiar office tools.
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.
NexLP and its data science team became Reveal AI, Brainspace brought visual analytics and concept search, and Reveal 11 was released as the integration of Brainspace, Reveal AI and Reveal Review into one platform. Capability spans supervised and unsupervised learning, deep learning and natural language processing, with a library of more than 30 reusable prebuilt models, custom models customers train, predictive scoring, concept clustering, translation and transcription, sentiment analysis, and generative review on aji, the review engine generally available since 30 September 2025, alongside the earlier Ask feature. The review tool was assembled around the models, and most of the product depends on them.
Citation Accuracy and Hallucination Disclosure
Whether the vendor publishes measured accuracy on citations and assertions, grounds output to primary sources, and says plainly what its system does when it does not know. Legal has a documented public record of fabricated citations reaching filed briefs, so an untested claim of accuracy is not evidence.
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.
aji, Reveal's generative review engine, gives reasoning inside the document for every generative rating and citations in the text on positively rated documents, which the documentation frames as opening the AI black box. It also ships a two stage measurement workflow. A Calibration Review compares the reviewing attorney's own coding decisions against generative ratings on the same sample and reports an Agreement Rate, and a low rate sends the user back to refine the definition. An optional Validate stage then runs against a larger random set drawn outside the calibration sample to confirm the ratings hold. Every figure is produced by the customer on its own matter and none is published. No vendor accuracy figure, precision or recall number for the prebuilt models, aji or Ask, hallucination rate, test set or independent benchmark is published. Reveal calls its predictive scores accurate without publishing a number.
Autonomy and Oversight Model
What the system decides on its own, what a lawyer must approve, and whether the vendor documents where the review point sits. A tool that drafts under review and a tool that files without one are different products and different risks.
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.
Much of the AI is trained by the customer. A user builds custom models as easily as creating a tag, trains them through batches the AI selects, evaluates effectiveness after each training round, and uses interactive simulations to decide when training is sufficient. That leaves the decision on whether a model is good enough with the customer and gives them the instrument to make it. Predictive scores prioritize review rather than replacing it, and Ask returns supporting materials alongside its narrative answer. Nothing published sets a threshold at which a model is fit to rely on, says what Ask does unaided, or states what Reveal commits to when an output is wrong.
Operational and Outcome Evidence
Named, dated evidence that the product works in production at real firms or legal departments. Case studies with figures and identified customers count. Unattributed testimonials and launch announcements do not.
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.
A service provider partner publishes an account of the full platform in use, covering legal hold, processing, early case assessment, review, Reveal AI and Brainspace, and an attributed practitioner testimonial appears on the vendor site. Corporate history is documented with checkable specifics, including a $250m investment from K1 Investment Management and a sequence of named acquisitions. None of that is a deployment outcome. No named customer paired with figures and a date, no case study with a method that can be assessed, and no adoption count appears on the platform pages, the why Reveal page, the blog or the resources index.
Privilege and Confidentiality Posture
How client confidences are handled: attorney client privilege and work product treatment, segregation of one client matter from another, whether client data trains any model, and what the vendor commits to in writing rather than in marketing.
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.
Reveal publishes a security and data privacy white paper addressing the Ask feature and the use of generative AI, behind a form. It commits to the confidentiality and integrity of customer data, backed by ISO 27001 and SOC 2 Type 2 certification. Private deployment inside the firewall is offered as an architectural answer to confidentiality, letting an organization keep discovery data inside its own perimeter with integration to internal authentication and logging. Granular audit logging and role based access controls are published. Reveal's own buyer guidance lists whether documents are used to train third party AI models among the questions to put to a vendor, and its public pages state no position of its own on that. Retention terms are not published either.
UPL and Professional Responsibility Posture
Whether the vendor is clear that it supplies a tool rather than legal advice, who its audience is, and how it addresses unauthorized practice of law, competence and supervision duties, and jurisdiction limits. ABA Formal Opinion 512 is the reference point. Where the advice line is not the duty a product raises, the axis is read through the nearest professional duty it does raise: judicial conduct rules and the reviewing duty for products sold only to courts, and the duty to bill for time actually spent for products that draft time entries.
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.
Users are law firms, corporate legal and compliance teams, government and alternative legal service providers, with no consumer offering. The product analyzes collected evidence rather than giving advice, so the question of where advice begins arises less sharply than for a research or drafting tool. The platform pages, the why Reveal page, the blog and the Academy pages publish no position on advice versus tooling, no treatment of competence or supervision duties as professional obligations, and no jurisdiction limits, although translation and transcription across multiple languages imply work across borders. Reveal Academy runs user certification courses across platform functions, which addresses operator competence as a commercial matter rather than as a professional duty.
AI Governance and Bias Disclosure
Published governance over model behavior: who owns it inside the vendor, what is tested before release, and what is disclosed about disparate output across matter types, parties, or populations.
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.
Reveal states three pillars of an AI pledge, Trust, Knowledge and Security, and publishes guidance on choosing generative AI tools. That guidance lists questions for a vendor: whether documents train third party models, how access controls and audit logging work, how data residency is handled and what certifications a vendor holds. No AI governance framework, AI management certification such as ISO 42001, named owner of model governance, testing regime before release, or account of uneven output across matter types, parties or populations appears on the platform pages, the why Reveal page, the blog or the trust center. Reveal ships more than 30 prebuilt models that categorize documents by human behavior, including harassment and discrimination, and publishes nothing on how those models were validated or how they behave across populations.
AI Safety and Data Stewardship
Retention, deletion, access control, and what happens to prompts and documents after they are processed. Whether the vendor states its subprocessors and its incident practice, or leaves the buyer to assume.
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.
Reveal publishes ISO 27001 certification with a 2025 audit completed and the certified products listed, SOC 2 Type 2 with documentation available to enterprise clients, CSA STAR and GDPR compliance, granular audit logging and role based access controls built into the platform, end to end encryption, support for data residency requirements in regulated jurisdictions, and private deployment inside the customer's firewall. A SafeBase trust center at a stable URL carries the security posture and a route to request documentation. Its published control set includes defined and implemented breach notification processes and policies reviewed at least annually. No retention period or deletion control for customer data, prompts or Ask outputs is stated, and no subprocessor list is named.
AI Liability and Recourse
What the vendor stands behind contractually when its output is wrong. Indemnities, caps, carve outs, insurance, and whether any of it is published or only reachable through a negotiated agreement.
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 indemnity, liability cap, carve out, warranty on output or insurance position is published, and no customer agreement or master terms is published. Reveal does say it works to make costs clear and predictable, framing the problem as an unexpected bill reaching a partner or client, which concerns commercial risk to the customer rather than liability for output.
Practice Systems Integration Depth
How deeply the product reaches into the systems legal work already lives in: document management such as iManage and NetDocuments, Word and Outlook, contract lifecycle management, matter management, e-billing, and court filing systems.
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.
The main published statement concerns private deployment, where the architecture is said to allow deep integration with internal authentication controls, logging frameworks and enterprise security policies. That is integration at the identity and audit layer rather than the file layer. Processing handles more than 900 file formats, which reduces the ingestion problem a connector would otherwise solve. No integrations index, no named connector for collection sources such as Microsoft, Google, Slack or Box, and no API documentation appears on the platform pages, the why Reveal page, the blog or the resources index.
Deployment Model and Data Residency
Where the software runs and where the data sits. Multi tenant cloud, single tenant, private deployment, on premises, and whether region of residence is a published option or an enterprise conversation.
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.
Three deployment models are published: cloud, hybrid, and private deployment inside the firewall, where discovery data stays within the organization's own perimeter. Private deployment is an area of active investment rather than a legacy option. Reveal announced it had expanded that investment by 50 percent on stated enterprise demand, and published material on how it works, covering secure infrastructure, scalable performance, consistent compliance inside the firewall and integration with internal authentication and logging. Data residency is addressed directly as support for residency requirements in regulated jurisdictions, and Reveal's own buyer guidance tells readers to insist on data locality options because data sometimes must stay within a region or country. The available cloud regions and the tenancy model for the hosted option are not published.
Security Certifications and Trust Center
Independent attestation a buyer can pull without a sales call: SOC 2, ISO 27001, penetration test summaries, a trust center with current reports and named scope rather than a badge image.
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.
Reveal names ISO 27001, SOC 2 Type 2, CSA STAR and GDPR compliance. The trust center announces completion of the ISO 27001 audit for 2025 and lists the products certified, which shows what the certificate covers. A SafeBase trust center at a stable URL offers a self serve route to request security documentation, and SOC 2 Type 2 documentation is stated as available to enterprise clients. That is a request flow rather than a sales gate. The SOC 2 audit coverage period, the auditing firm for either certification and the ISO 27001 certificate expiry are not published.
Model Supply Chain Disclosure
Which models sit underneath, whose they are, where they run, and whether the vendor commits to telling customers when that changes. A legal buyer inherits every dependency it cannot see.
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.
Models are built in house through the Reveal AI lineage from NexLP, and there is a proprietary model library, custom models trained by customers and Brainspace analytics, all owned rather than rented. What sits behind the Ask generative feature is not published. The platform pages, the why Reveal page, the Ask resource page description, the blog and the trust center name no large language model or provider, say nothing about where generative processing runs, list no subprocessors, and make no commitment to notify customers when the supply chain changes. Reveal's own buyer guidance raises whether documents train third party AI models, which implies third party models are involved without saying whose.
Commercial Transparency
Whether a buyer can learn what this costs without entering a sales process: published rates, the unit being charged, what sits behind an enterprise tier, and what implementation adds.
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.
No pricing page is published, no rate is published, no unit of charge is stated and no tier structure appears. Every commercial path ends in a demo request. One published commercial statement addresses the buying experience without giving a figure: Reveal states it works with customers to make costs as clear and predictable as possible, framing the problem explicitly as the unexpected bill a practitioner has to take to a partner or client. That is an acknowledgment of the cost predictability problem in ediscovery rather than a disclosure of price.
Firm and Practice Coverage
Who the product is actually built for. AmLaw, midlaw, small firm and solo, in house departments, government and courts, and which practice areas are supported rather than merely claimed.
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.
Practice scope runs across the EDRM from preservation and collection through processing, early case assessment, review, analytics and production, with more than 900 file formats, translation and transcription across multiple languages, and an interface that can be localized. Investigation types are listed concretely through the model library, with more than 30 prebuilt models for recurring matters and harassment and discrimination named specifically. Segments addressed include law firms, corporate legal and compliance, and alternative legal service providers, and customers may run work themselves or through a provider. Segmentation by firm size, jurisdictional coverage stated as such, adoption figures and any statement of what the platform is not built for are not published.
The 12 legal signals, side by side
Recorded rather than graded. These are the questions a practitioner has to answer before a tool touches a client matter, and the answers are taken from public material only.
Client Data in Training
Can material a lawyer puts into this product be used to train a model?
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.
Nothing on the trust center, the platform pages, the why Reveal page, the blog or the resources index states whether customer content may be used to train models, either by Reveal or by any third party model provider. Reveal's own published buyer guidance lists whether documents are used to train third party AI models among the questions that separate a serious tool from a risky one, and those pages do not answer that question about Reveal. A dedicated security and data privacy white paper on the Ask generative feature sits behind a form submission.
Prompt and Output Retention
How long does the product keep what a lawyer typed, and can that be set to zero?
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.
Nothing on the trust center, the platform pages, the why Reveal page, the blog or the resources index states how long documents, Ask prompts or generated narrative answers are retained, whether a customer controls the window, or whether deletion is available. An organization that chooses private deployment inside its firewall holds its own discovery data inside its own perimeter and sets retention by controlling the store; for hosted customers no retention terms are stated.
Ethical Walls and Matter Segregation
Does retrieval respect the firm’s ethical walls, or can the model read across them?
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.
Granular audit logging and role based permissions are built into the platform architecture, access controls and encryption are configured within customer environments, and private deployment integrates with the organization's own authentication controls and logging frameworks, bringing the firm's identity model to the platform. Reveal's buyer guidance also raises shared multi tenant infrastructure as a question to put to any vendor, and private deployment is its answer.
No published material states that retrieval or AI model application enforces those permissions for each user at query time, and no legal document management integration is published whose access model could be inherited. Ethical walls are not named as such.
Third Party Request and Subpoena Notice
If someone subpoenas the vendor for a firm’s data, does the firm hear about it first?
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 or data processing agreement is published. No clause committing to notify a customer of a government or law enforcement request for their data, and no transparency report, appears on the platform pages, the blog, the resources index or the trust center. The trust center's published control set includes defined and implemented processes for security breach notification, which addresses telling a customer about a compromise rather than about a lawful demand.
Primary Law Corpus Provenance
Where does the law in this product come from, and does the vendor have the right to use it?
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.
The product holds no primary law corpus. Retrieval and analysis run against the customer's own collected evidence, so for that material the provenance is the discovery process itself. The AI Model Library is different. More than 30 prebuilt models are shipped ready to identify content and categorize documents by human behavior, and a pretrained model necessarily encodes whatever it was trained on. The platform pages, the why Reveal page, the blog and the resources index say nothing about what those models were trained on, whose data was used, on what rights basis, or how the models are validated or updated.
Good Law Verification
Does the product tell you when the authority it just cited has been overruled?
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.
Reveal is an ediscovery and investigations platform whose data is collected evidence rather than published case law. Nothing on the platform pages, the why Reveal page, the blog or the resources index addresses whether authority carries a treatment signal or whether subsequent history is checked, and no commercial citator license is named. Its outputs classify and summarize documents in a matter rather than citing authority.
Refusal and Uncertainty Behavior
What does the product do when the answer is not in the corpus?
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.
Nothing on the platform pages, the why Reveal page, the blog, the resources index or the trust center describes what Ask does when the evidence does not support an answer, and no explicit path for declining to answer is published. Ask returns a narrative answer with supporting materials, which lets a reader check an answer that was given but does not say when nothing supported one. Predictive scoring exposes model confidence for each document to prioritize review, which is a ranking signal on classification rather than a way to abstain on a generative answer, and the two work on different parts of the product.
Fabricated Citation Record
Does a public court record exist addressing fabricated or hallucinated legal citations in output from this product?
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 the AI tool implicated where it is known, records no case naming Reveal, and published 2026 sanctions summaries and secondary trackers name none either. Their entries name filers, and in some rows other products. This is a statement about the public record rather than a finding about the product. Reveal analyzes collected evidence rather than generating citations to authority, so its characteristic failure would be a mischaracterized document or a wrong behavioral classification rather than an invented case, and neither would ordinarily surface in a hallucination database.
Bar Guidance Alignment
Has the vendor engaged in public with the ethics opinions its buyers are bound by?
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.
The platform pages, the why Reveal page, the blog, the resources index and the Academy pages engage with no named ethics opinion or bar guidance, including ABA Formal Opinion 512 and state bar guidance. Nor do they engage with the Federal Rules of Civil Procedure or with the case law on technology assisted review and defensible process, which bears directly on how predictive coding is used in discovery. Reveal Academy trains users on the platform but does not address professional duties.
Billing and Fee Posture
Does the vendor address what happens to the bill when the work takes an hour instead of six?
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.
Reveal's published claims center on reaching relevant content faster, prioritizing review through predictive scores and reducing time to insight. Separately, Reveal says it works with customers to make costs clear and predictable, framing the problem as the unexpected bill a practitioner has to take to a partner or client. That engages with what the client ultimately pays, but it concerns the vendor's own invoice rather than the firm's bill.
The platform pages, the why Reveal page, the blog and the resources index show no record of AI assisted work for each matter intended for fee purposes and no guidance on billing, fees or disclosure to clients.
Outside Counsel Guideline Readiness
Can a firm get this vendor through a client’s AI clause without a bespoke negotiation?
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.
Available without a sales conversation: a SafeBase trust center at a stable URL with the security posture and a documentation request flow, and named current certifications, including ISO 27001 with the 2025 audit and certified product list, SOC 2 Type 2 with documentation for enterprise clients, CSA STAR and GDPR. The trust center entry point, the platform pages and the blog show no subprocessor list, no statement naming which model providers see client content, and no consent or notification pack for clients.
Court Disclosure Support
If a judge’s standing order requires an AI disclosure, can the product produce one?
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.
Granular audit logging is built into the platform architecture, Ask returns supporting materials with each narrative answer so the basis of a generative output can be traced, and the custom model workflow records training rounds and effectiveness evaluations, which documents how a classifier reached its state. These are platform features rather than a disclosure document. No export for each document combining the model used, the sources retrieved and human verification is published, and the model behind the generative feature is not named.
Predictive coding and technology assisted review have an established judicial record in ediscovery, and Reveal's AI disclosure material does not connect to it.
The questions both sides leave open
Derived from the records above rather than written, so it cannot favor either vendor. Take these into both conversations and ask each side the same question.
- Primary Law Corpus Provenance
- Good Law Verification
- Refusal and Uncertainty Behavior
- Bar Guidance Alignment
Which one fits
Choose Lawdify if
- Claims preparation depends on a continuously updated view of parties, dates and issues.
- The team wants custom skills that reuse its own expertise in future tasks.
- Word and Excel work should draw directly on the assembled case material.
Choose Reveal if
- Preservation, collection, analysis and production need to be managed together.
- Investigators want to follow plain language questions back to the source text.
- The team needs both generative answers and other discovery analytics in the same suite.
In summary
Lawdify
Lawdify combines case knowledge, specialized tasks and office based drafting for disputes teams.
Reveal
Reveal provides discovery operations, evidence analysis and cited generative answers across a matter.
Questions buyers ask
Does Reveal show the evidence behind an answer?
Yes. Its current generative workflow links answers to supporting files and specific source text so the team can inspect the basis for the finding. From the AI Legal Index, based on each vendor's own published materials as of October 16, 2026. No vendor pays for placement.
What do Lawdify's Smart Tables do?
They organize specialized tasks and changing details through the case lifecycle, alongside the wider Case Brain used for analysis and drafting. From the AI Legal Index, based on each vendor's own published materials as of October 16, 2026. No vendor pays for placement.
Where does Reveal fit before review begins?
Its wider suite supports legal holds, collection and processing, so evidence can be preserved and prepared before the review team starts analyzing it. 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.
Reveal includes several products for discovery and information management. The comparison concentrates on its evidence analysis and case preparation overlap with Lawdify.