Reveal
AI powered ediscovery and investigations platform covering the full EDRM from legal hold and processing through early case assessment, review, analytics and production, handling more than 900 file formats. Assembled through acquisition: Mindseye for processing, NexLP for data science becoming Reveal AI, and Brainspace for visual analytics and concept search, integrated in Reveal 11. Machine learning runs throughout rather than sitting in one module, spanning supervised and unsupervised learning, deep learning and natural language processing, with an AI Model Library of more than 30 pre built reusable models for recurring investigation types such as harassment and discrimination, customer built custom models trained through AI driven batches with interactive simulations, predictive scoring, concept search, cluster visualisations, translation and transcription across languages, and sentiment analysis the vendor markets as emotional intelligence. Ask is the first generative feature, taking deposition style natural language questions and returning a narrative answer with supporting materials. Cloud and private in firewall deployment are both offered, with private deployment investment expanded by half on stated enterprise demand. Backed by a $250m investment from K1 Investment Management.
Capability grades
All 15 axes, graded from public sources on the date shown. 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 first A on this axis for an ediscovery platform, and it is earned on company structure rather than marketing. Reveal was assembled by acquiring model capability and building the platform around it: NexLP and its data science team became Reveal AI, Brainspace brought visual analytics and concept search, and Reveal 11 was released explicitly as the integration of Brainspace, Reveal AI and Reveal Review into one platform. The model layer is not a feature on top of a review tool, it is the thing the review tool was assembled around. Capability spans supervised and unsupervised learning, deep learning and natural language processing, with an AI Model Library of more than 30 reusable pre built models, customer trained custom models, predictive scoring, concept clustering, translation and transcription, sentiment analysis and the Ask generative feature. Distinguished deliberately from Everlaw and Relativity, both graded B, where a document review platform predates and stands without the model layer. Here removing the models removes most of the product.
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.
Grounding is real and documented, short of published measurement. Ask is described as returning a narrative answer with supporting materials, so output ties back to the documents it rests on and a reviewer can open them, and the vendor publishes a dedicated security and data privacy white paper addressing the generative feature specifically rather than folding it into general marketing. On the predictive side the vendor describes evaluating model effectiveness after each training round and using interactive simulations to decide how long to train, which is a described evaluation mechanism in the customer's hands, and it publishes that predictive scores are used to prioritise review. Searched the platform pages, the why Reveal page, the Ask resource page, the blog and the trust centre on 29 Aug 2026 and located no accuracy figure, no precision or recall number for either the pre built models or Ask, no hallucination rate, no test set and no independent benchmark participation. The vendor describes its predictive scores as 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.
A real published commitment with genuine control surfaces, short of thresholds. The control model is unusually concrete because so much of the AI is customer trained: a user builds custom models as easily as creating a tag, trains them through AI driven batches that select documents intelligently, evaluates effectiveness after each training round, and uses interactive simulations to decide when training is sufficient. That places the decision about whether a model is good enough with the customer and gives them the instrument to make it. Predictive scores prioritise review rather than replacing it, and Ask returns supporting materials alongside its narrative answer. Not located as of 29 Aug 2026: any published threshold at which a model is considered fit to rely on, what Ask does unaided, and what the vendor 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.
Partner and testimonial material stands where deployment evidence would go. 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 is published on the vendor site. Corporate history is documented with specifics that are checkable, including a $250m investment from K1 Investment Management and a sequence of named acquisitions. But none of that is deployment outcome. Searched the platform pages, the why Reveal page, the blog and the resources index on 29 Aug 2026 and located no named customer paired with figures and a date, no case study with an assessable method, and no adoption count comparable to those published by the other two ediscovery vendors on this 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.
Substantive published commitments with a dedicated document on the generative feature, short of the training limb. The vendor publishes a security and data privacy white paper specifically addressing the Ask feature and the use of generative AI, which is a targeted disclosure few vendors here produce, and states a commitment to the confidentiality and integrity of customer data backed by ISO 27001 and SOC 2 Type 2 certification. Private in firewall deployment is offered as an architectural answer to confidentiality, letting an organisation keep discovery data inside its own perimeter with deep integration to internal authentication and logging. Granular audit logging and role based access controls are published. What holds this off an A is that the training question is not answered on the surfaces reached: the vendor's own guidance tells buyers to ask whether their documents are used to train third party AI models, and no statement of Reveal's own position on that was located outside the gated white paper, which requires a form submission. Retention terms were not located 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.
The audience is professional and the position is unstated. Users are law firms, corporate legal and compliance teams, government and alternative legal service providers, with no consumer surface located, and the product analyses collected evidence rather than giving advice, so the advice line question arises less sharply than for a research or drafting tool. Searched the platform pages, the why Reveal page, the blog and the Academy pages on 29 Aug 2026 and located no published position on advice versus tooling, no treatment of competence or supervision duties as professional obligations, and no jurisdiction limits despite translation and transcription across multiple languages implying cross border work. Recorded as adjacent rather than credit: Reveal Academy runs user certification courses across platform functions, which addresses operator competence commercially rather than as the professional duty it also is, the same pattern recorded for Relativity.
AI Governance and Bias Disclosure
Published governance over model behaviour: who owns it inside the vendor, what is tested before release, and what is disclosed about disparate output across matter types, parties, or populations.
A published pledge without a governance mechanism behind it. The vendor states three pillars of an AI pledge, Trust, Knowledge and Security, and publishes buyer guidance on choosing generative AI tools that tells readers to ask whether their documents train third party models, how access controls and audit logging are implemented, how data residency is handled and what certifications a vendor holds. Publishing the questions a buyer should ask its vendors is a real editorial contribution and is unusual. But a pledge of three words is not a framework, and searched the platform pages, the why Reveal page, the blog and the trust centre on 29 Aug 2026 and located no published AI governance framework document, no AI management certification such as ISO 42001, no named owner of model governance, no pre release testing regime, and nothing on uneven output across matter types, parties or populations. The last is a live question for this vendor specifically: it ships more than 30 pre built models for categorising documents by human behaviour including harassment and discrimination, and publishes nothing about 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.
Substantive published policy covering most of the ground, with a trust centre providing the route to the rest. Published: ISO 27001 certification with a 2025 audit completed and the certified products listed, SOC 2 Type 2 certification with documentation available to enterprise clients, CSA STAR and GDPR compliance, granular audit logging and role based access controls built into the platform architecture, end to end encryption, support for data residency requirements in regulated jurisdictions, and private in firewall deployment. A SafeBase trust centre is published at a stable URL carrying the security posture and a documentation request route, and its published control set includes breach notification processes and procedures defined and implemented, and policies reviewed and updated at least annually. Not located as of 29 Aug 2026: a stated retention period or deletion control for customer data, prompts or Ask outputs, and a named subprocessor list.
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.
Searched the platform pages, the why Reveal page, the blog, the resources index and the trust centre on 29 Aug 2026. No published indemnity, liability cap, carve out, warranty on output or insurance position was located, and no customer agreement or master terms was located on the surfaces reached. Recorded as a pure absence on those surfaces. Worth noting the vendor does publish a commercial commitment adjacent to this, that it works to make costs clear and predictable and explicitly frames the problem of an unexpected bill reaching a partner or client, so it has thought about commercial risk to the customer without addressing liability for output. Rebuttable with one link.
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.
Integration is described at architectural level without named connectors. The strongest published statement concerns private deployment, where the vendor states the architecture allows deep integration with internal authentication controls, logging frameworks and enterprise security policies, which is integration at the identity and audit layer rather than the file layer and is a real capability. Processing handles more than 900 file formats, which reduces the ingestion problem a connector would otherwise solve. Searched the platform pages, the why Reveal page, the blog and the resources index on 29 Aug 2026 and located no integrations index page, no named connector for collection sources such as Microsoft, Google, Slack or Box, and no API documentation. That contrasts directly with the other two ediscovery records on this index, both of which name collection connectors individually.
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.
The strongest deployment position in the ediscovery category and among the strongest on the index. Three models are published and the differences between them are real: cloud, hybrid, and private in firewall deployment where discovery data stays inside the organisation's own perimeter. Private deployment is not a legacy option being quietly maintained but an area of active investment, with the vendor announcing it expanded that investment by 50 percent on stated enterprise demand and publishing architectural material on how it works, covering secure infrastructure, scalable performance, consistent compliance inside the firewall and deep integration with internal authentication and logging. Data residency is addressed directly as support for residency requirements in regulated jurisdictions, and the vendor's own buyer guidance tells readers to insist on data locality options because data sometimes must stay within a region or country. Short only of naming the available cloud regions and stating the tenancy model for the hosted option, neither of which was located as of 29 Aug 2026.
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.
Certification is named, current and reachable through a published portal, short of scope and auditor detail. Named: ISO 27001, SOC 2 Type 2, CSA STAR and GDPR compliance. Currency is evidenced rather than assumed, the trust centre announcing completion of the ISO 27001 audit for 2025 and listing the products certified, which addresses the question of what the certificate actually covers more directly than most vendors do. A SafeBase trust centre is published at a stable URL as a self serve route to request security documentation, and SOC 2 Type 2 documentation is stated as available to enterprise clients. Under the three tier test that is a request flow rather than a sales gate. Not located as of 29 Aug 2026: the SOC 2 audit coverage period, the named auditing firm for either certification, and the ISO 27001 certificate expiry. Compare Relativity, held at C in the same category for claiming certifications without naming any.
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.
The architecture is described in detail and the third party components are not identified. What is published is substantial on the vendor's own layer: models built in house through the Reveal AI lineage from NexLP, a proprietary model library, customer trained custom models, and Brainspace analytics, all of which a buyer can understand as owned rather than rented. What is not published is what sits behind the Ask generative feature. Searched the platform pages, the why Reveal page, the Ask resource page description, the blog and the trust centre on 29 Aug 2026 and located no named large language model or provider, no statement of where the generative processing runs, no subprocessor list, and no commitment to notify customers when the supply chain changes. The vendor's own buyer guidance instructs readers to ask whether their documents are used to train third party AI models, which implies third party models are in the picture 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.
Checked the platform pages, the why Reveal page, the blog and the resources index on 29 Aug 2026. No pricing page was located, no rate is published, no unit of charge is stated and no tier structure appears on the surfaces reached. Every commercial path located terminates in a demo request. One published commercial statement was located and is recorded here because it addresses the buying experience without giving a figure: the vendor 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 acknowledgement of the cost predictability problem in ediscovery rather than a disclosure of price, and it does not lift the grade.
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.
Practice coverage is described with real substance and segment coverage more thinly. Practice scope is stated end to end across the EDRM from preservation and collection through processing, early case assessment, review, analytics and production, with more than 900 file formats supported, translation and transcription across multiple languages, and a localisable interface. Investigation types are enumerated concretely through the model library, with more than 30 pre built models aimed at recurring matters and harassment and discrimination named specifically, which is a more granular statement of what the product is for than most records here provide. Segments addressed include law firms, corporate legal and compliance, and alternative legal service providers, with the vendor noting customers may run work themselves or through a provider. Not located as of 29 Aug 2026: firm size segmentation, jurisdictional coverage stated as such, adoption figures, and any statement of what the platform is not built for.
Legal Signals
What each signal meansA signal records what public sources say on the date shown. It is not a grade and it is not a recommendation. Where a signal reads Not addressed, it means the index did not locate the material in public sources on that date, which is a statement about disclosure rather than about the product.
Client Data in Training
Can material a lawyer puts into this product be used to train a model?
No located term or policy addresses the question either way.
Searched the platform pages, the why Reveal page, the blog, the resources index and the trust centre on 29 Aug 2026. No located material states whether customer content may be used to train models, either by the vendor or by any third party model provider. Recorded as silent under the rule that a value is never inferred from the absence of a contradiction. The absence is conspicuous rather than neutral here, because the vendor's own published buyer guidance instructs readers to ask a prospective vendor whether their documents are used to train third party AI models, listing it among the questions that separate a serious tool from a risky one, and does not answer that question about itself on the surfaces reached. A dedicated security and data privacy white paper on the Ask generative feature is published but sits behind a form submission and was not retrieved, so the answer may exist there.
Prompt and Output Retention
How long does the product keep what a lawyer typed, and can that be set to zero?
No located public material states how long prompts and outputs are retained.
Searched the platform pages, the why Reveal page, the blog, the resources index and the trust centre on 29 Aug 2026. No public material on these surfaces states how long documents, Ask prompts or generated narrative answers are retained, whether a customer controls the window, or whether deletion is available. Noted for a reader as a genuine mitigation rather than an answer: private in firewall deployment means an organisation choosing that option holds its own discovery data inside its own perimeter and therefore sets retention by controlling the store, which removes the question for those customers without addressing it for the hosted ones.
Ethical Walls and Matter Segregation
Does retrieval respect the firm’s ethical walls, or can the model read across them?
The product maintains its own permission model, documented, requiring the firm to keep it aligned.
The product maintains its own documented permission model, described at a useful level of specificity. Published: granular audit logging and role based permissions built into the platform architecture rather than layered on, access controls and encryption configured within customer environments, and for private deployment, deep integration with the organisation's own authentication controls and logging frameworks, which brings the firm's identity model to bear on the platform. The vendor's buyer guidance also raises shared multi tenant infrastructure as a question a buyer should put to any vendor, and private deployment is its answer to that. Recorded at own model documented rather than the positive value because no published material states that retrieval or AI model application enforces those permissions per user at query time, and no legal document management integration was located whose access model could be inherited. Ethical walls are not named as such anywhere located.
Third Party Request and Subpoena Notice
If someone subpoenas the vendor for a firm’s data, does the firm hear about it first?
No located term or policy addresses third party requests for customer data.
Searched the platform pages, the blog, the resources index and the trust centre on 29 Aug 2026, and no published customer agreement or data processing agreement was reached. No clause committing to notify a customer of a government or law enforcement request for their data was located, and no transparency report was located. The trust centre'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, and the two were not conflated.
Primary Law Corpus Provenance
Where does the law in this product come from, and does the vendor have the right to use it?
No located public material identifies the corpus behind the product’s answers.
No primary law corpus is identified because the product does not hold one, but this vendor raises a related provenance question that none of the others do. Retrieval and analysis run against the customer's own collected evidence, so for that corpus the provenance is the discovery process itself and no vendor corpus would be expected. What is different here is the AI Model Library: more than 30 pre built models are shipped ready to identify content and categorise documents by human behaviour, and a pre trained model necessarily encodes whatever it was trained on. Searched the platform pages, the why Reveal page, the blog and the resources index on 29 Aug 2026 and located no statement of what those models were trained on, on whose data, on what rights basis, or how they are validated or updated. Recorded as not addressed on that basis rather than as inapplicable.
Good Law Verification
Does the product tell you when the authority it just cited has been overruled?
No located public material addresses whether authority is checked for subsequent history.
Searched the platform pages, the why Reveal page, the blog and the resources index on 29 Aug 2026. No material was located addressing whether authority carries a treatment signal or whether subsequent history is checked, and no commercial citator licence was located. Noted for context: this is an ediscovery and investigations platform whose corpus is collected evidence rather than published case law, so a citator is outside its design entirely, consistent with both other ediscovery records on this index.
Refusal and Uncertainty Behaviour
What does the product do when the answer is not in the corpus?
No located public material addresses what the product does when it cannot ground an answer.
Searched the platform pages, the why Reveal page, the blog, the resources index and the trust centre on 29 Aug 2026. No published material describes what Ask does when the evidence does not support an answer, and no explicit no answer path was located. Two adjacent features were considered and not treated as satisfying this signal. Ask returns a narrative answer with supporting materials, which lets a reader check an answer that was given rather than telling them when nothing supported one. And predictive scoring exposes model confidence per document for review prioritisation, which is a ranking signal on classification rather than an abstention path on a generative answer, and the two operate on different parts of the product.
Fabricated Citation Record
Does a public court record exist involving output from this product?
No court order, opinion or disciplinary record naming this product has been located as of the date shown. This is a statement about the public record, not a finding about the product.
No court order, opinion or disciplinary record naming this product has been located as of 29 Aug 2026. Instrument searched: the AI Hallucination Cases database maintained by Damien Charlotin, which tracks court decisions worldwide involving AI generated hallucinated content and records the AI tool implicated where it is known. Also checked published 2026 sanctions summaries and secondary sanctions trackers. The entries located name filers, and in some rows other products, rather than this one. This is a statement about the public record on the date shown and not a clearance. Note the exposure differs from a research tool: this product analyses collected evidence rather than generating citations to authority, so its characteristic failure would be a mischaracterised document or a wrong behavioural classification rather than an invented case, and neither would ordinarily surface in a hallucination database.
Bar Guidance Alignment
Has the vendor engaged in public with the ethics opinions its buyers are bound by?
No located public material engages with bar or ethics guidance.
Searched the platform pages, the why Reveal page, the blog, the resources index and the Academy pages on 29 Aug 2026. No engagement with any named ethics opinion or bar guidance was located, including ABA Formal Opinion 512 and state bar guidance. Also not located, and consistent with the other two ediscovery records here: any engagement with the Federal Rules of Civil Procedure or with the case law on technology assisted review and defensible process. That is now an absence recorded across all three ediscovery vendors on this index, which makes it a category pattern rather than a vendor failing.
Billing and Fee Posture
Does the vendor address what happens to the bill when the work takes an hour instead of six?
Public materials claim time savings without addressing billing or disclosure.
Savings are implied through efficiency framing and the vendor addresses cost from an unusual direction. Published claims centre on getting to relevant content faster, prioritising review through predictive scores and reducing time to insight. Separately and more distinctively, the vendor addresses cost predictability directly, stating it works with customers to make costs clear and predictable and 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, which is adjacent to this signal and rarer than the usual hours saved claim, but it concerns the vendor's own invoice rather than the firm's. Searched the platform pages, the why Reveal page, the blog and the resources index on 29 Aug 2026 and located no per matter record of AI assisted work intended for fee purposes and no guidance on billing, fee or client disclosure treatment.
Outside Counsel Guideline Readiness
Can a firm get this vendor through a client’s AI clause without a bespoke negotiation?
The material exists behind a sales conversation or an executed agreement.
A published trust centre provides the route, and the specific artifacts this signal names are not behind it on the surfaces reached. Available without a sales conversation: a SafeBase trust centre at a stable URL carrying the security posture with a documentation request flow, named current certifications including ISO 27001 with the 2025 audit completion and certified product list, SOC 2 Type 2 with documentation stated as available to enterprise clients, CSA STAR and GDPR. A firm could evidence a good deal from that. Searched the trust centre entry point, the platform pages and the blog on 29 Aug 2026 and located no subprocessor list, no statement naming which model providers see client content, and no client facing consent or notification pack. Recorded at on request on the strength of the published documentation route falling short of the named artifacts.
Court Disclosure Support
If a judge’s standing order requires an AI disclosure, can the product produce one?
Some elements of the record are available, short of a document level export.
Several elements of a record are available, assembled from platform features rather than offered as a disclosure artifact. Granular audit logging is published as built into the platform architecture rather than added on, Ask returns supporting materials alongside each narrative answer so the basis of a generative output is traceable, and the custom model workflow records training rounds and effectiveness evaluations, which is a documented account of how a classifier reached its state. Two elements are missing: no per document export combining model used, sources retrieved and human verification was located, and no model is named in published material so the model used could not be stated for the generative feature. Recorded at partial record. Noted for a reader: predictive coding and technology assisted review have an established judicial record in ediscovery, and none of the three ediscovery vendors on this index connects its AI disclosure material to it.