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Reveal

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.