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Everlaw

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.

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.

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.

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.

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 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.