Everlaw AI Review: Ediscovery for Practicing Lawyers
Everlaw is one of the larger cloud-native ediscovery platforms used by U.S. litigators, and over the past two years it has shipped a steady stream of generative-AI features branded as EverlawAI Assistant. This review looks at what the platform actually does, where client data goes when you use the AI features, how it lines up against recent ethics guidance, and who it makes sense for. Basil pairs well alongside Everlaw for capturing witness interviews and internal case meetings, but this review is about Everlaw on its own merits.
What Everlaw Actually Does
Everlaw is a full-stack ediscovery and litigation platform: ingestion and processing of ESI, review with predictive coding, production, depositions, and trial preparation, all delivered through a browser. The core workflow — load data, cull, review, tag, produce — is what you would expect from a modern review platform, and Everlaw has a reputation for a clean interface and fast search.
The AI story has two layers. The older layer is classical predictive coding and clustering, which uses supervised models trained on your reviewers' coding decisions to prioritize documents. The newer layer, EverlawAI Assistant, layers generative-AI features on top: document summarization, deposition-prep question generation, coding suggestions with written rationales, and review of investigation-style questions against a document set. Everlaw has publicly stated these features are built on large language models accessed through Microsoft Azure OpenAI Service.
In practice, the generative features are most useful for early-case-assessment triage — getting a quick read on what a custodian's inbox is about — and for drafting deposition outlines from key documents. They are not a substitute for lawyer review, and Everlaw does not market them as one.
The Confidentiality Lens
This is the section that matters most for lawyers, because ediscovery inherently involves other people's most sensitive documents, often under protective order.
Where the data lives. Everlaw is a cloud platform hosted on Amazon Web Services, with an option for Google Cloud in some regions. Client data is stored in AWS regions selected at the account level. Everlaw publishes a Trust Center with SOC 2 Type II, ISO 27001, and FedRAMP Moderate authorization for its GovCloud environment. There is no on-premises or local-only deployment option for the standard product.
Model training. Everlaw's published position is that customer data is not used to train the underlying foundation models, and that its Azure OpenAI configuration disables data logging for model improvement. That is consistent with Microsoft's standard Azure OpenAI data-handling terms, under which prompts and completions are not shared with OpenAI and are not used to train Microsoft or OpenAI models. Predictive coding models are trained on your data but scoped to your matter or account.
Retention. Documents remain in the workspace until you delete them or the account terminates; standard contractual terms address post-termination deletion windows. AI prompt and response logs are subject to a shorter retention window described in Everlaw's AI documentation, and abuse-monitoring logs on the Azure side default to 30 days, which enterprise customers can sometimes have zeroed out.
What a subpoena served on the vendor could reach. Because Everlaw is a hosted platform, a subpoena or search warrant served on Everlaw could, as a technical matter, reach the databases holding your loaded review sets, coding, work-product tags, and AI prompt logs, subject to Everlaw's obligation under its terms to notify customers where legally permitted and to push process back to the client. This is the standard cloud-ediscovery risk profile — the same one that exists with Relativity-hosted vendors — not a defect unique to Everlaw. The mitigations are contractual (notice provisions, narrow data-processing terms) and operational (limiting what you load and how long it sits).
DPA and BAA. Everlaw offers a Data Processing Addendum for GDPR/UK data and will sign HIPAA Business Associate Agreements for qualifying customers. Firms handling regulated data should ask for both in writing before loading anything sensitive.
For comparison, a lawyer-built on-device tool like Basil for Law keeps meeting audio and transcripts on the device by design, so the vendor-subpoena vector effectively does not exist for that piece of the workflow. Everlaw solves a different problem — hosted collaborative review at scale — and the tradeoff is inherent to that model.
Ethics-Opinion Fit (ABA 512)
ABA Formal Opinion 512 (July 2024) sets out what practicing lawyers need to do before feeding client information into a generative-AI tool: understand the technology, evaluate confidentiality risks, get informed client consent where appropriate under Model Rule 1.6, supervise outputs, and consider billing implications. NYC Bar Formal Opinion 2025-6 (Dec 2025) reinforces the diligence obligation for cloud-based AI vendors.
Everlaw gives you a reasonable amount of what Opinion 512 asks for: published documentation of how EverlawAI works, contractual data-handling commitments, audit certifications, and the ability to enable AI features selectively at the matter or user level. The gaps you have to close yourself are the client-consent conversation (which no vendor can do for you) and the supervision layer — you still need to verify AI outputs against source documents before they leave your firm. The recent decision in US v. Heppner (S.D.N.Y. Feb 2026), holding that a litigant's chats with a public consumer AI platform were not privileged, is a useful reminder to your clients about the distinction between consumer chatbots and contracted enterprise tools like Everlaw. Opinion 512 does not bless any product; it sets the diligence bar, and Everlaw's documentation makes it feasible to clear that bar for most matters.
The AI Features, Feature by Feature
Document summaries. Solid for getting a fast read on long emails and attachments. Hallucination risk is present, as with any LLM summary, and Everlaw provides citations back to source text, which materially reduces the verification burden.
Coding suggestions with rationales. The written rationale is helpful for QC — a reviewer can see why the model flagged something as responsive rather than just accepting a probability score. It is not a substitute for a privilege-log-quality review.
Deposition prep. Generates outline questions from a set of hot documents. Useful as a first draft; every question still needs a lawyer's judgment about strategy, form, and admissibility.
Investigation queries. Free-text questions run against a document set, returning answers with citations. This is where the platform is most impressive and also where you should be most careful about verification, because a confidently wrong answer at the investigation stage can steer a case in the wrong direction.
Pricing and Who It's For
Everlaw does not publish list prices. Pricing is typically per-gigabyte-per-month for hosting plus per-user fees, with AI features often billed as an add-on module or included at higher tiers. Ask for a written quote scoped to your expected data volume, and confirm whether EverlawAI is included or separately metered. See the Everlaw pricing page for the current commercial structure.
Everlaw is a good fit for litigation boutiques, mid-size and large firms, corporate legal departments, and government offices that already run cloud ediscovery and want AI features integrated into the same review environment rather than bolted on. It is probably overkill for a solo who handles one or two document-heavy matters a year — a lighter, matter-priced platform will be cheaper. It is also not the right tool if your ethical or contractual posture requires that source documents never leave your infrastructure; in that case you are looking at on-premises Relativity or a similar deployment.
Verdict
Everlaw is a mature, well-run cloud ediscovery platform with a credible and improving AI layer. The confidentiality architecture is standard-for-category rather than exceptional — cloud hosting means vendor-facing risk you have to manage contractually — but the documentation, certifications, and Azure OpenAI configuration give you the raw material to conduct the diligence ABA Opinion 512 expects. If your practice is already comfortable with cloud ediscovery, EverlawAI is worth a pilot on a live matter with a real budget.
| Pros | Cons |
|---|---|
| Clean, fast review interface with strong search | Cloud-only; no on-prem option |
| AI features cite back to source documents | AI is an add-on cost at most tiers |
| Published trust documentation and audit certifications | Vendor-facing subpoena exposure inherent to hosted model |
| Azure OpenAI configuration excludes prompts from model training | Opaque list pricing requires a sales conversation |
| DPA and BAA available for qualifying customers | Overkill for low-volume solo practices |
This review is for information only and is not legal advice.
Frequently asked questions
Does Everlaw use my client data to train AI models?
Everlaw's published position is that customer data is not used to train the underlying foundation models. EverlawAI runs on Microsoft's Azure OpenAI Service, whose standard terms prevent prompts and completions from being shared with OpenAI or used to train Microsoft or OpenAI models. Predictive coding models are trained on your matter data but are scoped to your account. Confirm the current terms in your MSA before loading sensitive data.
Can Everlaw sign a Business Associate Agreement for PHI?
Everlaw offers a Business Associate Agreement for qualifying customers handling protected health information, and a Data Processing Addendum for GDPR and UK data. Both should be executed before loading regulated data. Ask your Everlaw account representative in writing and keep the signed copies with your matter file.
How does Everlaw fit with ABA Formal Opinion 512?
Opinion 512 requires lawyers to understand the AI tool, evaluate confidentiality, obtain informed client consent where appropriate under Model Rule 1.6, and supervise outputs. Everlaw's public documentation, audit certifications, and contractual data-handling commitments give you the material to conduct that diligence. The client-consent conversation and output supervision remain the lawyer's responsibility.
Is Everlaw a good fit for solo practitioners?
For most solos, Everlaw is more platform than the practice needs and more cost than a one-off matter can absorb. It shines for litigation boutiques, mid-size and large firms, and in-house teams running multiple document-heavy matters. Solos with an occasional large matter often get better economics from matter-priced platforms.
What happens to my data if I leave Everlaw?
Standard cloud ediscovery contracts include a post-termination window during which you can export data before deletion. Confirm the specific window, export formats, and deletion certification process in your Everlaw agreement, and build the offboarding steps into your matter closing checklist so nothing is left orphaned on the platform.
Does Everlaw offer an on-premises deployment?
No. Everlaw is a cloud-native platform hosted on AWS, with a FedRAMP Moderate GovCloud environment for qualifying government customers. If your matter or client requires that data never leaves your own infrastructure, Everlaw is not the right tool and you should look at on-premises platforms.
Meeting notes with no server to subpoena
Basil transcribes and summarizes entirely on-device — privilege-safe by architecture. See Basil for Law →
This review is for information only and is not legal advice.