DISCO vs Everlaw: how they compare in 2026
DISCO and Everlaw are cloud ediscovery platforms with generative AI built into review, and both now publish their contracts, data processing terms and subprocessor lists. DISCO prices on processed data at a rate per GB with unlimited Cecilia AI included. Auto Review runs first pass review at up to 32,000 documents an hour, with an explanation for every tag. DISCO's terms add a warranty that the software conforms to its documentation and a separate data protection cap at twice the general cap, and its operator's annual filing reports 1,549 customers at the end of 2025. Everlaw publishes a dated customer study, Dinsmore & Shohl's 98.2% recall on 26,000 contracts, and lets customers choose among five AWS regions with AI processing kept in region. It holds FedRAMP Moderate and GovRAMP authorizations. A team that wants volume pricing with AI included leans to DISCO. One that wants its evidence reachable from Claude, ChatGPT Enterprise, Gemini, Copilot, Harvey or Legora leans to Everlaw, which connects to them through API and MCP connectors.
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.
The models drive a core layer on a platform that works without them. DISCO Ediscovery covers upload, filtering, review and production, and Cecilia AI arrived in 2023, about a decade after the company was founded. Cecilia Q&A, single document Q&A, summaries, definitions and Auto Review's first pass classification run on language models. DISCO now sells unlimited Cecilia AI inside its per GB rate, so the generative features come with every matter on the platform.
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.
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.
Cecilia Q&A answers only from the customer's evidence, with citations, and Auto Review gives an explanation for every tagging decision so a reviewer sees why each classification was made. Auto Review tracks recall and precision inside the product, and DISCO's AI consulting team advises on sampling and validation. DISCO says Auto Review delivers precision and recall 10 to 20 percent above human reviewers. No test set, date, evaluator or method accompanies that figure, so an outsider cannot check or reproduce it.
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.
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.
Auto Review suggests tags with an explanation for each, and the team moves to quality control on those suggestions. Customers choose whether to run Auto Review themselves, refining tags with Tag Tuner, or to have DISCO's Managed Review team run the project, including privilege review. Recall and precision are tracked in the product so a team can validate the review before relying on it. Cecilia answers cite the evidence they draw on. No threshold at which a document must go to a person, and no limit on what Auto Review decides unaided, is published.
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.
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.
CS Disco's annual filing, a regulated document, reports 1,549 customers including 330 large customers and a 98 percent dollar based net retention rate at 31 December 2025. Named customer material is published too. A Perkins Coie associate is quoted on Auto Review, and a story on Rhoades McKee describes DISCO's professional services on an inherited multi jurisdiction case. Auto Review is said to deliver precision and recall 10 to 20 percent above human reviewers, with no test set, date or method given. No named customer is paired with dated figures and a method a reader can assess.
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.
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 language models used in Cecilia never train on or learn from customer data and usage, DISCO says, and do not retain customer data after a query. The models behind Auto Review are prohibited from training on or retaining customer data after processing, and model vendors must hold SOC 2 Type 2 or ISO 27001 certification. Data is encrypted in transit and at rest. The data processing addendum binds DISCO to confidentiality, breach notice and deletion or return of personal data on termination, and notice of subpoenas or other orders seeking personal data. No position on privilege or work product as such, and no separation between users or matters inside a customer, is published.
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.
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.
The buyers are law firms, corporate legal teams and government legal teams, with no consumer product, and DISCO analyzes collected evidence rather than giving advice. Auto Review instructions can be written with DISCO's AI consulting team, and its Managed Review team can run a first pass review and handle privilege review, which places vendor staff inside the customer's review method. Nothing published addresses advice versus tooling, the supervising lawyer's responsibility for a review DISCO's team runs, competence duties or jurisdiction limits.
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.
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.
Every Auto Review tag suggestion carries an explanation, and Auto Review tracks recall and precision inside the product so a team can validate a review. DISCO's AI consulting team advises on sampling, validation and defensibility. The annual filing discusses AI related operational and regulatory risk beside cybersecurity, privacy compliance and reliance on third party model and cloud providers. No AI governance framework, AI principles, ISO 42001 certification, named owner of model governance or prerelease testing regime is published. Nothing addresses uneven output across matter types, parties or populations, for a product that classifies up to 32,000 documents an hour.
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.
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.
DISCO's data processing addendum commits to notice of a personal data breach without undue delay, with assistance afterward, and on termination DISCO deletes or returns customer personal data or provides a way to do so. Its subprocessor list names each provider with function and country, and customers have 14 days to object after the list is updated. The language models behind Cecilia and Auto Review do not train on or retain customer data after processing, and every model vendor must hold SOC 2 Type 2 or ISO 27001 certification. Data is encrypted in transit and at rest on AWS. No period is stated for keeping case documents, Cecilia prompts or generated output, and retention under DISCO's own document retention policies is reserved.
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.
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.
DISCO's terms and conditions indemnify the customer against third party claims that the technology offerings or documentation infringe United States intellectual property rights, with the usual exclusions. DISCO warrants that each technology offering conforms in all material respects to its documentation, and that professional services are performed in a professional and workmanlike manner, with correction or termination as the remedy. Each party's liability is capped at the fees paid or payable under the order in the 12 months before the claim. A separate data protection cap of twice the general cap covers breaches of data protection obligations. Fraud, wilful misconduct, negligence causing death or injury and indemnification obligations sit outside the caps. The AI supplementary terms exclude AI services from any service level agreement, and no insurance position is published.
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.
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.
DISCO Hold connects to a customer's systems, and the high speed uploader moves enterprise data straight into the platform. DISCO publishes guidance on Google Workspace collection through Google Vault and on migrating data from other ediscovery providers. A DISCO API is published for connecting the platform to a firm's other systems, and the subprocessor list names Box and Dropbox as file transfer tools. The consolidated integrations page now returns a not found page, and no documentation per integration sets out what moves, in which direction or what an administrator configures.
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.
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.
DISCO runs in the cloud on AWS, which its subprocessor list places in the United States, Ireland and Canada. Microsoft Azure and Google appear as further hosting providers in the United States, and OpenAI processes in the United States and the EU. The platform was designed to operate in the cloud, with AWS used to minimize data footprint and strengthen privacy controls. The data processing addendum sets out transfer mechanisms for data leaving the EEA, the UK and Switzerland. No customer choice of region, tenancy model or private deployment is described.
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.
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.
DISCO undergoes an annual SOC 2 Type 2 audit covering security, availability and privacy, holds ISO 27001 certification for the technical and operational delivery of its service, and commissions penetration testing. Data is encrypted in transit and at rest on AWS, and DISCO states GDPR compliance. The security page links a trust center at trust.csdisco.com, and the data processing addendum points to it for the subprocessor list. Customers can use two factor authentication, expiring passwords and single sign on through SAML, and a vulnerability reporting route is published. The auditing firm and the SOC 2 coverage period are not stated on the public pages.
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.
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.
DISCO's subprocessor list names Writer and OpenAI as large language model providers, Writer in the United States and OpenAI in the United States and the EU, and Pinecone as the vector database. AWS, Microsoft Azure and Google are named as hosting providers. The data processing addendum gives customers 14 days to object after the list is updated on DISCO's trust center. Model vendors must hold SOC 2 Type 2 or ISO 27001 certification and may not train on or retain customer data. No model or version is tied to a particular feature.
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.
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.
DISCO publishes its unit and structure without a figure. Its pricing page bills on processed data at a rate per GB, with no additional charge for unlimited use of Cecilia AI on the platform. The per GB rate includes filters, Cecilia Q&A, single document Q&A, document summaries, definitions, timelines and deposition management, and a feature table sets out what is included. The rate itself comes through a quote. The annual filing records the 2026 move to usage based pricing.
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.
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.
DISCO sells to law firms, corporate legal teams and government, with dedicated material for IP litigation, construction litigation, plaintiffs and a pro bono program. Its annual filing counts 1,549 customers, including 330 large customers, at 31 December 2025. The platform spans the ediscovery lifecycle with hold, ediscovery, review, timelines, deposition management and case building, and pricing is quoted for customers across the United States, the United Kingdom, Europe and parts of Asia. Firm size segmentation, language coverage and any statement of what the platform is not built for are not published.
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.
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?
According to DISCO's AI page, the language models used in Cecilia never train on or learn from customer data and usage. The Auto Review page adds that its models are prohibited from training on or retaining customer data after processing. Both statements address the model layer, and the terms and conditions and AI supplementary terms contain no training clause. Nothing addresses whether DISCO itself trains or tunes any model on customer content.
Under its Customer Terms of Service, Everlaw will not use inputs or outputs to train or fine tune a generative AI model for general use through the service. Inputs and outputs are case materials the customer owns. The AI governance framework says the enterprise language models follow zero data retention terms, deleting data on completion and never using it to train their models. Predictive Coding learns from the coding decisions within a customer's own matter.
Prompt and Output Retention
How long does the product keep what a lawyer typed, and can that be set to zero?
Auto Review's language models may not retain any customer data after processing, and every model vendor must hold SOC 2 Type 2 or ISO 27001 certification. On termination, the data processing addendum has DISCO delete or return customer personal data, subject to retention its own policies or the law require. No period is stated for how long DISCO keeps case documents, Cecilia prompts, generated answers or the explanation attached to each tag suggestion, and no customer setting for that window is described.
Data sent to the language models is deleted on completion and not kept after the request. Everlaw keeps customer data only as long as the customer stores it on the platform, and does not retain deleted data unless the law requires it. The Pinecone vector database behind Deep Dive stores numerical embeddings created from customer documents, which retrieval needs. Termination instructs Everlaw to delete customer data within a commercially reasonable timeframe, and no period is stated for the embeddings or for deletion after termination.
Ethical Walls and Matter Segregation
Does retrieval respect the firm’s ethical walls, or can the model read across them?
DISCO describes no separation between users, teams or matters inside one customer and no ethical wall feature. Its published controls cover single sign on, two factor authentication and the vendor's own supplier and communications security. For a platform sold to large firms running concurrent matters, how access is screened between matters is not described.
Everlaw says data submitted and responses received are not shared between customers, and the models hold no persistent knowledge across requests. Nothing published sets out how separation is enforced between users, teams or matters inside one customer, where walls operate. A firm running two adverse matters on the platform has no published description of whether Deep Dive retrieval can cross between them.
Third Party Request and Subpoena Notice
If someone subpoenas the vendor for a firm’s data, does the firm hear about it first?
Under DISCO's data processing addendum, each party must notify the other promptly in writing of any subpoena or other judicial or administrative order seeking customer personal data, to the extent the law allows. No transparency report is published. The clause covers personal data, which in an ediscovery collection runs through most of the documents a firm uploads.
Everlaw's data processing addendum says that if it must respond to a request for customer data, it will promptly notify the customer and provide a copy of the request unless legally prohibited. Its government request transparency report adds that Everlaw reviews each request, may seek to narrow or object to it, and emails the customer before disclosing. Notice is withheld only where the law prohibits it, after an account takeover or in an emergency, and is given once a prohibition lifts. United States authorities must use a subpoena, court order, warrant or national security request.
Primary Law Corpus Provenance
Where does the law in this product come from, and does the vendor have the right to use it?
DISCO holds no primary law collection. Cecilia answers only from the customer's own evidence, and Auto Review classifies against tag descriptions written for each matter, so the source material is the documents collected in discovery. No vendor supplied legal content, license basis or update cadence applies.
No primary law corpus sits behind Everlaw. Retrieval runs against the customer's own case documents, uploaded into the platform and indexed as embeddings in a vector database, so the source material is the evidence in the matter. No vendor supplied legal collection, license basis or update cadence applies.
Good Law Verification
Does the product tell you when the authority it just cited has been overruled?
DISCO works on collected evidence rather than published case law, so its output cites documents, not authority. It describes no treatment signal and no check of later history.
Everlaw's corpus is the evidence in a matter rather than published case law, so its output cites documents, not authority. Writing Assistant drafts arguments from the evidence, and any legal authority in that writing comes from elsewhere. No treatment signal or check of subsequent history is described.
Refusal and Uncertainty Behavior
What does the product do when the answer is not in the corpus?
Nothing describes what Cecilia Q&A or Auto Review does when the evidence does not support an answer or a tag, or how uncertainty is shown to the reviewer. Cecilia answers only from the customer's evidence, with citations, and Auto Review explains each tagging decision.
Everlaw does not describe what the product does when the documents do not support an answer, and shows no confidence signal for an answer. It says grounding curtails hallucinations, and answers cite source documents so a user can check them. Deep Dive answers questions across matters of more than 20 million documents, and its behavior when the evidence is absent is not described.
Fabricated Citation Record
Does a public court record exist addressing fabricated or hallucinated legal citations in output from this product?
Damien Charlotin's AI Hallucination Cases database, which tracks court decisions worldwide on hallucinated AI content, has no entry naming DISCO or Cecilia. DISCO answers and classifies from a matter's collected evidence rather than from case law.
Damien Charlotin's AI Hallucination Cases database, which tracks court decisions worldwide on hallucinated AI content, has no entry naming Everlaw. Everlaw generates answers and drafts from a matter's own evidence rather than from case law.
Bar Guidance Alignment
Has the vendor engaged in public with the ethics opinions its buyers are bound by?
DISCO names no ethics opinion or bar guidance, including ABA Formal Opinion 512. Its Auto Review material speaks of defensibility, sampling and validation, but connects them to no rule of civil procedure or case law on technology assisted review.
Everlaw names no ethics opinion or bar guidance, including ABA Formal Opinion 512. Its AI governance framework and generative AI principles govern its own conduct rather than the professional duties of the lawyers who use it. It also does not connect its AI features to the Federal Rules of Civil Procedure or to case law on technology assisted review.
Billing and Fee Posture
Does the vendor address what happens to the bill when the work takes an hour instead of six?
DISCO claims its savings in review hours, saying Auto Review completes a first pass review in hours with precision and recall above human reviewers, at up to 32,000 documents an hour. It bills on processed data per GB with unlimited Cecilia AI included, so the AI cost a firm passes on follows data volume rather than review hours. Nothing published addresses how AI assisted review is recorded or billed per matter, or whether clients are told.
Everlaw's savings claims are about throughput: Coding Suggestions automates first pass review with results it says can exceed human review, and Deep Dive answers questions across very large matters in seconds. Everlaw sells to law firms, where that review time is billed to clients. Nothing published addresses how AI assisted review is recorded or billed, or whether clients are told.
Outside Counsel Guideline Readiness
Can a firm get this vendor through a client’s AI clause without a bespoke negotiation?
A published subprocessor list names each provider with function and country, including Writer and OpenAI as language model providers, Pinecone as the vector database and AWS, Microsoft Azure and Google for hosting. DISCO's terms and conditions, AI supplementary terms and data processing addendum sit beside it. Certifications are stated with scope, SOC 2 Type 2 across security, availability and privacy and ISO 27001 for service delivery, and a trust center sits at trust.csdisco.com.
A subprocessor list, updated 2 October 2026, names AWS, Google, OpenAI, Pinecone, Zendesk and Datadog with function and country. Everlaw's customer terms, data processing addendum and government request transparency report sit beside it. The trust portal carries SOC 2 and SOC 3 reports, a penetration test summary, a CAIQ and a SIG Lite behind an access request. Security, endpoint detection and response tools are left off the public list and are available by email.
Court Disclosure Support
If a judge’s standing order requires an AI disclosure, can the product produce one?
Each Auto Review tagging decision carries an explanation on the document it concerns, and recall and precision are tracked in the product. The basis for a first pass review can therefore be reconstructed document by document. Cecilia answers carry citations to the evidence. No single export combines the model used, the sources retrieved and the human check, and no model is named for any feature.
Each Everlaw AI result cites the source documents behind it, and an ediscovery platform keeps review and production histories at document level, so what an answer relied on can be traced. No export per document records the model used, the sources retrieved and the human check together, and no model is named for any feature. Courts have long engaged with technology assisted review in ediscovery, and Everlaw does not connect its AI disclosure material to that case law.
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 DISCO if
- You want customer figures from a regulated filing. CS Disco is listed on Nasdaq and reported 1,549 customers, including 330 large customers, and 98 percent dollar based net retention in its annual filing.
- You want AI priced into the data volume. DISCO bills per GB of processed data with unlimited Cecilia AI included, where Everlaw charges credits for Coding Suggestions, Deep Dive, translation and custom extractions.
- You want to control how much the AI does per task. Agentic Cecilia offers Quick thinking and Advanced research modes and tells the user to choose by need, so autonomy is a per task decision rather than a fixed product setting.
Choose Everlaw if
- Your team wants to reach the evidence from other AI tools. Everlaw connects to Claude, ChatGPT Enterprise, Gemini, Copilot, Harvey and Legora, and offers API and MCP connectors for custom builds.
- You are selling into or working with government. Everlaw holds FedRAMP Moderate and GovRAMP authorizations and serves federal, state and local agencies, with use cases for litigation, investigations and Freedom of Information Act requests.
- You want the generative feature grounded in your own documents with the architecture stated. Deep Dive is powered by a vector database of embeddings of the customer's own documents, with answers synthesized from extracted facts and citations back to source.
In summary
DISCO
DISCO is an ediscovery and litigation platform in the cloud, with hold, review, timelines, deposition management and case building, sold to law firms, corporate legal teams and government. Cecilia AI answers questions from the evidence with citations, and Auto Review classifies documents for first pass review with an explanation for each tag. DISCO bills per GB of processed data with unlimited Cecilia AI included, and publishes its terms, AI supplementary terms, data processing addendum and subprocessor list. CS Disco reported 1,549 customers, including 330 large customers, at 31 December 2025.
Everlaw
Everlaw is a cloud platform for ediscovery, investigations and litigation, sold to law firms, corporate legal departments and government agencies. Its Everlaw AI suite covers Coding Suggestions for first pass review, Deep Dive question answering across matters of more than 20 million documents, and Review and Writing Assistants. Everlaw publishes its customer terms, data processing addendum, subprocessor list and a government request transparency report, and runs in five AWS regions. It connects to the main AI assistants through API and MCP connectors.
Questions buyers ask
DISCO vs Everlaw: which ediscovery platform is better?
Both publish their contracts and subprocessor lists, so the choice turns on how each is bought and connected. DISCO bills per GB with unlimited AI included and reports its customer base in a regulated filing. Everlaw publishes a dated customer study, keeps data and AI processing in the customer's chosen region, and connects to outside AI assistants through API and MCP connectors. From the AI Legal Index, based on each vendor's own published materials as of August 30, 2026. No vendor pays for placement.
Which one is safer for privileged material?
Everlaw, on published evidence. An outside authority has tested its generative features specifically. Its confidentiality terms can be read in advance, including that data submitted and responses received are not used to train models across customers and are not shared between customers. DISCO's position is substantive too, stating that the models used in Cecilia AI are not permitted to train on customer data. From the AI Legal Index, based on each vendor's own published materials as of August 30, 2026. No vendor pays for placement.
How many customers does DISCO have?
1,549 customers including 330 large customers at 31 December 2025, with a 98 percent dollar based net retention rate, according to CS Disco's annual filing. Everlaw publishes a customer study instead: Dinsmore & Shohl reached 98.2% recall reviewing 26,000 contracts with Coding Suggestions and Predictive Coding. From the AI Legal Index, based on each vendor's own published materials as of August 30, 2026. No vendor pays for placement.
Do DISCO or Everlaw integrate with Microsoft 365?
Everlaw's cloud connectors collect from Microsoft 365, Zoom and Google Workspace. DISCO Hold connects to a customer's systems, DISCO publishes guidance on collecting Google Workspace data through Google Vault, and its high speed uploader moves enterprise data into the platform. Both offer an API. From the AI Legal Index, based on each vendor's own published materials as of August 30, 2026. No vendor pays for placement.
What do DISCO and Everlaw both leave unpublished?
Neither stands behind the accuracy of AI output when a review or privilege call is wrong. DISCO's terms warrant that the software conforms to its documentation and set a separate data protection cap, while Everlaw's warrant only legal compliance and exclude AI output from its infringement indemnity. Neither publishes a rate, and neither publishes a professional responsibility position, though the advice line arises less for review platforms than for research tools. From the AI Legal Index, based on each vendor's own published materials as of August 30, 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.
DISCO's customer and retention figures come from its operator's annual filing, and Everlaw's recall figure comes from one customer's study published on Everlaw's blog, so the two are different kinds of evidence. Neither vendor reviewed this page.