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Discernis
Discernis is an AI document review platform from Discernis, Inc. of New York, sold to law firms, corporate legal departments and legal service providers for litigation discovery and internal investigations. Its Discovery product takes the guiding questions from a review protocol, pasted in plain English, and runs the company's own models over every document in a collection to assess responsiveness, prioritise what it finds and flag likely privileged communications, attaching an explanation to each call.
Reviewers validate or override the tags in a side pane with a built-in quality-control workflow, every action is recorded in an audit log, and results export as load files or as a CSV of responsive documents with their scores and explanations. A second product, Discernis Investigations, reads a collection against chosen topics to reconstruct timelines, map parties and entities and flag anomalies, with example uses ranging from case strategy to HR complaints and reviews of staff activity.
The company builds and hosts its own models and states that no third-party AI service is called; the platform is offered as Discernis Cloud (US-based Azure by default), Discernis Private Cloud, or on the customer's own premises, including air-gapped environments. Discovery is priced per document, on matter or annual terms.
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 models are the product. Discernis Discovery takes the questions from a review protocol and has the company's own models read every document in the collection, assess its responsiveness, prioritise it, flag likely privileged communications and explain each call; Discernis Investigations reads the same kind of collection against chosen topics to build timelines and map parties. The FAQ states that the company builds and hosts its own models rather than calling an outside AI service, and the pricing, deployment options and review workflow are all organised around that model output. Take the models away and there is nothing left to buy. Verified 22 September 2026.
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.
Each tag is tied to the document it describes and carries the model's explanation, which reviewers check in a side pane, and results export with a score and an explanation for every responsive document. The product reads every document in the collection rather than retrieving a sample. Accuracy figures are published but cannot be tested from outside: the FAQ states about 99 per cent accuracy with recall above 95 per cent and precision above 90 per cent, and says inter-annotator agreement is indistinguishable from top reviewers, while the Discovery product overview gives about 95 per cent or more on relevance classification, about 90 per cent precision and about 95 per cent recall.
Neither set of figures describes a test set, matter type, sample size or date, and no failure modes are named. Verified 22 September 2026.
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.
The models run first-pass review across the whole collection on their own, and the review surfaces around them are real. The FAQ states that reviewers can override or correct any AI suggestion, that a built-in quality-control workflow validates the AI tags, that a side pane shows each AI answer for human validation, and that an audit log records extraction, editing and validation by date, time and person and cannot be edited by users.
The home page presents the explanations as the basis for a second-pass human review. What is not published is the rest of the control structure: no threshold at which a document is routed to a person rather than tagged, no statement of what the output may not be relied on for without review, and nothing on what happens when a tag proves wrong. Verified 22 September 2026.
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.
Results are quoted with no basis stated. The home page claims 90 per cent faster review, 70 per cent lower cost and about 99 per cent accuracy; the law firm page claims a 40 to 50 per cent cost reduction; and the funding announcement of 25 August 2026 says the product is in use at Am Law firms on active matters. No customer is named, no matter or date is attached to any figure, and no method is given for how the savings were measured.
No named case study was located on the case studies page, in the newsroom or on the blog. Checked the home page, both product pages, the law firm page, the case studies page, the Discovery product overview, the newsroom and the blog on 22 September 2026. Verified 22 September 2026.
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 confidentiality commitments sit in the FAQ and on the home page, not in the published terms. The FAQ says no data is retained for training and that data is removed from all systems within 30 days of a user deleting it; the home page says the company builds and hosts its own models so documents are never sent to outside AI companies; and the platform can run on the customer's own premises or air-gapped, which keeps documents inside the customer's control.
The published Terms of Use, last updated 1 May 2024, carry none of this: they contain no confidentiality or training term, state that the company may access, store, process and use any information and personal data a user provides, and say the Services are not tailored to comply with HIPAA, while the FAQ states HIPAA compliance. Nothing is published on privilege or work product handling, or on separation between matters or users. Verified 22 September 2026.
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.
Checked the home page, both product pages, the FAQ, the Terms of Use, the Privacy Notice, the Discovery product overview and the blog on 22 September 2026. Nothing states what the product is and is not, that its tags and explanations are not legal advice or a substitute for a lawyer's judgement, or who may use it, and the Terms of Use carry no advice disclaimer. The blog advises in-house counsel to keep meaningful, documented oversight of AI findings and calls private deployment a professional responsibility requirement, which is guidance to buyers rather than a statement about this product.
The Investigations product is marketed for HR complaints and staff-activity reviews as well as legal matters, and no audience limit is stated for it. Verified 22 September 2026.
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.
The product explains every tag it makes, and the FAQ says the company measures its models against human reviewers, including inter-annotator agreement. That is the extent of it: no governance framework, accountable owner, pre-release testing regime or bias finding is published. The gap matters most on the Investigations product, which is marketed for HR discrimination complaints and for reviewing staff activity for signs of burnout and disengagement after layoffs, uses where uneven output across groups of people is the central risk, and no statement addresses it.
Checked the home page, both product pages, the FAQ, the Terms of Use, the Privacy Notice and the blog on 22 September 2026. Verified 22 September 2026.
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.
The FAQ answers the questions that matter for documents under review: nothing is retained for training, data is removed from all systems within 30 days of a user deleting it, hosting defaults to a US-based Azure cloud with other regions or clouds available on request, the platform can run on premises or air-gapped so documents never leave the customer, and an audit log records every extraction, edit and validation by person and time and cannot be altered by users.
What is missing is a named subprocessor list and any stated incident or breach notification practice. The Privacy Notice, last updated 1 May 2024, covers website visitors and account details and does not address documents loaded for review. A trust centre is linked from the FAQ; it could not be opened on 22 September 2026, so its contents are not reflected here. Verified 22 September 2026.
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.
Liability is handled only by the standard clauses of the Terms of Use, last updated 1 May 2024. The Services are provided as is, with all warranties disclaimed, including any warranty on the accuracy or completeness of content; the company's liability is capped at the amount paid in the six months before the claim arose; and the user indemnifies the company, with no indemnity running the other way. The Discovery product overview carries the heading Remarkable Accuracy, Guaranteed, but no guarantee terms are published anywhere, and the terms themselves disclaim accuracy. Verified 22 September 2026.
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.
No integration with a review platform, document management system or matter system is named. Data moves in and out by file: the FAQ says the platform accepts load files, .dat files and native files, and exports load files, .dat files and a CSV listing responsive documents with their scores and explanations, with no ingestion or export fees. That keeps it compatible with the load file exchange review platforms already use, but a firm moving work between Discernis and its existing systems does so by export and import rather than through a connection.
Checked the home page, both product pages, the FAQ and the Discovery product overview on 22 September 2026. Verified 22 September 2026.
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.
Deployment and residency are published in detail. The product overview names three options, Discernis Cloud, Discernis Private Cloud and on-premises, and the FAQ adds air-gapped installation, deployment on any Kubernetes cluster and any major cloud provider, naming Google Cloud, AWS and Azure; the home page states full functionality in each. Hosted data sits in a US-based Azure cloud by default, and the company will deploy to any region or other cloud on request.
Processing is stated separately from storage: the funding announcement says all inference runs inside the environment the customer chooses, with no third-party or commercial AI service called, and the FAQ publishes the compute a local deployment needs, about 32 CPUs, 128 GB of RAM, 2 TB of storage and eight H100 GPUs for a terabyte of data a month. Cloud offerings scale with demand, while local throughput depends on the compute allocated. Verified 22 September 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.
No independent security attestation is held. The FAQ states compliance with HIPAA, which has no certification scheme of its own, and says the company is working to finalise ISO 27001 and SOC 2; neither is claimed as achieved, and no auditor, scope or date is given. The published Terms of Use, by contrast, say the Services are not tailored to comply with HIPAA and may not be used where HIPAA applies. A trust centre is linked from the FAQ; it could not be opened on 22 September 2026, so its contents are not reflected here. Verified 22 September 2026.
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 FAQ states that the company builds and hosts its own models, purpose-built for discovery, and the funding announcement says no third-party or commercial AI service is called at any point, with inference running inside the deployment the customer chooses and hosted by default on a US-based Azure cloud. The architecture, and any base model the proprietary models are built from, is not identified, and no commitment to notify customers when the models change is published. Verified 22 September 2026.
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.
The unit and structure are published without a figure. The Discovery page states per-document pricing with no per-gigabyte, per-user or storage charges; the FAQ adds no ingestion or export fees; the pricing page offers matter and annual pricing options, fast setup and cancellation at any time; and the Terms of Use describe a monthly subscription that renews automatically. A blog post explains that the per-document price is meant to be quoted before a matter starts.
No rate is published, and the law firm page describes the offer as having no per-document charges, which conflicts with the per-document unit stated on the Discovery page and in the pricing blog post. Verified 22 September 2026.
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.
The buyer segments are set out on separate pages for in-house legal departments, law firms and legal service providers, and the use is clear: first-pass responsiveness and privilege review in litigation discovery, and fact-finding in internal investigations, compliance reviews and case strategy. Where the product stops is not stated: no document types, matter sizes or practice areas are named as unsupported, and the Investigations examples reach beyond legal work into HR, manufacturing and operational risk. Verified 22 September 2026.
4 public documents
The public pages on file for Discernis, with the recorded signals each one supports and the date it was last read. Open any of them and check the reading against the record.
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discernis.ai/faq2 signals
Client Data in Training, Prompt and Output Retention
Read Sep 22, 2026
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Bar Guidance Alignment
Read Sep 22, 2026
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discernis.ai/privacy1 signal
Third Party Request and Subpoena Notice
Read Sep 22, 2026
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Fabricated Citation Record
Read Sep 22, 2026
No published figureUSD, as published, never converted
- Discernis charges for each document it reviews, not for gigabytes of data or for each user.
- You can pay per matter or sign up for a year, and you can cancel at any time.
- Loading documents in and exporting results out costs nothing extra.
- The price itself is not published, so you have to ask for a quote.
- One page on the site says there are no per-document charges, which does not match the rest.
Unit and structure published, figure withheld. The Discovery page states per-document pricing with no per-gigabyte, per-user or storage charges; the FAQ says there are no ingestion or export fees; the pricing page offers matter and annual pricing options, fast setup and cancellation at any time; and the Terms of Use describe a monthly subscription that renews automatically, billed in US dollars. The law firm page describes the offer as having no per-document charges, which conflicts with the per-document unit stated elsewhere. No rate, band or minimum is published, and every route to a number is a meeting request.
Implementation: None published as a separate charge. The pricing page advertises fast setup and the law firm page promises fast implementation. On-premises deployments run on hardware the customer supplies; the FAQ publishes the compute a deployment handling a terabyte of data a month needs.
Confidentiality and data terms: No Business Associate Agreement is offered or referred to on the site. The FAQ states HIPAA compliance, while the Terms of Use say the Services are not tailored to comply with HIPAA and may not be used where HIPAA applies.
Note: No figure is published at any level. The Terms of Use state that all purchases are non-refundable and that subscription fees may change with notice.
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?
A public policy or trust page states no training on customer content, with no matching term located in the published agreement.
The FAQ answers no to whether any data is retained for training, and the home page says the company builds and hosts its own models so documents are never sent to outside AI companies. The published Terms of Use, last updated 1 May 2024, carry no training term in either direction: they state that the company may access, store, process and use any information and personal data a user provides, without naming training. The commitment a buyer can read sits on a policy page, not in the agreement.
Prompt and Output Retention
How long does the product keep what a lawyer typed, and can that be set to zero?
The customer controls the retention window, by product configuration or by contractual instruction, but zero retention is not stated as available.
The customer controls how long documents stay: the FAQ says data is completely removed from all systems within 30 days of a user deleting it, and on-premises or air-gapped deployments keep the collection inside the customer's own environment. A setting that retains nothing is not described. The Privacy Notice keeps account information for as long as an account exists, and addresses website and account data rather than documents under review.
Ethical Walls and Matter Segregation
Does retrieval respect the firm’s ethical walls, or can the model read across them?
No located public material addresses walls or matter level segregation.
Checked the home page, both product pages, the FAQ, the Terms of Use, the Privacy Notice and the Discovery product overview on 22 September 2026. No material addresses ethical walls or separation between matters or users within an account. The FAQ describes an audit log of who extracted, edited and validated what, and an on-premises deployment leaves access to the system with the customer, but neither is a published permission model at the matter level.
Third Party Request and Subpoena Notice
If someone subpoenas the vendor for a firm’s data, does the firm hear about it first?
Published terms or policy address disclosure to authorities or in response to legal process, and no commitment or reservation regarding customer notice is located anywhere. The vendor has told the customer that data can leave and has said nothing about whether the customer hears of it.
The Privacy Notice, last updated 1 May 2024, says information may be processed to comply with legal obligations and respond to legal requests, and the Terms of Use reserve the right to report users to law enforcement. Neither document commits to telling the customer before its data is disclosed, or reserves a discretion to do so. The FAQ presents deployment inside the customer's own environment as keeping data out of reach of outside legal requests, which applies where the platform runs on the customer's premises.
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.
Checked the home page, both product pages, the FAQ and the Discovery product overview on 22 September 2026. The product works on the customer's own document collection, and no body of primary law behind its output is identified anywhere.
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.
Checked the home page, both product pages, the FAQ and the Discovery product overview on 22 September 2026. The product classifies and explains documents from the customer's own collection rather than citing legal authority, and nothing addresses checking authority for subsequent history.
Refusal and Uncertainty Behaviour
What does the product do when the answer is not in the corpus?
The product exposes a confidence or grounding score without an explicit abstention path.
Every document receives a responsiveness score and an explanation, which export together in a CSV, and reviewers validate the tags in a side pane with a quality-control workflow. No path is described in which the system declines to classify a document it cannot assess.
Fabricated Citation Record
Does a public court record exist addressing fabricated or hallucinated legal citations in output from this product?
No court order, opinion or disciplinary record addressing fabricated or hallucinated legal citations produced by this product has been located as of the date shown. This is a statement about the public record on that one subject, not a finding about the product, and this signal is not a litigation history.
Searched the AI Hallucination Cases database maintained by Damien Charlotin on 22 September 2026 for Discernis, and no recorded case was returned. No court order, opinion or disciplinary record naming the product was located. This is a statement about the public record rather than a finding about the product.
Bar Guidance Alignment
Has the vendor engaged in public with the ethics opinions its buyers are bound by?
Public materials refer to professional responsibility in general terms without naming guidance.
A blog post on defensible AI investigations says several bar ethics committees have weighed in on using AI tools with client confidential information, and calls private deployment for sensitive matters a professional responsibility requirement. No ethics opinion is named, and the product pages, FAQ and terms do not engage with professional guidance.
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, and the product sits inside a fee relationship between a lawyer and a client where those savings would change the bill.
The home page claims review that is 90 per cent faster and 70 per cent cheaper than traditional review, and the law firm page claims a 40 to 50 per cent cost reduction. A blog post on per-document pricing discusses predictable budgets and easier cost conversations with clients, but nothing addresses how AI-assisted review is billed or disclosed to the client when the hours fall.
Outside Counsel Guideline Readiness
Can a firm get this vendor through a client’s AI clause without a bespoke negotiation?
No located public material supports a client side disclosure obligation.
No subprocessor or model provider list was located on the home page, product pages, FAQ, Terms of Use or Privacy Notice on 22 September 2026, and the trust centre linked from the FAQ could not be opened that day. What a firm can forward to a client is the vendor's stated position: its own models, no third-party AI service called, no data retained for training, and a US-based Azure cloud by default. For an on-premises deployment, a firm can answer its client's AI clause from its own configuration.
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.
Exports include a CSV listing responsive documents with their scores and the model's explanations, and the audit log records extraction, editing and validation by date, time and person. No per-document record combining the model used, the basis for the call and the human verification is described as an export, and no disclosure guidance or template is published.