DecoverAI
DecoverAI is an eDiscovery platform that takes a document set from upload to court-ready production. It classifies documents for responsiveness, privilege and confidentiality, detects and burns redactions without altering originals, generates a privilege log as a spreadsheet, applies Bates numbering to a firm's conventions, and produces an audit trail intended to stand up in a regulatory production. Alongside review it offers a chronology viewer that reconstructs timelines across custodians and evidence analysis for surfacing key facts, with material aimed at early case assessment, internal investigations and trial preparation as well as discovery proper. Every classification is reviewable and every redaction overridable, and the agreement requires a licensed attorney to verify AI output before professional reliance. Pricing is published openly at 60 US dollars per gigabyte per month with no seat fees and no minimum commitment, and there is a cost estimator on the site. The platform runs on AWS in the United States with single-tenant and private VPC options including deployment inside the customer's own VPC, and DecoverAI publishes a dated subprocessor list naming every AI provider it uses and what each one does. Its terms prohibit training on customer data without prior written consent, state that nothing in the agreement waives privilege, and name the ABA Model Rules on competence and confidentiality. DecoverHQ, Inc. is based in San Mateo, California.
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 machine learning is the mechanism the buyer pays for. Classification for responsiveness, privilege and confidentiality is what compresses the review, and every downstream artifact depends on it: the privilege log is generated from the AI's privilege calls, redaction is driven by automated detection with attorney override, and the headline claim is 80 per cent less review because the model triages every document. The company markets a webinar on how it post-trains an LLM to run document review at under five cents a document, which is a statement that the model is the product rather than a feature on one. Remove the models and what remains is document storage with Bates numbering. Checked 4 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.
Grounding is real and documented at the supplier level, which is unusual and more useful than most architecture claims. The published subprocessor list names Parallel Web Systems for web search grounding of AI-generated answers and citations, Cohere for search result re-ranking to improve relevance, and UniCourt for legal case-law and litigation research data feeding AI-assisted legal research features. A reader can therefore see what the answers are grounded in and which supplier provides each part. Failure modes are named in the agreement rather than avoided: clause 13.5 refers expressly to incorrect, inaccurate or hallucinated information generated by the AI features, including case citations and regulatory references. What is missing is measurement. No accuracy figure is published anywhere, no test set is described, and no evaluation is linked. A white paper on AI review defensibility is published but was not opened in this pass and is not credited for its contents.
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.
A written commitment that the models work alongside a supervising lawyer, with real review surfaces. The product position is that every classification is reviewable and every redaction overridable, with redactions burned without touching originals so the underlying document survives an override. The commitment is contractual as well as marketing: clause 2.5 makes the customer solely responsible for supervising and verifying all AI-generated outputs before professional reliance, and clause 13.5 requires independent review and verification by a licensed attorney. Clause 4.3 keeps access and activity logs for at least twelve months and makes them available on written request, which is a real oversight surface rather than a claim. What is absent is the rest of the control structure: no threshold is published at which the system stops or escalates, no confidence signal is described as visible to the reviewer, and nothing states what happens after a misclassification is found beyond the customer's own override.
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.
Deployment evidence with unusually tight attribution, held below the top band by the absence of dates. Six case studies are published and three name the firm: Clayton Trial Lawyers, with a 15.4 million dollar jury verdict after a five-week trial; Schaff Law Group, with more than 100 hours saved on case preparation; and Gregor Wynne Arney PLLC, processing more than a million documents in a 35 million dollar healthcare fraud and anti-kickback investigation. Three further studies carry figures without the firm named, including 30,000 documents produced in three days saving 147,000 dollars and 25 days, a production remediation of more than 360,000 documents resolving six of six defects under federal scrutiny, and a construction defect matter covering a terabyte across three buildings. Unlike most records on this axis the figures are attached to the named firms rather than floating free, which is the limb this band usually fails. No case study carries a date, no methodology is stated on the summary cards, and the case study pages were not opened in this pass, so they are credited for existing and for the attribution visible on the index rather than for their contents.
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.
Every limb is met and the privilege limb, which is the one this axis exists for, is met directly rather than by implication. Clause 7.3 states that DecoverAI treats all Customer Data as Confidential Information and that nothing in the agreement constitutes a waiver of any legal privilege or protection applicable to it. Clause 10.4 goes further: Customer Data constituting Privileged Information is afforded the highest level of protection, DecoverAI personnel are instructed not to review Customer Data except as strictly necessary for technical support and only with the customer's prior authorisation, and all personnel with potential access are bound by written confidentiality obligations. Training is prohibited by clause 7.9 absent prior written consent. Segregation is documented at the level a firm needs, with single-tenant deployment, private VPC, an option to deploy inside the customer's own VPC, role-based access control on least privilege, and processing described as taking place in isolated environments destroyed once the job completes. Retention and deletion are specific under clause 7.10, with irreversible deletion and written certification available. The position on model providers is the most complete in this pull: nine AI and machine learning subprocessors are named individually with the function each performs and the country it operates in.
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.
The professional responsibility treatment is the most specific located in this pull and it sits in the agreement rather than in a footer. Clause 2.5 states plainly that the Services are productivity tools and do not constitute legal advice, and names the duties engaged: competence under ABA Model Rule 1.1, confidentiality under ABA Model Rule 1.6, and supervision. Clause 11.4 states in terms that the Services are not a substitute for professional legal judgment and that no content generated constitutes legal advice. Clause 13.5 requires independent review and verification by a licensed attorney before professional reliance and puts the risk of unverified reliance on the customer. Clause 4.4 goes to a question most vendors ignore, requiring the customer to warrant it has obtained any client consent required under bar rules or ethics opinions governing cloud-based legal technology. Clause 2.3 prohibits use in violation of applicable professional responsibility rules and bar regulations. The one limb not squarely met is a stated jurisdiction limit for substantive coverage, which bites weakly on a product that operates on the customer's own document set rather than on jurisdiction-specific law; the named authorities are American and the agreement is governed by California law.
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.
This is the weakest disclosure on an otherwise strong record. No AI governance framework is published: nobody inside DecoverHQ is named as accountable for model behaviour, no pre-release evaluation regime is described, no testing results are published, and there is nothing at all on bias or uneven output, which matters more than usual on a product whose core function is classifying documents as responsive or privileged. What exists is adjacent rather than on point: SOC 2 Type II covers security, availability and confidentiality; controls are said to be monitored continuously and published live in a trust centre; and a white paper on what courts expect from AI-assisted document review is published but was not opened. Security certification is a different subject from AI governance and is credited on its own axis rather than here.
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 full set is published, current and specific enough to hold the vendor to. Retention is a stated period rather than a gesture: clause 7.10 retains Customer Data for the subscription term plus thirty days for export, then irreversibly deletes or destroys it, with written certification of deletion on request, and clause 7.7 lets the customer request removal at any time with a thirty-day processing commitment. Access control under clause 7.4 covers TLS 1.2 or higher in transit, AES-256 or equivalent at rest, role-based access and least privilege for DecoverAI personnel, and regular penetration testing and vulnerability scanning by independent third parties. Incident practice is contractual: clause 3.5 commits to notifying the customer of any confirmed security breach affecting Customer Data within seventy-two hours of confirmation. The subprocessor position is exceptional, with a dated and versioned public list naming every provider, its function and its country, thirty days' prior written notice before any addition, and a subscribable change-notification list. Clause 4.3 retains access logs for at least twelve months and releases them on request, and clause 7.11 gives an annual customer audit right.
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.
A real published position on liability, and the shortfall is the subject rather than the structure. What is present is substantial: clause 12.1 gives an intellectual property indemnity covering patent, copyright, trademark and trade secret claims with five named exclusions at 12.3 and remedies at 12.2; clause 13.2 caps each party at twelve months of fees; clause 13.3 lifts that cap entirely for breach of confidentiality, gross negligence or wilful misconduct, and for DecoverAI's own indemnity obligations, which leaves confidentiality breach uncapped; clause 11.2 gives six warranties including that the Services will perform materially in accordance with the Documentation and, unusually, that DecoverAI maintains SOC 2 Type II certification, which converts a marketing claim into a contractual promise; and clause 14.1 commits to 99.9 per cent uptime with service credits. No insurance position was located. What holds this off the top band is clause 13.5, which disclaims liability under any theory for incorrect, inaccurate or hallucinated information generated by the AI features, naming case citations and regulatory references among them, and places all risk of reliance on the customer. The allocation is published and unusually clear, and on the question this axis asks the published answer is that the vendor stands behind nothing when the output is wrong.
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.
Documented integrations into the systems litigation work actually lives in, with direction and configuration described. The subprocessor page lists customer-authorised connections individually: Microsoft SharePoint, OneDrive and Microsoft 365 through the Graph API, Google Drive and Gmail, Dropbox, Box, customer-owned Amazon S3 buckets, and two legal-specific systems that matter here, iManage and Clio. The same page describes the direction of travel and who controls it, stating that DecoverAI accesses those sources solely at the customer's direction and under the customer's own agreement with that provider, and that the connections are configured by the customer rather than engaged by DecoverAI on its own behalf. Appendix A of the agreement records connectors to document management systems, email platforms and cloud storage providers as a contracted service element specified in the Order Form. Naming iManage and Clio is what distinguishes this from generic cloud connectivity: those are the systems a firm's matter files already sit in.
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.
The deployment model is stated clearly and offered in tiers, with residency detail that is partial rather than absent. Three postures are published: default multi-tenant, single-tenant with dedicated infrastructure, and deployment inside the customer's own VPC for organisations requiring full isolation, with AES-256 at rest and TLS 1.2 or higher in transit across all of them and processing described as taking place in isolated environments destroyed when the job completes. That publishes both the tenancy limb and what changes between tiers. Residency is answerable only indirectly: the subprocessor list gives a country for every provider, placing AWS hosting and storage in the United States, optical character recognition with Mistral AI in France, and search re-ranking with Cohere in Canada, which distinguishes where processing happens from where data is stored. What is not published is a residency offering: no region is named as available or selectable, and nothing states that a customer can require its data to remain in a given jurisdiction.
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.
Certification is real, stated, and scoped, which is more than most records in this band carry. SOC 2 Type II is claimed with the trust services criteria named as security, availability and confidentiality, and with the distinction drawn that it is verified over an observation period rather than at a single point in time. HIPAA compliance is claimed with a Business Associate Agreement available on request. Clause 11.2(f) warrants on an ongoing basis that DecoverAI maintains SOC 2 Type II certification, and clause 7.11 gives an annual audit right that DecoverAI may satisfy by producing its most recent SOC 2 Type II report and third-party penetration testing summary. A trust centre operates at trust.decover.ai and is said to publish control monitoring live; it was not opened in this pass and is credited for existing rather than for its contents. What is missing is the accessible evidence: no auditor or certification body is named, no report date or observation period is published, and the full report is available only under NDA. Applying the third-party verifiability test, a buyer cannot check the claim against the auditor without contacting DecoverAI. GDPR is described as certification in progress, which is intent and is credited to nothing.
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 most granular supply chain disclosure located in this pull. The published subprocessor list, dated August 2026 and version-numbered, names nine artificial intelligence and machine learning providers individually, states what each one does, and gives the country it operates in: OpenAI, Anthropic and Google under its Gemini API for model inference across document analysis, classification and question answering, all in the United States; OpenRouter as a routing layer used to reach additional supported models; Mistral AI in France for optical character recognition of scanned documents; Cohere in Canada for search result re-ranking; E2B for sandboxed isolated code execution supporting agent workflows; Parallel Web Systems for web search grounding of answers and citations; and UniCourt for case-law and litigation research data. AWS is separately identified for hosting, storage, database, managed search, authentication and content delivery. Change notification is committed at thirty days' prior written notice before any addition, with a subscribable notification list, and clause 7.5 of the agreement repeats it. The one limb only partly satisfied is model naming: Gemini is identified as a model family and the rest are named at provider level, and the OpenRouter routing layer means the specific model behind a given request is not disclosed.
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.
A buyer can learn what this costs without speaking to anyone. The rate is published as 60 US dollars per gigabyte per month, described as all-in with no seat fees, no enterprise tier and no contract required, and the page states what that covers: AI classification for responsiveness, privilege and confidentiality, automated redaction detection with attorney override, an auto-generated privilege log as a spreadsheet, Bates numbering to firm conventions, a full audit trail for regulatory productions, and SOC 2 compliant encrypted hosting. A second unit is published alongside it at two cents per document, with a worked illustration that most teams process five to twenty gigabytes per matter for a 300 to 1,200 dollar all-in cost, and a cost estimator tool is offered on the site. Self-serve signup is available. One tension is recorded rather than smoothed over: the marketing says no contracts and no seat fees, while the agreement contemplates Order Forms with authorised user seats, service capacity limits, overage rates, annual invoicing in advance, sixty-day non-renewal notice and fees due through the end of the subscription term. Both are published and a buyer should read both.
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.
Coverage is described with substance across two dimensions and its outer edge is left open. Four stages of the litigation lifecycle each carry their own page: eDiscovery, early case assessment, internal investigations covering cyber breaches, whistleblower actions, subpoenas and regulatory inquiries, and trial preparation. Matter types are evidenced rather than claimed, through case studies spanning commercial litigation, personal injury, white collar crime, a tax credit investigation, a federal production remediation and a multi-party construction defect matter. The buyer is stated as law firms and in-house legal teams, and HIPAA is positioned specifically as the baseline for personal injury, medical malpractice and healthcare investigation matters. Clause 2.4 addresses United States Government use rights under FAR and DFAR, which is a segment most vendors leave silent. What is missing is the boundary: no firm size is stated, no matter volume floor or ceiling is given, and nothing says which matter types or data types the product does not handle.
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?
Training occurs only where the customer has affirmatively enabled it.
Clause 7.9 of the published agreement, headed No Training on Customer Data, provides that DecoverAI will not use Customer Data or Inputs to train, fine-tune or improve any artificial intelligence or machine learning model without the customer's prior written consent, and adds that its models are trained exclusively on licensed datasets and public information sources. The consent gate is what decides the value: training is prohibited by default and can occur only where the customer affirmatively agrees in writing, which is opt-in rather than an unqualified prohibition. Clause 7.6 is consistent, licensing Inputs and Customer Data solely to operate the Services, address technical problems and meet legal obligations, and expressly withholding any commercial purpose unrelated to the Services absent prior written consent. Recorded for completeness: the home page states the position more absolutely than the agreement does, as your data never trains our models, without the consent carve-out.
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.
Clause 7.10 publishes a specific window: Customer Data is retained for the subscription term plus thirty days, during which the customer may export, after which DecoverAI irreversibly deletes or destroys it except where law requires retention, with written certification of deletion available on request. The customer controls that window by contractual instruction rather than being fixed to it, because clause 7.7 allows removal of Customer Data from the Services to be requested in writing at any time, with DecoverAI committing to process such requests within thirty days. Zero retention is not stated as an available setting, although the security material describes documents being processed in isolated environments that are destroyed once the job completes.
Ethical Walls and Matter Segregation
Does retrieval respect the firm’s ethical walls, or can the model read across them?
The product maintains its own permission model, documented, requiring the firm to keep it aligned.
DecoverAI maintains its own documented permission and isolation model rather than inheriting a source system's access control at query time. Published detail covers single-tenant deployment and private VPC, with dedicated infrastructure or deployment inside the customer's own VPC for organisations requiring full isolation, AES-256 at rest and TLS 1.2 or higher in transit by default, enterprise single sign-on through the customer's own identity provider, role-based access controls and enforced multi-factor authentication. Clause 7.4 adds role-based access and least privilege for DecoverAI personnel, and clause 10.4 restricts personnel from reviewing Customer Data except as strictly necessary for technical support and only with the customer's prior authorisation. The customer administers its own Authorized User access under clauses 2.2 and 3.2, which is the alignment burden this value describes. No material addresses segregation between separate matters inside one customer account.
Third Party Request and Subpoena Notice
If someone subpoenas the vendor for a firm’s data, does the firm hear about it first?
Terms commit to notice where lawfully permitted. No transparency report located.
Clause 10.3, headed Compelled Disclosure, commits that a party required by law or court order to disclose the other's Confidential Information will provide prompt written notice where legally permissible, cooperate in seeking a protective order, and disclose only what is legally required. Customer Data is Confidential Information under the agreement and clause 10.4 gives Privileged Information the highest level of protection, so the notice commitment reaches the material a firm most cares about. No transparency report of government or third-party requests was located on any surface, which is what separates this from the top value. Searched the terms of service, the subprocessor list, the security material and the home page on 4 September 2026.
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.
Clause 7.9 states that DecoverAI's AI models are trained exclusively on licensed datasets and public information sources, which characterises the rights basis without identifying any dataset, publisher or collection. The published subprocessor list separately names UniCourt as the supplier of legal case-law and litigation research data feeding AI-assisted legal research features, which identifies a supplier rather than a corpus. No primary law source, jurisdictional coverage or update cadence is published, and the product's principal corpus is the customer's own document set rather than an external legal collection. Searched the home page, the terms of service, the subprocessor list and the security material on 4 September 2026.
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.
Nothing on any located surface addresses whether authority is checked for subsequent history. The product is an eDiscovery platform whose core work is classification, redaction and production over the customer's own documents, so the question bites only through the AI-assisted legal research features referred to in clause 13.5 and supplied with data by UniCourt. No treatment signal, currency check or citator relationship is described anywhere. Searched the home page, the product pages listed in the navigation, the terms of service and the subprocessor list on 4 September 2026.
Refusal and Uncertainty Behaviour
What does the product do when the answer is not in the corpus?
No located public material addresses what the product does when it cannot ground an answer.
No located public material describes what the product does when it cannot ground an answer. Hallucination is acknowledged squarely in clause 13.5, which disclaims liability for incorrect, inaccurate or hallucinated information generated by the AI features, but acknowledgement of the risk is not a description of behaviour. No abstention path, no no-answer state and no confidence or grounding score visible to the reviewer is described, and the published control is human override of every classification rather than anything the system does itself. Searched the home page, the product pages, the terms of service and the subprocessor list on 4 September 2026.
Fabricated Citation Record
Does a public court record exist involving output from this product?
No court order, opinion or disciplinary record naming this product has been located as of the date shown. This is a statement about the public record, not a finding about the product.
The AI Hallucination Cases database maintained by Damien Charlotin was searched on 4 September 2026 on both the product name DecoverAI and the corporate name DecoverHQ. No court order, opinion or disciplinary record naming the product was located. This records the state of the public record on that date and is not 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 engage with at least one named ethics opinion.
The agreement engages named professional conduct rules directly. Clause 2.5 makes the customer responsible for compliance with applicable Rules of Professional Conduct and names three duties with their sources: competence under ABA Model Rule 1.1, confidentiality under ABA Model Rule 1.6, and supervision. Clause 4.4 requires the customer to warrant that uploading client materials does not violate any court order or rule of professional conduct and that it has obtained any client consent required under applicable bar rules or ethics opinions governing cloud-based legal technology, which engages the specific ethics question this product raises. Clause 2.3 prohibits use of the Services in violation of applicable professional responsibility rules and bar regulations. The guidance engaged is that of one jurisdiction and no clause-by-clause mapping of product behaviour to named opinions is published.
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.
Public materials claim cost savings without addressing what happens to a client bill. Published claims include 98 per cent cost reduction against traditional attorney review, 147,000 dollars and 25 days saved on a 30,000-document production, 20,000 dollars saved on a subpoena response, and pricing framed as 300 to 1,200 dollars per matter against weeks of associate hours. A cost estimator tool estimates the customer's own review spend rather than anything disclosable to a client. Nothing addresses how AI-assisted work should be billed or disclosed, and although the product generates detailed per-matter artifacts including privilege logs and audit trails, none is described as a record of AI-assisted work for fee purposes.
Outside Counsel Guideline Readiness
Can a firm get this vendor through a client’s AI clause without a bespoke negotiation?
A subprocessor and model provider list plus client facing disclosure material is published or available without an agreement in place.
All three limbs are evidenced separately. Subprocessor list: a dated and version-numbered page published at decover.ai/legal/subprocessors, last updated August 2026, naming every subprocessor engaged for Customer Data with its function and country. Model provider statement: the same page names nine artificial intelligence and machine learning providers individually and states what each does, covering inference by OpenAI, Anthropic and Google Gemini, routing by OpenRouter, optical character recognition by Mistral AI, re-ranking by Cohere, sandboxed execution by E2B, web search grounding by Parallel Web Systems and case-law data by UniCourt, so a firm can tell its client precisely who touches its content and for what. Forwardable client-facing material: the page states it is the subprocessor list referenced in the Data Processing Agreement, records that each subprocessor is bound by obligations no less protective including Standard Contractual Clauses where applicable, commits to thirty days' prior written notice before any addition, and offers a countersigned DPA and SCCs on request. The DPA is published at decover.ai/legal/dpa and incorporated into the terms by clause 7.2; that page was not opened in this pass, and the third limb is credited on the published subprocessor annex itself rather than on the DPA's contents.
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.
Substantial elements of a record are produced by the product, short of a per-document export covering the model used. Published: an auto-generated privilege log delivered as a spreadsheet, Bates numbering applied to firm conventions, a full audit trail described as supporting regulatory productions and defensibility before a regulator, and access and activity logs retained under clause 4.3 for at least twelve months and released to the customer on written request. A white paper on what courts expect from AI-assisted document review and how to document it is published, though it was not opened in this pass. What is missing is the model dimension: nothing states that the model behind a given classification is recorded or disclosed, and the OpenRouter routing layer means the specific model handling a document is not surfaced.