ipQuants
ipQuants builds Qthena, a co-pilot for patent professionals that sits around a digital cockpit for working on documents rather than a search box. The platform has three parts. The cockpit handles document review, annotation and management, primarily of published patent documents, with project views and panels built for collaboration. An analytics layer lets a user query ipQuants' own datasets and statistics without uploading anything, and the company was first to publish insights on individual EPO examiners and opposition and appeal board members, so a professional can shape a prosecution, opposition or appeal strategy around who will decide it. The generative layer, AskQthena, runs document analysis, summarisation, drafting and conversational queries across many documents at once. Qthena is organised into named skills rather than a single assistant: multi-document analysis, EPO and USPTO examination, patent claim feature breakdown, prior art comparison, invention disclosure generation, patent drafting, and patent drawing generation from sketches, photographs or text descriptions, with drawings correctable conversationally. The company publishes unusually specific limits on what those skills do: it states that drafting produces a strong first draft covering about 80 per cent of the work with the user finalising, that drawing generation reliably completes around 90 per cent, and that a human decides at each step. Its approach to models is deliberately provider-agnostic, and it says so in public, having moved its default between vendors as capability changed. Users are corporate IP departments, law firms and R&D teams, and the platform is also sold for technology and IP strategy and for business development work. Qthena is additionally distributed through Questel, which integrates it into its own IP platform. ipQuants AG is independent and based in Schaffhausen, Switzerland.
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 engine of a core capability and the agreement itself shows what sits underneath them. The terms divide the platform into three components: the Qthena Digital Cockpit, a feature set for document review, annotation and management of published patent documents; analytics and big data features that let a user query ipQuants' proprietary datasets and statistics without uploading their own content; and generative AI functionality, currently AskQthena, for analysis, summarisation, drafting and interactive queries. Only the third is generative. The first two are a document workspace and a statistics product that would still work with the models removed, and the company's original distinguishing claim was data rather than generation, being first to publish EPO examiner, opposition and appeal member insights. The generative layer is plainly where the recent product work has gone, with named skills for drafting, prior art comparison, invention disclosure and drawing generation, but this is a machine learning layer on a working platform rather than a product that disappears without it. 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.
The published limits are unusually candid and none of them is a measurement. The company states that Qthena's drafting skill does about 80 per cent of the work with the user finalising, that drawing generation reliably completes around 90 per cent, that it deliberately withheld both skills until quality was adequate, and that it expects a higher figure as underlying models improve. It also names the failure modes it says competitors ship with, listing broken geometry, wrong perspective, missing edges and incorrect part definition. Those are completion estimates rather than accuracy figures: no test set is described, no evaluation method is published, no error rate against a benchmark appears, and nothing states how the percentages were arrived at. The terms disclaim accuracy directly, stating that generative output is probabilistic, may not always be accurate, complete or appropriate, and may not be relied on as a sole source of truth. Grounding is real in the sense that the corpus is published patent documents and the company's own datasets, but no retrieval method is described, which is what the band above requires.
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 human decides, backed by real constraints in the agreement, short of published thresholds. The company states plainly that it will always ensure the human is in charge and decides on each step, and that the drafting skill produces a first draft the user must finalise. The terms carry that into obligations rather than leaving it as marketing: the customer must evaluate output for accuracy and appropriateness including through human review as needed, must not rely on output as a sole source of truth or as a substitute for professional advice, and must not use output relating to an individual to make decisions with a legal or material effect on that individual. Review surfaces are real, with the cockpit holding the documents and drawings correctable conversationally so a user can adjust reference signs and line work. What is missing is the threshold. The company says it shows customers when AI-generated drawings can be used, when they need human correction, and when they should not be used at all, but that guidance is delivered by its team during rollout rather than published, so a buyer cannot read it before committing.
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.
Unattributed claims of scale stand in for deployment evidence on the surfaces that were read. The company states that Qthena is used by hundreds of corporate teams and law firms across the globe, and that many corporate teams and law firms trust it worldwide, without naming one. The single customer voice located is anonymous, quoted only as a valued customer saying the product is for the smart user, and it carries no firm, no role and no figures. No dated deployment, no measured outcome and no named reference was located on the pages read. One first-party partnership is documented and is a different kind of fact, recorded rather than credited as deployment evidence: Questel integrates Qthena into its own IP platform, announced by both parties. A dedicated case studies page exists in the site navigation and was not opened in this pass; it is named here as the limit and is the cheapest available upgrade on this record.
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.
Substantive commitments in the published agreement, including the one most vendors leave out, and no privilege treatment. Section 15 defines confidential information to expressly include customer content, customer information and generative AI inputs and outputs generated on behalf of the customer, so the material a patent attorney puts into the tool is contractually confidential rather than merely secure. Section 15.6 goes further than most: LLM providers are named, customer input and output are stated not to be used to train or improve those models, identifying metadata such as email and IP addresses is stated not to be transmitted, access is through paid enterprise APIs, and abuse monitoring is deactivated by default for at least one named provider so prompts are not scanned. Section 15.6.2 leaves content under customer control until the customer deletes it, and section 9 commits to permanent deletion of all customer data on termination. Two things hold it here. No privilege or work product treatment appears anywhere, and that limb is required rather than satisfied by a strong confidentiality regime. And nothing describes separation between customers or between matters inside a firm's own workspace, on a product built around shared project views and collaboration.
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.
A real published position on the advice line, with no engagement with professional conduct rules. The terms state expressly that no legal advice is intended or offered by ipQuants in making the platform, its content or any tools, features or support interactions available, and disclaim liability for decisions taken in reliance on any of it. That is paired with substantive restrictions on how output may be used rather than left as a single sentence: output may not be relied on as a sole source of truth or as a substitute for professional advice, the customer must evaluate it including through human review, and output relating to an individual must not be used to make decisions with a legal or material effect on that individual. The public material is consistent with it, the company stating that the human is in charge at each step and declining to claim the tool replaces skilled drafting. What is absent is the professional layer: no bar or patent-office conduct rules are named, no ethics guidance is referenced, no jurisdiction limit is drawn, and nothing addresses a representative's supervision or competence duties when a machine drafts a claim set or a set of figures for filing.
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.
No governance position of any kind was located. There is no responsible AI page, no principles statement, no accountable owner or function named, no pre-release testing regime described, no management system and no certification such as ISO 42001. Nothing addresses uneven output at all, which on this product would bear on performance across technical fields, drawing types and the languages of the patent corpus. The company does publish a quality philosophy, stating that it withholds a skill until it is genuinely useful and that it is provider-agnostic about models, and that is product judgement rather than a governance framework: it names no reviewer, no criteria and no results. Its abuse monitoring configuration is a confidentiality control and is graded on the privilege row rather than counted twice here. The site navigation and footer were inventoried on 4 September 2026 and carry no governance surface.
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.
Substantive published policy covering most of the ground, thinner where the documents could not be read. From the terms: customer content stays under customer control and remains accessible until the customer deletes it; on expiry or termination ipQuants commits to permanently delete all customer accounts, data and information held in the platform; data transmitted to LLM providers is anonymised, sent over encrypted paid enterprise APIs, retained on a minimised basis aligned to those providers' policies, and not used to train them; each party must inform the other promptly of any unauthorised access to or disclosure of confidential information; and availability carries a 99 per cent uptime commitment with fourteen days' notice of scheduled maintenance and prompt notice of unscheduled downtime. The gaps are specific rather than general. Retention on the model provider side is described as minimised and aligned with provider policies, which states no period. No encryption specification, access control model or audit logging is described in the material that could be read. A full Information Security Policy and a data processing addendum are published as ungated links in the site footer; both resolve to a document viewer that returned only a title and a thumbnail, so their contents are unread and are neither credited nor held against the vendor.
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 with a vendor-side indemnity, and a cap a buyer should read carefully. Section 11.2 commits ipQuants to defend and indemnify the customer against third-party claims that authorised use of the platform infringes intellectual property rights, with damages and costs finally awarded by a US or European court covered, and sets out remedies if infringement is alleged, running to procuring the right to continue, modifying the platform, or ceasing use with a prorated refund. Named carve-outs cover customer alteration, unauthorised combination, use in an unauthorised region and claims arising from customer content. That indemnity is stated to be the customer's exclusive remedy for infringement. Section 12.1.1 commits to 99 per cent uptime and section 12.2 offers dedicated SLAs to enterprise customers. Against that, section 13 provides the platform as is with all implied warranties disclaimed including accuracy, and section 14 caps cumulative liability at the lesser of five thousand euros or twelve months of fees, expressly including claims relating to generative AI output. There is no warranty on output, no insurance position, and no indemnity touching the accuracy of what the models produce, which is the exposure this product creates.
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.
One documented connection into a system a buyer already runs, and no documentation an implementer could use. The substantial fact is the Questel relationship: Questel integrates Qthena into its own IP management platform and both companies have published the arrangement, so a firm already on Questel can reach the product there. Beyond that the platform is reached at its own application domain, and the terms contemplate authorised distributors marketing and providing access. What is absent is everything a practice would need to plan a deployment: no document management integration is named, so nothing addresses iManage or NetDocuments, nothing addresses Word, Outlook or an IP docketing system, no API or developer documentation was located, and no statement describes what data moves, in which direction or on what trigger. The terms in fact restrict automated access, prohibiting robots, AI agents and crawlers from accessing the platform or its content, which bears on programmatic integration. Feature pages for workflow automation and collaboration exist in the navigation and were not opened in this pass; they are named here as the limit and the grade rests on what was read.
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.
Cloud delivery is unambiguous and neither of the co-equal limbs is stated in the material that could be read. The terms define the platform as a suite of online, web-based applications reached at a named application domain and its subdomains, so delivery is not in doubt and no on-premises or self-hosted option is offered anywhere. Nothing read states whether the platform is single or multi-tenant, and no dedicated or isolated option appears. No region is named for storage or for processing of customer content, and no residency commitment was located, which is a live question for European IP work given that the named LLM providers operate across multiple regions. The only geographic facts published are corporate and contractual rather than architectural: a Schaffhausen address, Swiss governing law, mediation seated in Schaffhausen and fees denominated in euros, none of which locates the data. The Information Security Policy and data processing addendum published in the footer are the surfaces where tenancy and residency would normally sit; both returned a viewer shell rather than their contents, and that is recorded as a limit on the reading rather than as a gap in the disclosure.
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 was located. No SOC 2 of either type, no ISO 27001 or ISO 42001, no penetration test summary, no named auditor, no examination period and no certification claim of any kind appears on the pages that were read, and there is no trust centre or trust portal on the estate. The company does publish an Information Security Policy as an ungated link in its site footer, and that is a real disclosure artifact; it is a self-published policy rather than an independent examination, so it does not answer what this axis asks, and its existence is credited on the stewardship row instead of twice. Its contents could not be read: the link resolves to a hosted document viewer that returned the file title and a thumbnail only, which is a retrieval limit on this side and is not treated as an absence. The distinction that matters to a buyer is undrawn either way, since nothing on the readable estate claims an audit, so there is no unsupported badge here and no attestation either.
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.
Three of the four limbs are answered, and the fourth is expressly reserved rather than merely absent, which is a better disclosure than most and stops short of the top band. The providers are named in the agreement itself rather than in marketing: the terms identify the LLM API providers as OpenAI, Microsoft Azure OpenAI and Google Gemini, and describe the hosting arrangement as paid, enterprise-grade API access with anonymised transmission and no training on customer content. Specific models are named in current public material, the company stating that it defaulted to GPT-4o and has defaulted to Gemini 2.5 since January 2025, with an expectation of moving again as newer models arrive. What fails is change notification, and it fails deliberately: the same statement declares the product LLM-agnostic and says the company can switch at any time, so a customer has no committed notice before the model reading its patent documents changes. Two limits belong on the record. The model names sit in a blog post dated November 2025 rather than on a maintained model page, so a buyer in September 2026 cannot tell whether they are current. And no provider is identified for hosting or infrastructure outside the model layer.
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 commercial structure is published in real detail and there is no figure anywhere. There is no pricing page in the site navigation and every route to a number is a demo request, but the terms set out the shape of the deal more fully than most published price pages do: subscriptions are per user, one subscription entitles one named user and may not be shared, billing is annual in advance in euros, invoices are due net fourteen days, fees are non-refundable and non-cancellable, the subscription auto-renews unless cancelled with sixty days written notice, the number of user subscriptions cannot be reduced during a term, overdue amounts accrue interest at one per cent per month, and taxes are excluded. Section 6 adds that the number of accessible reports and the volume of downloads are limited by subscription tier and subject to fair use, with limits amendable without notice. A buyer can therefore describe the unit, the term, the currency and the commitment before contacting the company, and cannot estimate the cost. Tier names are not published and no enterprise or entry rate appears at any level.
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 real substance across two dimensions, and the boundary is left open. The vendor publishes five named use cases rather than a generic audience statement: patent workflows, research and science workflows, technology and IP strategy, legal marketing and business development, and remote work, and it names both corporate IP teams and law firms as users. Practice depth within patents is specific and unusual for its jurisdictional precision, with skills built around EPO and USPTO examination and analytics covering patent offices in Europe, the United States and Germany, plus examiner and opposition and appeal member insights that only exist where the underlying decision data does. That jurisdictional shape is itself the strongest coverage statement on the record. What is absent is the limit: nothing states which jurisdictions are not covered, no firm size is addressed, nothing says whether trademark, design or litigation work is in or out, and the marketing and business development use case sits oddly beside the patent depth without any statement of how far it extends.
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?
The published terms prohibit training on customer content. Not a policy page, the agreement.
The commitment sits in the published agreement rather than in a policy page. The AskQthena content terms state that ipQuants and its LLM API providers, naming OpenAI, Microsoft Azure OpenAI and Google Gemini, do not use customer data or input for model training, and that no input or output is used to train or improve the LLM models. Section 15.6 repeats it as a confidentiality obligation, adding that input and output processed by those providers is handled confidentially and not used to train or improve them. Two supporting provisions strengthen it rather than qualify it: identifying metadata such as email and IP addresses is stated not to be transmitted to the providers, and abuse monitoring is deactivated by default for at least one named provider so prompts are not scanned. No de-identification, anonymisation or aggregation carve-out appears anywhere in the agreement. One narrower reservation is recorded for completeness and does not touch customer content: section 10 gives ipQuants a perpetual licence over feedback and suggestions, which section 15.3.5 excludes from confidential information.
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.
Retention on the platform is under the customer's control and is stated in the agreement. Section 15.6.2 provides that the customer retains full control over all input and output generated through the platform, and that such content remains accessible to the customer within the platform until the customer deletes it from ipQuants servers, so the customer sets how long prompts and outputs persist. Section 9 adds an end-state commitment, that on expiry or termination ipQuants shall permanently delete all customer accounts, data and information stored within the platform. The limit is on the model-provider side and is recorded rather than smoothed: section 15.6 states only that data retention there is minimised and aligned with the policies of the LLM providers, which names no period and points at a third party's policy rather than a term the customer holds. No zero-retention option is described or offered, and no configurable retention period as distinct from customer-initiated deletion was located.
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.
No located public material addresses walls or separation between customers or between matters. The confidentiality regime in section 15 is strong on what may be disclosed and by whom, and expressly covers customer content and generative AI inputs and outputs, but it governs disclosure obligations rather than how one customer's workspace is partitioned from another's. Nothing read states whether the platform is single or multi-tenant, no permission or role model is described, and no matter-level walls inside a firm's own workspace are addressed, which is a live question on a product built around shared project views, panels and collaboration features. Two documents where this would normally sit are published as ungated footer links, an Information Security Policy and a data processing addendum, and both resolve to a hosted document viewer that returned a title and thumbnail only; their contents are unread, so nothing is inferred from them in either direction. Searched the terms and conditions, the product navigation, the blog and the footer inventory on 4 September 2026.
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.
A notice commitment in the confidentiality section of the published agreement, which is where this evidence belongs. Section 15.4 provides that where a party is required by law, regulation or court order to disclose the other's confidential information, it shall promptly notify the disclosing party of that requirement, to the extent permitted by law, so the disclosing party may seek a protective order or other appropriate remedy, and shall use reasonable efforts to limit the disclosure and maintain confidentiality so far as possible. That reaches customer content and generative AI inputs and outputs, which section 15.1 defines as confidential information, so it covers the material a patent professional puts into the tool rather than only business information. Two limits keep this below the top value. The obligation is mutual and generic rather than a customer-facing law-enforcement policy, and no transparency report or record of requests received is published. The qualifier permitting silence where notice is legally barred is standard and is recorded rather than counted against it.
Primary Law Corpus Provenance
Where does the law in this product come from, and does the vendor have the right to use it?
Coverage is described by jurisdiction with no identification of the underlying corpus.
The corpus is described by jurisdiction and the sources behind it are not named or licensed in public. The terms state that the platform uses third-party products and services to provide its content, giving patent publications obtained via third-party providers such as patent office products as the example, and disclaim responsibility for the content, completeness, accuracy or quality of that third-party data. No specific provider, database or publisher is named, and no licensing position is published. What is described precisely is jurisdictional reach: EPO and USPTO examination skills, and analytics covering patent offices in Europe, the United States and Germany, together with proprietary datasets of examiner, opposition and appeal member decisions that only exist where the underlying decision data does. The vendor also asserts its own rights over the resulting content, licensing rather than selling it and prohibiting the creation of an archival or searchable database from it, which addresses the customer's licence rather than the vendor's own.
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 addresses checking authority for subsequent history, and the concept does not transfer cleanly to this product class. The platform works on published patent documents, office examination records and opposition and appeal decisions rather than on case law with a citation history, so there is no citator, no treatment signal and no equivalent currency check to describe. One adjacent capability is recorded so a reader sees it was weighed: the analytics identify how individual examiners and appeal board members have decided, which is decision data used for strategy rather than a check on whether an authority still stands. No statement was located on how current the underlying office data is or how often it refreshes, which is the nearest live question for a buyer here. Searched the terms and conditions, the product navigation, the blog and the footer inventory 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 material describes what the system does when it cannot answer, though the company is unusually direct about the limits of what it produces. It publishes that the drafting skill completes about 80 per cent of the work and that drawing generation reliably completes around 90 per cent, that the user must stay in control to finalise, and that its team shows customers when AI-generated drawings can be used, when they need human correction and when they should not be used at all. All of that is guidance to the user about where to apply judgement, not a description of system behaviour: nothing states whether the model declines, flags, retries or caveats when it cannot ground an output, and no confidence signal or no-answer state is described. The terms address the same territory as an allocation of responsibility, requiring the customer to evaluate output for accuracy including through human review, which places the burden rather than describing an abstention path. Searched the terms, the blog, the product navigation and the footer inventory 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 the product name Qthena and on the corporate name ipQuants. No court order, opinion or disciplinary record naming the product or the company 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?
No located public material engages with bar or ethics guidance.
No located material engages with professional conduct guidance at any level. No bar association, patent office code of conduct, rule of professional conduct, ethics opinion or jurisdiction-specific guidance for representatives is named or referred to in general terms, and nothing maps a professional's obligations when a machine drafts a claim set, an invention disclosure or a set of figures intended for filing. The one adjacent statement is a disclaimer rather than an alignment: the terms state that no legal advice is intended or offered, which is a position on the advice line and is graded on the professional responsibility row rather than counted here. The gap is worth stating precisely on this product class, because European and US patent practice both impose duties on the representative that signs, and nothing published addresses how the tool's output sits against them. Searched the terms, the product navigation, the blog and the footer inventory on 4 September 2026.
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.
Efficiency claims are published and nothing addresses the billing consequence. The company states that Qthena's drafting skill does about 80 per cent of the work and that drawing generation completes around 90 per cent, and frames the drawing capability as needing to be affordable enough for entire teams rather than a few users sharing an account, which are time and cost claims directed at the customer's own economics. None of it reaches what happens to a client's bill when a drafting task that took days takes hours. No per-matter record of AI-assisted work is described as available, no guidance on fee or disclosure treatment is published, and nothing addresses whether a firm should tell a client that a draft specification, an invention disclosure or a set of figures was machine-generated. The commercial terms describe per-user subscriptions and tiered report limits, which is the vendor's charging model rather than an answer to this question.
Outside Counsel Guideline Readiness
Can a firm get this vendor through a client’s AI clause without a bespoke negotiation?
A current subprocessor or model provider list is published.
The model providers are named in the agreement itself, which is the hard part of this question, and the full pack was not established. Section 15.6 identifies the third-party LLM providers as OpenAI, Microsoft Azure OpenAI and Google, states that access is through their paid enterprise APIs, and commits that customer input and output is not used to train or improve those models, that transmission is encrypted, that identifying metadata is not shared, and that abuse monitoring is deactivated by default for at least one named provider. Because the terms are published, a firm can forward that section to a client verbatim without an agreement or a sales conversation, which answers the question a client AI clause actually asks about who sees its content. What was not established is a subprocessor register covering the platform beyond the model layer, with no hosting or infrastructure provider named anywhere. A data processing addendum is published as an ungated footer link but resolves to a document viewer returning only a title and thumbnail, so its contents and any subprocessor annex are unread and are not credited.
Court Disclosure Support
If a judge’s standing order requires an AI disclosure, can the product produce one?
No located public material addresses court disclosure or verification certification.
No located material addresses producing a record of AI-assisted work. Nothing describes an export covering which model produced an output, which sources it drew on, or what a human changed before filing, and no attribution marks any part of a generated draft, disclosure or drawing as machine-generated. No disclosure template or guidance is published. Two published facts sit nearby and neither does this job, so both are recorded: the terms assign ownership of generative output to the customer and leave content under customer control within the platform, which settles who owns the record rather than whether one exists, and the cockpit holds documents with annotation and version features that record user activity rather than model activity. The gap has a specific edge here, since the United States Patent and Trademark Office and other offices have been active on disclosure of AI use in prepared filings, and nothing published helps a representative answer that.