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ipQuants
ipQuants builds Qthena, a copilot for patent professionals built around a digital cockpit for working on documents. The cockpit handles review, annotation and management of published patent documents, with project views and panels for collaboration. An analytics layer lets a user query ipQuants' own datasets and statistics without uploading anything, including insights on individual EPO examiners and opposition and appeal board members.
The generative layer, AskQthena, runs document analysis, summarization, drafting and conversational queries across many documents. Qthena is organized into named skills: analysis across documents, EPO and USPTO examination, claim feature breakdown, prior art comparison, invention disclosure generation, patent drafting, and patent drawings from sketches, photographs or text. The company states that drafting covers about 80 percent of the work with the user finalizing, that drawing generation reliably completes around 90 percent, and that a human decides at each step.
It says publicly that it does not tie itself to one model provider and has moved its default between vendors. Users are corporate IP departments, law firms and R&D teams. Questel also distributes Qthena inside its own IP platform.
ipQuants, head to head
Side by side on the same 15 capability axes and 12 legal signals: where the two part company, and what neither one publishes.
Alternatives to ipQuants
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Similar is not the same, so check that each one does the job you need. No vendor pays for placement.
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Ordered by how closely the graded profiles match across the 15 capability axes, inside the same categories. See how each axis is graded in the methodology.
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 drive a core capability, and the agreement shows what sits underneath them. The terms divide the platform into three parts. The Qthena Digital Cockpit is for review, annotation and management of published patent documents. Analytics and big data features let a user query ipQuants' proprietary datasets and statistics without uploading their own content. Generative AI functionality, currently AskQthena, handles analysis, summarization, drafting and interactive queries.
Only the third is generative. The first two are a document workspace and a statistics product that would still work without the models, and the company's original pitch was data rather than generation, built on EPO examiner, opposition and appeal member insights. Recent product work has gone into the generative layer, with named skills for drafting, prior art comparison, invention disclosure and drawing generation. Even so, it is a layer on a working platform, not a product that disappears without it.
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 candid, and none of them is a measurement. The company states that Qthena's drafting skill does about 80 percent of the work with the user finalizing, and that drawing generation reliably completes around 90 percent. It says it held both skills back until quality was adequate and expects higher figures as models improve. It also lists failure modes it attributes to other tools: broken geometry, wrong perspective, missing edges and incorrect part definition.
Those are completion estimates, not accuracy figures. No test set, evaluation method or error rate is published, and nothing states how the percentages were reached. The terms disclaim accuracy directly: generative output is probabilistic, may not always be accurate, complete or appropriate, and may not be relied on as a sole source of truth. The sources are published patent documents and the company's own datasets, but no retrieval method is described.
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 that the human is always in charge and decides each step, and that the drafting skill produces a first draft the user must finalize. The terms make that an obligation. The customer must evaluate output for accuracy and appropriateness, including through human review as needed. Output must not be relied on as a sole source of truth or as a substitute for professional advice, or used to make decisions with a legal or material effect on an individual.
Review surfaces are real: the cockpit holds the documents, and drawings can be corrected conversationally, so a user can adjust reference signs and line work. What is missing is the threshold. The company says its team shows customers when AI generated drawings can be used, when they need correction and when they should not be used at all. That guidance is given during rollout and is not published.
Operational and Outcome Evidence
Named, dated evidence that the product works in production at real firms or legal departments. Case studies with figures and identified customers count. Unattributed testimonials and launch announcements do not.
ipQuants gives unattributed claims of scale in place of deployment evidence. It says Qthena is used by hundreds of corporate teams and law firms across the globe, without naming one. The one customer voice is anonymous, quoted only as a valued customer saying the product is for the smart user, with no firm, role or figures. No dated deployment, measured outcome or named reference is published. One partnership is documented by both parties, a different kind of fact from deployment evidence: Questel integrates Qthena into its own IP platform. The site navigation links a case studies page.
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 published agreement makes substantive commitments, and privilege is not addressed. Section 15 defines confidential information to include customer content, customer information and generative AI inputs and outputs generated for the customer, so what a patent attorney puts into the tool is contractually confidential. Section 15.6 names the LLM providers and states that customer input and output are not used to train or improve their models.
Identifying metadata such as email and IP addresses is not sent, access is through paid enterprise APIs, and abuse monitoring is off 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. Privilege and work product are not addressed anywhere.
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. 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.
A real published position on the advice line, with no engagement with professional conduct rules. The terms state that no legal advice is intended or offered by ipQuants in making the platform, its content or any tools, features or support available, and disclaim liability for decisions taken in reliance on any of it. Restrictions on how output may be used come with it. 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 about an individual must not be used for decisions with a legal or material effect on that person.
The public material is consistent: the company says the human is in charge at each step and does not 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, and no jurisdiction limit is drawn. Nothing addresses a representative's supervision or competence duties when a machine drafts a claim set or figures for filing.
AI Governance and Bias Disclosure
Published governance over model behavior: who owns it inside the vendor, what is tested before release, and what is disclosed about disparate output across matter types, parties, or populations.
No governance position of any kind is published. There is no responsible AI page, principles statement, accountable owner or function, testing regime before release, management system or certification such as ISO 42001. Nothing addresses uneven output, which here would bear on performance across technical fields, drawing types and the languages of patent documents. The company does publish a quality philosophy: it withholds a skill until it is useful, and it does not tie itself to one model provider.
That is product judgment, not a governance framework, and it names no reviewer, criteria or results. Its abuse monitoring setting is a confidentiality control. The site navigation and footer carry no governance page.
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.
Published policy covers most of the ground. Under the terms, customer content stays under customer control until the customer deletes it, and on expiry or termination ipQuants permanently deletes all customer accounts, data and information. Data sent to LLM providers is anonymized, sent over encrypted paid enterprise APIs, kept on a minimized basis in line with those providers' policies, and not used to train them. Each party must promptly tell the other of any unauthorized access to or disclosure of confidential information.
Availability carries a 99 percent uptime commitment, with fourteen days' notice of scheduled maintenance and prompt notice of unscheduled downtime. The gaps are specific: retention on the provider side states no period, and no encryption specification, access control model or audit logging is described. An Information Security Policy and a data processing addendum are linked without a login in the site footer.
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.
ipQuants publishes a real position, with an indemnity from the vendor and a low cap. Section 11.2 commits ipQuants to defend and indemnify the customer against third party claims that authorized use of the platform infringes intellectual property rights, covering damages and costs finally awarded by a US or European court. Its remedies are procuring the right to continue, modifying the platform, or ending use with a prorated refund.
Named exceptions cover customer alteration, unauthorized combination, use in an unauthorized region and claims arising from customer content. That indemnity is the customer's exclusive remedy for infringement. Section 12.1.1 commits to 99 percent uptime, and section 12.2 offers dedicated SLAs to enterprise customers. Against that, section 13 provides the platform as is, disclaiming all implied warranties including accuracy.
Section 14 caps cumulative liability at the lesser of five thousand euros or twelve months of fees, expressly including claims about generative AI output. There is no warranty on output, no insurance position, and no indemnity for the accuracy of what the models produce.
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 firm may already run, and no documentation an implementer could use. 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. Otherwise the platform is reached at its own application domain, and the terms allow authorized distributors to market and provide access.
Everything a practice would need to plan a deployment is absent. 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 is published, and nothing describes what data moves, in which direction or on what trigger. The terms restrict automated access, prohibiting robots, AI agents and crawlers from the platform or its content, which bears on programmatic integration. The navigation also has feature pages for workflow automation and collaboration.
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 clear, and neither tenancy nor region is stated. The terms define the platform as a suite of online applications at a named application domain and its subdomains, and no on premises or self hosted option is offered. Nothing states whether the platform is single or multi tenant, and no dedicated or isolated option appears. No region is named for storing or processing customer content, and no residency commitment is published, though the named LLM providers operate across multiple regions.
The only geographic facts published are corporate and contractual: a Schaffhausen address, Swiss governing law, mediation seated in Schaffhausen and fees in euros. None of them locates the data. An Information Security Policy and a data processing addendum, which would normally cover tenancy and residency, are linked in the footer.
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 published: no SOC 2 report of either type, ISO 27001 or ISO 42001 certificate, penetration test summary, named auditor, examination period or certification claim, and no trust center. ipQuants publishes an Information Security Policy as a footer link that needs no login, which is a policy it publishes itself, not an independent examination. Nothing on the site claims an audit, so there is no unsupported badge 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.
The providers and models are named, and notice of change is expressly reserved. The agreement itself names the LLM API providers as OpenAI, Microsoft Azure OpenAI and Google Gemini, and describes paid enterprise API access with anonymized transmission and no training on customer content. Public material names specific models: the company says it defaulted to GPT-4o and has defaulted to Gemini 2.5 since January 2025, and expects to move again as newer models arrive.
Notice of change is what fails, and deliberately. The same statement says the product is not tied to one model and the company can switch at any time, so a customer has no committed notice before the model reading its patent documents changes. Two limits apply. The model names sit in a blog post dated November 2025, not on a maintained model page, so whether they are still current is not clear. And no provider is named 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 detail, and there is no figure anywhere. There is no pricing page and every route to a number is a demo request, but the terms set out the shape of the deal. Subscriptions are per user, and one subscription covers one named user and may not be shared. Billing is annual in advance in euros, invoices are due in fourteen days, and fees cannot be canceled or refunded. The subscription renews automatically unless canceled with sixty days' written notice, and the number of user subscriptions cannot be reduced during a term.
Overdue amounts accrue interest at one percent per month, and taxes are excluded. Section 6 adds that accessible reports and downloads are limited by subscription tier and fair use, with limits that can change without notice. The unit, term, currency and commitment are public; tier names and every rate are not.
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 the boundary is left open. ipQuants publishes five named use cases: patent workflows, research and science workflows, technology and IP strategy, legal marketing and business development, and remote work. It names corporate IP teams and law firms as users. Patent coverage is specific by jurisdiction, with skills built around EPO and USPTO examination and analytics covering patent offices in Europe, the United States and Germany.
It also offers examiner and opposition and appeal member insights, which exist only where the underlying decision data does. What is absent is the limit. Nothing states which jurisdictions are not covered, no firm size is addressed, and nothing says whether trademark, design or litigation work is in or out. The marketing and business development use case sits beside the patent depth without any statement of how far it extends.
2 public documents
The public pages on file for ipQuants, 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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Client Data in Training, Ethical Walls and Matter Segregation, Third Party Request and Subpoena Notice
Read Sep 4, 2026
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Fabricated Citation Record
Read Sep 4, 2026
No published figure
- ipQuants does not publish a price, so you have to ask for a quote.
- What it does publish is how the deal works: you buy a subscription for each named person, and that seat cannot be shared.
- You pay for a whole year upfront in euros, and the money is not refundable if you stop using it.
- It renews itself automatically unless you give sixty days notice before the end of the year, and you cannot reduce the number of seats partway through.
- How many reports you can open and download depends on which tier you buy, and those limits can be changed without warning.
The structure is published in detail, with no figure at any level. There is no pricing page, and every commercial route is a demo request, but the published terms set out the shape. Subscriptions are sold per user, and one subscription covers one person and may not be shared. Fees are quoted and payable in euros, billed annually in advance, due fourteen days from invoice, and cannot be canceled or refunded. Fees are based on services purchased rather than actual usage, and the number of user subscriptions cannot be reduced during a term.
The subscription renews automatically unless either party gives at least sixty days' written notice before the term ends. Overdue amounts may accrue interest at one percent per month after a reminder and a seven day grace period, and taxes are excluded. Section 6 limits accessible reports by subscription tier and downloads by fair use, with limits that can change without notice. Enterprise customers may receive dedicated service level agreements that replace the standard availability terms. No rate, band, minimum or tier price is published, and no free tier exists.
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, not a policy page. The AskQthena content terms state that ipQuants and its LLM API providers, named as 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 models. Section 15.6 repeats it as a confidentiality obligation, adding that input and output processed by those providers is handled confidentially.
Two further provisions support it. Identifying metadata such as email and IP addresses is not sent to the providers, and abuse monitoring is off by default for at least one named provider, so prompts are not scanned. No exception for deidentified, anonymized or aggregated data appears in the agreement. Separately, section 10 gives ipQuants a perpetual license 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 gives the customer full control over all input and output generated through the platform, which stays accessible until the customer deletes it from ipQuants servers. Section 9 adds that on expiry or termination ipQuants will permanently delete all customer accounts, data and information in the platform. The limit is on the model provider side.
Section 15.6 says only that retention there is minimized and aligned with the LLM providers' policies, which names no period and points to a third party's policy rather than a term the customer holds. No zero retention option is offered, and no configurable retention period, as distinct from deletion by the customer, is described.
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.
Nothing published 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. It governs disclosure, not how one customer's workspace is partitioned from another's. Nothing states whether the platform is single or multi tenant, no permission or role model is described, and no walls between matters inside a firm's own workspace are addressed, on a product built around shared project views, panels and collaboration. An Information Security Policy and a data processing addendum, where this would normally sit, are linked in the footer.
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 sits in the confidentiality section of the published agreement. 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, to the extent permitted by law, so it may seek a protective order or other remedy. It must also use reasonable efforts to limit the disclosure and maintain confidentiality.
That reaches customer content and generative AI inputs and outputs, which section 15.1 defines as confidential information. The obligation is mutual and generic rather than a law enforcement policy aimed at customers, and no transparency report or record of requests is published. Notice may be withheld where the law bars it, a standard qualifier.
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 sources are described by jurisdiction and 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 from providers such as patent office products as the example, and disclaim responsibility for the content, completeness, accuracy or quality of that data. No specific provider, database or publisher is named, and no licensing position is published.
Jurisdictional reach is described precisely: EPO and USPTO examination skills, analytics covering patent offices in Europe, the United States and Germany, and proprietary datasets of examiner, opposition and appeal member decisions. The vendor also asserts its own rights over the resulting content, licensing rather than selling it and prohibiting an archival or searchable database built from it. That governs the customer's license, not the vendor's own sources.
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 idea does not transfer cleanly to this product. The platform works on published patent documents, office examination records and opposition and appeal decisions, not case law with a citation history, so there is no citator or treatment signal to describe. The analytics show how individual examiners and appeal board members have decided, which is decision data used for strategy, not a check on whether an authority still stands.
Nothing says how current the underlying office data is or how often it refreshes, which is the nearest live question here.
Refusal and Uncertainty Behavior
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.
Nothing published describes what the system does when it cannot answer, though the company is direct about the limits of what it produces. It says the drafting skill completes about 80 percent of the work and drawing generation around 90 percent, that the user must stay in control to finalize, and that its team shows customers when AI generated drawings can be used, need correction or should not be used. That is guidance to the user, not a description of system behavior.
Nothing says 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 require the customer to evaluate output for accuracy, including through human review, which places the burden on the user rather than describing a way to abstain.
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.
The AI Hallucination Cases database maintained by Damien Charlotin records no court order, opinion or disciplinary record naming Qthena or ipQuants.
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.
Nothing published engages with professional conduct guidance at any level. No bar association, patent office code of conduct, rule of professional conduct, ethics opinion or guidance for representatives in any jurisdiction is named or referred to. Nothing maps a professional's obligations when a machine drafts a claim set, an invention disclosure or a set of figures for filing. The one related statement is a disclaimer: the terms say no legal advice is intended or offered, which is a position on the advice line rather than conduct guidance.
European and US patent practice both impose duties on the representative who signs, and nothing addresses how the tool's output sits against them.
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.
Efficiency claims are published, and nothing addresses the billing consequence. The company states that Qthena's drafting skill does about 80 percent of the work and that drawing generation completes around 90 percent. It frames the drawing capability as affordable enough for whole teams rather than a few users sharing an account. Those are time and cost claims about 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 record of AI assisted work for each matter is described, no guidance on fee or disclosure treatment is published, and nothing addresses whether a firm should tell a client that a draft specification, invention disclosure or set of figures was machine generated. The per user subscriptions and tiered report limits are the vendor's own charging model, not 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, and no wider subprocessor register is published. Section 15.6 names the LLM providers as OpenAI, Microsoft Azure OpenAI and Google, says access is through their paid enterprise APIs, and commits that customer input and output are not used to train or improve those models. Transmission is encrypted, identifying metadata is not shared, and abuse monitoring is off by default for at least one named provider.
Because the terms are published, a firm can forward that section to a client without an agreement or a sales conversation, and it answers who sees the client's content. No subprocessor register beyond the model layer is published, and no hosting or infrastructure provider is named. A data processing addendum is linked in the footer.
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.
Nothing 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 nothing 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: 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 matters 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.