Cypris vs PatSnap: how they compare in 2026

The platform is organized around agents and priced by model usage. The site leads with AI agents for every stage of the R&D process, built for technology scouting, literature review, competitive intelligence, prior art, regulatory research and white space analysis, with agents that retain project context and can be configured to a team's own methods. Semantic retrieval across the data runs through a proprietary ontology rather than keyword matching. The agreement makes the metering explicit: AI credits are allocated annually and consumed per session at a rate reflecting the processing cost of the model chosen. Remove the models and a large licensed database remains, which is what the vendor positions itself against. The human side is separate: Research Briefs are written by the vendor's analysts, and the site calls them built for decisions where accuracy matters too much to leave to AI alone.

Founded in 2007, PatSnap's underlying asset is its data: more than 2 billion structured data points and over 210 million patent, science and technology records, with search, landscaping, dashboards and analytics that predate generative AI and work without it. The vendor describes Eureka as a capability layer rather than a separate platform. That layer spans agents for prior art, freedom to operate, drafting, translation and life sciences work, and the vendor describes a four stage data pipeline built to feed it. Without the models, PatSnap remains a working patent analytics business.

Every AI output is described as anchored to source citations, and the published comparative study makes that checkable. It states its method, names the date, preserves the outputs, gives the verbatim queries, and says the patent numbers and assignees were checked against USPTO Patent Center and WIPO PATENTSCOPE. The study also discloses that the vendor commissioned it and has a commercial interest, and records a place where its own tool fell short, failing to fully separate two related bodies of work. No measurement of the platform's own precision or recall against a gold standard is published: the study counts what competitors missed rather than what Cypris misses. The agreement runs the other way, disclaiming any warranty that results will be accurate, complete or error free.

PatentBench is published as a benchmark built for real patent work, measuring specific tasks against structured data, legal context and source linked evidence, with a full methodology the vendor links to. The published results are a 77 percent hit rate on design freedom to operate and a claim of being 83 times more accurate than ChatGPT on that measure. Grounding is described in the architecture: retrieval augmented generation over a governed corpus, a four stage pipeline the vendor says reduces hallucinations, and source linked evidence as a property of the benchmark. The benchmark is the vendor's own rather than independent, and the 83 times figure compares against a general purpose chatbot on a task it was not built for.

Agents are published as executing research workflows across large datasets in parallel, generating deliverables and monitoring continuously, which is autonomy over research rather than over a client matter. Against that, the vendor sells Research Briefs as human in the loop work written by trained analysts, and frames them as being for decisions too important to leave to AI alone. That is a statement about a different product, not a checkpoint in the automated one. Nothing published describes what an agent decides on its own, at what point a person reviews an agent's output before it informs an investment or filing decision, or what the system does when its evidence is thin. The agreement is silent on oversight, and the vendor reserves the right to change the available models at any time.

The platform is described as agentic across IP and life sciences, with named agents including an FTO Agent, a lead compound agent, an Antibody Target Predictor and a Markush Claim Drafting agent. A published customer account describes completing infringement analysis for new products in hours rather than weeks. Data governance is described at each stage of the pipeline, which governs inputs rather than agent behavior. Nothing published states what an agent may complete without human review, whether any output can be relied on without verification, what confidence signal accompanies an agent result, or where a human checkpoint sits in a workflow that runs from query to report. The outputs drive decisions: a freedom to operate assessment clears a product for launch, and a novelty judgment decides whether a molecule is pursued.

Customers are named across three industry sections with what each uses the platform for: Johnson & Johnson for cross domain research intelligence spanning materials science and pharma, Yamaha for scouting emerging technologies and landscaping suppliers, Oerlikon for monitoring compound activity and tracking competitive filings across global patent offices. Government and research customers named elsewhere on the site include NASA, the US Air Force and Los Alamos National Laboratory. Two testimonials carry a name and title, Chuck Wright, Corporate R&D Manager at NOV, and John Parks, Senior Footwear Innovation Developer at Brooks Running, each with a published customer story. Scale is given as hundreds of R&D teams and thousands of researchers. The strongest figure is that work which took weeks now takes days, with no baseline, period or method behind it.

Named customers include NASA, Tesla, Vodafone, MIT and General Electric, reported in independent trade coverage as well as on vendor pages. NCH Corporation is also named, with Michael Schuster, VP of Product Development and Innovation, quoted by name and title. Corporate history is public: founded in 2007, a unicorn valuation in 2021 after a $300m Series E led by SoftBank Vision Fund 2 and Tencent, and Jeffrey Tiong as chief executive. Independent sources include a Gartner Peer Insights listing, a SoftwareOne marketplace listing, and a hands on review by a named trade publication that took a trial and published its assessment. The outcome claims, productivity up to 60 percent higher and workflows 90 percent faster, carry no method, and one customer quote about hundreds of thousands of dollars saved names no organization.

In the published agreement, confidentiality is mutual, covers information whether or not marked confidential, restricts disclosure to employees with a need to know, and survives five years after disclosure, with trade secrets protected for as long as the law protects them. On termination the receiving party must return or destroy every copy and certify the destruction in writing. Compelled disclosure requires written notice to the other party first and a reasonable effort to obtain a protective order. The customer owns all right, title and interest in its data, and the vendor's carve out extends only to data derived from monitoring use of the service. Around that sit tenant isolation, row level security for the most sensitive data, least privilege access, background checks and audited access. No subprocessor list is published, no data processing addendum is referenced, and nothing treats the disclosure risk that unfiled invention material carries.

PatSnap publishes strict data isolation, ISO certified security policies, encryption in transit and at rest, consent management, GDPR and CCPA compliance, and positioning as a privacy first partner for enterprises, covering the confidential research and portfolio material customers upload. Nothing mentions attorney client privilege or work product, or treats a law firm user's material as carrying professional obligations distinct from a corporate research team's. PatSnap sells across research and development, strategy, life sciences and legal, and treats confidentiality as an enterprise data question throughout.

The agreement disclaims all warranties and states specifically that no warranty is given that results will be accurate, complete or error free. Nothing says the output is not legal advice, nothing addresses an attorney client relationship, and nothing addresses who should review a freedom to operate assessment before a company acts on it. The output is legal in character: per patent freedom to operate risk ratings, novelty and prior art assessment, and white space identified for new filings. On audience, Cypris says two different things. Product surfaces address IP teams, a patent committee and IP counsel directly. The vendor's own comparison writing says the platform is built for researchers rather than IP attorneys and points readers to other tools for prosecution and IP legal work. Both are the vendor's own words.

The platform generates freedom to operate assessments, novelty and design patent risk analyses, and drafts patent claims including Markush structures, outputs that a practitioner signs off or a business relies on. The published customer accounts describe research and development staff who are not lawyers using them for risk decisions. Nothing published addresses whether output is legal advice, what role qualified counsel plays where an engineer or chemist runs a freedom to operate agent directly, or any professional conduct framework. Named buyers include scientists and engineers as well as attorneys.

The site argues at length that general purpose models cannot do patent intelligence, citing training data limits, the closure of the web to crawlers and the absence of ontological frameworks for claim scope, prosecution history and assignee normalization. It commits to being model agnostic and to updating its ontology continuously. Nobody is named as accountable for model behavior, and nothing describes what is tested before an agent or ontology change ships. No disclosure addresses whether coverage or ranking differs across technology domains, languages or patent offices. No evaluation of the platform's own coverage gaps is published.

The vendor publishes that a four stage pipeline processes over 2 billion data points across 20 domains with rigorous governance at every stage, says this is what reduces hallucinations and improves reliability, and names its retrieval architecture. PatentBench is a published evaluation instrument. No AI policy, model card, bias or fairness testing, accuracy monitoring beyond the benchmark, drift statement, named governance body, ISO 42001 or EU AI Act positioning is published.

Cypris publishes encryption in transit and at rest from the interface to the storage layer, HTTPS with modern TLS, hosting on major cloud infrastructure with network and data segmentation, firewalls and automated threat detection, least privilege production access that is regularly reviewed, row level security for the most sensitive data, multifactor authentication and single sign on, employee background checks, annual penetration testing by independent firms and continuous vulnerability scanning. Data is stored within United States borders with no offshore routing. Customer data is never used to train models and is not shared with model providers for training. No retention period is published for prompts, outputs or uploaded documents, no subprocessor list is published, and the agreement carries no breach notification commitment, so what happens after an incident is unaddressed.

Strict data isolation, ISO certified security policies, encryption in transit and at rest with named protocol and cipher, GDPR and CCPA compliance with stated consent management, and a self description as privacy first are all published. Nothing is published on whether documents customers upload, search histories, proprietary formulations or research materials are used to train or improve the domain models, and no retention period or deletion right is stated. Customers upload their own novel compounds, formulations and unpublished technical material.

The agreement gives the customer an indemnity against third party claims that the service infringes or misappropriates US patents, copyrights or trade secrets. It comes with stated conditions, two exceptions and the vendor's right to modify, replace or terminate if it cannot cure. The customer indemnifies the vendor for negligence, unauthorized use and unapproved combinations. Liability is capped at fees paid in the twelve months before the claim, for both the infringement indemnity and the agreement as a whole. Consequential, incidental and punitive damages are excluded, along with loss of data and breach of data or system security. A wrong answer is not covered: the warranty section disclaims any assurance of accuracy or completeness, so a freedom to operate assessment that misses a blocking patent carries no recourse. No insurance or service level is published.

No position is published on liability for AI output, warranty, service levels or remedy. The outputs feed business decisions rather than filings: a published customer account describes the FTO Agent completing infringement analysis in hours, and another describes lead compound screening as the first gate in a research process, so a false clearance sends a product to market and a false negative on novelty ends work on a viable molecule. The vendor publishes a 77 percent hit rate on design freedom to operate, which by its own arithmetic means roughly one in four is missed.

The agreement treats API and Model Context Protocol access as part of the service rather than an add on. Integrated Intelligence connects a customer's own Copilot, Claude or ChatGPT to the patent, scientific, chemical, regulatory and market data, with prebuilt stage gate agents or custom agents built around the customer's workflows, and the vendor describes enterprise API partnerships with OpenAI, Anthropic and Google. Journal access runs through a named third party with published terms: the Cypris Journal Connection links a customer's existing journal subscriptions through OpenAthens single sign on, with the OpenAthens flow down terms incorporated by reference. Single sign on is supported with leading identity providers. The vendor says integrations into electronic lab notebooks and enterprise tools are expanding, and no developer documentation is published.

The Patsnap Open Platform is a dedicated developer product rather than a connector list, with four integration surfaces: REST APIs, MCP servers, UI widgets and agent skills, backed by a published Developer Center and documentation. Coverage is counted: more than 20 patent APIs, more than 25 corporate APIs and more than 16 infringement APIs. The MCP servers expose the data and its agents as tools for outside AI systems, and the vendor names the frameworks it targets, including LangChain, AutoGen and function calling workflows. An API key is available from the site, so a developer can assess, obtain and build against the platform without contacting sales. No IP management or docketing system integration is named.

The platform is built and operated in the United States, with infrastructure across datacentres, hosting and operations based in the US, and Cypris states there is no offshore data routing, with no exceptions. All data is stored within US borders. Tenant isolation is described as by design, with customer data logically separated between tenants and row level security applied to the most sensitive data, hosted on major cloud infrastructure with network and data segmentation. The company describes itself as headquartered in the US with US investors and leadership, and the agreement carries export control obligations and the federal commercial item clauses that government buyers need. No single tenant, private or on premises deployment is published, no cloud provider is named, and a buyer outside the United States has no region to choose.

No hosting provider is named, no region or residency commitment is published, and no single tenant, dedicated or on premises option is described. The company is headquartered in Singapore, names SoftBank and Tencent among its investors and serves customers including NASA, MIT and General Electric, and nothing published says where research material and unpublished technical data are processed. Stated GDPR and CCPA compliance does not say where data sits.

The vendor states it maintains SOC 2 Type II compliance, independently audited each year, with continuous controls monitored across availability, security and confidentiality. It points buyers to a trust center at a stable address where evidence of compliance is available. The portal shows SOC 2 marked compliant and lists more than fifty named controls across product, data, network, application, endpoint and corporate security. It also lists more than 38 named policy and procedure documents, including access control, asset management, acceptable usage, endpoint security and data protection, behind a button to request access. Annual penetration testing by independent firms is stated separately. No audit period, auditor or subprocessor list is published, and there is no way to confirm from outside that a request is fulfilled.

The trust center lists certification by the AICPA, ISO 27001:2022 with a statement that systems undergo annual external audits, TLS 1.3 and AES-256 encryption in transit and at rest, GDPR and CCPA compliance, and consent management. The SOC attestation is announced and described as SOC 2 Type 1, which assesses the design of controls at a single point in time; Type 2 tests whether controls operated effectively over a period. No auditing firm is named, and no certificate date is given for the ISO certification.

Cypris states that agents run on models from OpenAI, Anthropic and Google, describes the platform as model agnostic and multi model against competitors locked to one provider, and refers to enterprise API partnerships with all three. The agreement makes the arrangement concrete: AI credits are allocated annually by subscription value and each model consumes them at a rate reflecting its processing cost, so model choice is a priced variable. Customer material is not shared with third party model providers for training. Nothing is published at version level: no model identifiers, nothing on which model serves which agent, and no statement of where inference happens. Instead of a change commitment, the vendor reserves the right to adjust available models, credit allotments and costs per prompt at any time.

No foundation model provider, model family or version is named, and no subprocessor list is published. The vendor describes its architecture in technical terms, naming retrieval augmented generation and what it calls retrieval augmented thinking, and describes domain specific and domain trained models, which describe method rather than provenance. An independent product review notes an evolving AI backbone, so the underlying models change over time. The Open Platform exposes the agents built on those models to external systems through MCP servers.

No price is published anywhere. There is no pricing page in the navigation or the footer, and the only route is a demo request. The published agreement sets out the structure. Fees are set in an order form, seats are counted as named authorized users with a cap stated in that form, and the initial term runs at least a year and renews automatically unless either side gives forty five days' notice. Late payment carries 1.5 percent monthly interest. Three consumption units are defined. AI credits are allocated annually by subscription cost and consumed per model session, expiring at the end of the year. Research paper credits are valued one to one with US dollars for buying closed access papers. Research entitlements are capped at one report and two briefs a month. None of it is attached to a number.

Figures are published for the self serve plans. Eureka Pro is priced by monthly credits, at $100 a month for 10,000 credits and $200 a month for 20,000, with larger bundles above. The materials science Pro plan is listed at $200 a month and the life sciences Pro plan at $400 a month, each with a discount for yearly billing. Two self serve entry points exist: a 14 day free trial of Eureka with 10,000 credits and an API key from the Open Platform with usage based pricing. Enterprise plans and the Analytics Standard and Premium plans carry no figure, and those routes end in a demo or quote request. An independent analyst platform describes a named Enterprise plan that includes agents such as the Antibody Target Predictor and Markush Claim Drafting.

Materials and chemicals teams get intelligence at compound level linking patent filings, scientific literature and regulatory data, with prior art search, freedom to operate analysis, competitive monitoring and chemical structure mapping, and Oerlikon is named. Industrial and manufacturing teams get technology landscaping, patent analytics, supplier scouting and literature review, with Yamaha named. Life sciences and pharma teams get deep patent family analysis, citation tracking, clinical and scientific literature review and pipeline monitoring, with Johnson & Johnson named. Buyer types run from Fortune 100 companies to government agencies and national laboratories, and the agreement carries the federal commercial item clauses that public buyers require. No statement says where the platform does not reach, whether by right, jurisdiction or workflow.

Scale is stated as more than 2 billion structured data points and over 210 million patent, science and technology records, spanning patents, scientific literature, litigation records, clinical data and technical knowledge across 20 domains, so coverage extends beyond patents to other prior art. Industries are listed: agriculture and chemicals, consumer goods, food and drink, life sciences, automotive, oil and gas, professional services, aviation and aerospace, and education. Life sciences capabilities are named for sequences, Markush structures, structure activity relationship extraction, clinical trial comparison and druggability. Patent translation trained on global patent data supports work across jurisdictions. No update lag or refresh frequency is published, and no list of patent offices is given.