Patent law is one of the most knowledge-intensive professions in the world, but also one of the most operationally broken. Patent filings have grown steadily for decades, and modern inventions spanning AI, biotech, robotics, and semiconductors require increasingly cross-disciplinary analysis that no single expert or legacy software tool can handle alone. The professionals responsible for protecting those inventions, like patent attorneys, IP paralegals, and in-house counsel, spend the majority of their time on processes that are highly repetitive, expensive, and ripe for automation: prior-art searches, claim chart creation, office-action responses, and first-draft patent applications.
The system is overloaded, the tooling is outdated, and the cost of protecting innovation remains far too high. It’s exactly the kind of historically underserved sector that we at SignalFire love to back.
That’s exactly why we are excited to back Patlytics in its $40 million Series B. Patlytics is building the AI-native, end-to-end platform for patent professionals, starting with the hardest and highest-stakes workflows in IP and expanding across the full patent lifecycle.
A $50B+ industry still running on manual workflows
A single prosecution or litigation-related patent workflow can consume hundreds of hours of research, drafting, analysis, and back-and-forth across multiple patent offices, systems, and jurisdictions. For large law firms and sophisticated in-house teams, that creates serious throughput constraints. For smaller boutiques and solo practitioners, it can be the difference between scaling profitably and hitting a ceiling.
What makes this problem even more acute is the nature of the underlying data. Patent filings from the United States Patent and Trademark Office (USPTO), European Patent Office (EPO), World Intellectual Property Organization (WIPO), and national offices around the world form one of the largest semi-structured corpora of technical knowledge on the planet. But historically, that data has been incredibly difficult to search, synthesize, and operationalize at scale. Legacy software was built for keyword search and rudimentary databases, not for modern technical reasoning across multilingual, cross-jurisdictional invention data.
In our view, three structural shifts make this the right moment for a category-defining platform:
• Exploding patent volume and complexity. Global filings continue to rise, and modern inventions at the intersection of AI, biotech, and hardware demand cross-disciplinary prior-art analysis that strains traditional workflows. AI improves both the speed and completeness of that analysis, reducing missed citations and materially increasing patent quality.
• High-margin work that’s highly automatable. A huge share of patent work (60–80%) is spent on structurally repetitive tasks like prior art searching, claim chart creation, drafting, and office action responses. These are ideal targets for LLM-based automation, driving meaningful leverage on lawyer time and significant margin expansion for firms and in-house counsel alike.
• Proprietary data moats from global filings. AI platforms in this market can build real data moats by systematically ingesting patent filings across jurisdictions to materially improve search recall, claim drafting, and validity prediction in ways that generic AI simply cannot replicate.
Patlytics started with the hardest wedge in IP and kept expanding
One of the earliest things that stood out to us was that Patlytics did not start with the easiest problem. Most legal AI companies begin with lower-risk drafting assistance or narrow workflow tools. Patlytics started with one of the hardest, most consequential lanes in IP, patent infringement analysis, and prior-art search, and then kept going.
That wedge matters. The company's earliest product gave patent professionals a dramatically faster, more accurate way to analyze infringement: mapping claim charts against accused products, surfacing relevant prior art, and producing the kind of structured output that used to take days. That initial wedge was deliberate. Infringement and prior-art work are both high-stakes and highly labor-intensive, which meant that early customers across top-tier law firms and major corporate IP teams felt the ROI immediately and deeply.
From there, the company moved into drafting, and today it is building across the full patent lifecycle: invention disclosure, prior-art clearance, application drafting, claim strategy, office-action responses, and post-grant workflows. Instead of a single-purpose tool, Patlytics is building an integrated platform that shares context, data, and intelligence across every stage of the process. That means the system's value compounds as more of the workflow passes through it.
We think that is the right strategy for this category. Patent professionals do not need another isolated AI feature. They need a system of record and action for how IP work actually gets done.
Why Patlytics fits the SignalFire thesis
At SignalFire, we use our proprietary AI platform, Beacon, to track the world's most promising companies and talent. We spend our time looking for large markets that are operationally painful and historically underserved by great software. We get especially excited when AI is not just making an existing workflow a little faster, but re-architecting how the work gets done.
Patlytics surfaced at the precise intersection of forces we find most compelling:
- a massive, underserved professional workflow
- AI capabilities that are now genuinely sufficient to the task
- a data flywheel that becomes harder to replicate with every matter processed
This is a category where the customer pain is real, the ROI is immediate, and the underlying data becomes more valuable as the platform scales. That kind of flywheel is hard to fake and even harder to catch once it starts compounding.
We also care deeply about founder-market fit, and from our earliest conversations, it was obvious that Paul Lee and Arthur Jen are operating with unusual clarity and speed. They understand both the technical challenge and the practical reality of how patent professionals work. They are not building for a hypothetical future user. They are building for demanding experts who need trustworthy outputs, workflow depth, and measurable time savings. That conviction comes through in every product decision they make and every customer relationship they've built.
They also move extraordinarily fast. In the time it takes most teams to finalize a roadmap, their team is shipping, iterating, and incorporating customer feedback in real time. The pace at which they've expanded their product from infringement analysis to drafting to a full end-to-end platform is a direct reflection of that operating velocity.
Patlytics is already deeply embedded with leading law firms and sophisticated in-house IP departments, and the pull from customers is unmistakable. When the most technically exacting IP professionals in the world become your strongest advocates, you know you've built something that genuinely works.
The road ahead is much bigger than one workflow
The total addressable market (TAM) for IP management and legal technology is estimated to exceed $100 billion as of 2026. This figure encompasses the multi-billion-dollar global spend on patent services, specialized SaaS platforms, and the expansive in-house IP operations of Fortune 500 companies.
Patent prosecution, portfolio management, IP litigation support, and freedom-to-operate analysis are all in scope for a platform with Patlytics' ambitions.
We believe Patlytics has the potential to become the AI-native operating system for intellectual property.
With this new financing, Patlytics is well-positioned to deepen product coverage, expand across jurisdictions, and keep building the data infrastructure that turns every completed matter into a better starting point for the next one.
We are proud to partner with Paul, Arthur, and the entire Patlytics team as they build the definitive AI-native operating system for intellectual property. If you're a patent attorney, IP director, or in-house counsel curious about what this generation of AI can genuinely do for your practice, we'd encourage you to check out Patlytics.
SignalFire is built to help founders win in complex vertical markets
At SignalFire, we help founders build category-defining companies with our suite of portfolio support services, including our AI platform Beacon (which helps founders with GTM and talent intelligence), dedicated recruiting support, and hands-on operators across product, growth, and scaling. Our firm pairs category conviction with practical company-building support and a direct invitation to founders building in adjacent spaces.
If you’re building AI for legal tech or another complex professional workflow that legacy software has underserved for too long, we want to hear from you. We’re always looking to partner with founders rebuilding critical industries from the ground up. Reach out to YY at yy@signalfire.com or Aayush at aayush@signalfire.com.
*Portfolio company founders listed above have not received any compensation for this feedback and may or may not have invested in a SignalFire fund. These founders may or may not serve as Affiliate Advisors, Retained Advisors, or consultants to provide their expertise on a formal or ad hoc basis. They are not employed by SignalFire and do not provide investment advisory services to clients on behalf of SignalFire. Please refer to our disclosures page for additional disclosures.
Related posts
Mega AI is leveling the playing field for SMBs with its AI growth engine
The missing layer in Enterprise AI: Why we invested in Solid
Why we invested in Solace: Making healthcare advocacy a standard of care
Beyond the billable hour - How AI is reshaping margins and models at law firms
