NLPatent raises $3M to make patent work faster for product teams
Four years ago, IP lawyer and NLPatent co-founder and CEO Stephanie Curcio set out to fix a bottleneck most R&D and product leaders know well: slow, brittle patent research. Keyword search missed nuance. Teams lost days scanning noise. So she teamed up with CTO James Stonehill to build an AI-first way to surface what actually matters.
Today, NLPatent says more than 2,000 IP and R&D professionals at leading IP firms, Fortune 500s, and universities use its platform to make better, faster decisions. As Curcio puts it, "Our vision is for NLPatent to be the infrastructure, the operating system behind how patent work gets done in the AI era."
What's under the hood
NLPatent has evolved past simple search. The platform includes modules for searching, monitoring, and visualizing patent documents and trends, powered by proprietary, domain-tuned large language models. Teams use it to turn patent data into usable intelligence for IP strategy and R&D roadmaps.
In Curcio's words: "We've taken that concept of just surfacing relevant documents to the next level." Translation for product leaders: faster prior art checks, clearer design-around paths, and fewer late-stage surprises.
Fresh capital and the plan
The company secured $3 million USD in new funding, co-led by Draper Associates and Mighty Capital, with participation from The LegalTech Fund, Storytime Capital, and The51. The round closed in September as all-primary equity.
NLPatent plans to expand across North America and Europe and accelerate its roadmap-specifically, "fully agentic workflows that execute complex patent tasks." Earlier funding included $1 million CAD in October 2023 (led by Storytime) and $400,000 in SAFEs. A larger Series A is planned down the line.
The market is heating up
Legaltech funding has picked up alongside broader interest in AI. Curcio notes that patent professionals are no longer waiting on the sidelines-adoption is underway. Other entrants are moving too, with new tools focused on patent research and analytics gaining traction.
Curcio believes NLPatent's head start and reputation in the patent space will keep it competitive: "We went through a lot of pain in the early days to build something that delivers on the promise of what it's supposed to do."
Why product teams should care
Patents are an early-warning system for risk, opportunity, and positioning. If your process still leans on manual keyword searches and sporadic reviews, you're giving away time and confidence. AI-native patent research shifts that from reactive to proactive.
Practical plays you can run now
- Concept validation: During discovery or sprint zero, run natural-language prior art checks on problem statements and solution sketches-not just final specs.
- Design-arounds: Use similarity search to map potential claim conflicts, then generate design alternatives your team can prototype without delay.
- Competitor tracking: Set up monitoring on key assignees and inventors; route weekly changes to the PM channel your team actually reads.
- R&D portfolio planning: Visualize filing trends by category to spot whitespace and saturation before allocating engineering cycles.
- Go/No-Go gates: Add an IP check to your PRD template; require a high-recall search snapshot and summary before advancing.
- Documentation quality: Use LLM-generated summaries to keep claim language, architecture notes, and experiment logs consistent across teams.
Metrics that matter
- Time to confidence: Hours from question to a defendable IP position.
- Recall@Top-K: Percent of relevant documents captured in the first K results (track by domain).
- False-positive rate: How often results look relevant but aren't on review.
- Decision velocity: Days to Go/No-Go on features with IP risk.
Guardrails to set early
- Data provenance: Log sources and timestamps for every decision memo.
- Human-in-the-loop: Require expert review on high-stakes claims or filings.
- Privacy and export controls: Keep sensitive queries and drafts inside your approved environment; document vendor data policies.
- Repeatability: Save search prompts and filters as versioned templates so results are comparable quarter over quarter.
The bottom line
IP work isn't a back-office chore anymore-it's product risk management and market insight. Tools like NLPatent compress research cycles, reduce guesswork, and give product teams a clearer path to ship without rework.
The train has left the station. Whether you use NLPatent or another stack, build patent intelligence into your product loop now-before your next roadmap review turns into a rewrite.
Want to upskill your team on practical AI workflows? Explore role-based programs at Complete AI Training.
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