Patented AI is a narrow but credible IP-analysis platform aimed at patent owners, litigators, and investors who need to turn patent portfolios and source code into actionable licensing, diligence, or dispute-readiness work. The clearest jobs on the live March 2026 site are not generic ‘AI for patents’ claims: discover inventions in source code, assess patent portfolio value and industry applicability, identify likely licensees or infringers, run IP valuation for diligence, and generate source-code-to-claim evidence for infringement or invalidity arguments. The product fit is legal enough to keep because the workflows map directly into patent litigation, IP licensing strategy, and investment diligence rather than generic innovation scouting. The evidence base is still thin and mostly first-party. Release notes show real shipped capability in 2024, the company page names recognizable backers and IP-market advisors, and funding coverage exists, but meaningful G2/Capterra/Reddit practitioner signal did not surface. Public trust and procurement detail is also light: no public SOC 2, ISO 27001, or SSO proof was found, pricing is not published, and even outside descriptions of the company’s funding story are somewhat inconsistent about the current product narrative and total capital raised.
Company Info
- Founded: 2023
- Team size: 11-50 employees
- Funding: $4M
- HQ: United States
- Sector: IP
What We Haven’t Verified
This page was assembled from publicly available information. Feature claims and workflow mappings are based on what the vendor and third-party listings publish — not hands-on testing or practitioner feedback.
Workflows
Based on practitioner evidence, Patented AI is used in these workflows:
What practitioners struggle with
Real frustrations from legal professionals — the problems Patented AI addresses (or should address). Sourced from practitioner reviews, Reddit threads, and case studies.
Litigation team preparing a patent invalidity defence needs to find prior art that anticipates or renders obvious each claim element — manually building claim charts across dozens of references takes weeks and costs $50-100K in associate time, and missing one key reference could lose the case
In-house IP team asked to evaluate whether a new product infringes competitor patents — evidence-of-use analysis requires manually comparing product features against hundreds of patent claims, which takes weeks of attorney time at $500/hour and still produces incomplete results
Corporate IP lead or investor gets asked which patents in a 200-asset portfolio are actually worth maintaining, licensing, or relying on in a financing or acquisition, but prosecution history alone does not answer business value and the team cannot connect claims to markets, products, or likely licensees without months of bespoke analysis.
Where it fits in your workflow
Before Patented AI
A corporate IP team, outside patent litigator, or investor has a portfolio, codebase, or dispute target and needs to answer concrete questions fast: which patents are worth keeping, licensing, asserting, or stress-testing; which products likely read on the claims; and what evidence in source code or market data supports that answer.
After Patented AI
Patented AI's outputs feed licensing outreach, infringement or invalidity analysis, portfolio-pruning decisions, diligence memos, and outside-counsel strategy. The legal judgment still happens with the IP team, investors, or litigators using the analysis.
Integrations & hand-offs
Engineering or patent-owner inputs -> Patented AI portfolio / code / market analysis -> IP counsel, licensing team, or investor review -> diligence, monetization, litigation, or portfolio strategy decisions.
Also used by similar teams
Community Data
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