Amplified AI is a semantic patent search and knowledge management platform that lets IP practitioners find prior art by meaning rather than Boolean keyword combinations. Instead of constructing complex queries with CPC codes, users describe an invention in natural language and get AI-ranked results by conceptual relevance across 140M+ global patents. The AI relevance model improves with user feedback as results are marked relevant or irrelevant. Founded 2017 by Christopher Grainger and Samuel Davis. Bootstrapped, ~7 employees, ~$770K revenue (Latka). Listed on WIPO Inspire and Stanford CodeX TechIndex. Named among ‘Top 5 AI Patent Search Platforms’ by Lexology (Jul 2025) and Patseer. Cited in OECD report on AI developments (2020), SMU law review on USPTO/AI (2025), and academic papers on patent search (ACM, arXiv). Founder actively posts in r/patentlaw and r/Patents. Pricing: $500/month for teams with unlimited searches (Tekpon); enterprise $15K-$50K/year (PatSnap comparison). Self-service free trial, no credit card required. Vendor claims 5x faster search and up to 85% cost savings (not independently validated). Narrowly focused on patent search — no relevance to general litigation, corporate, family, or other practice areas.
Company Info
- Founded: 2016
- Team size: 1-10 employees
- Funding: $2.4M
- 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, Amplified AI is used in these workflows:
What practitioners struggle with
Real frustrations from legal professionals — the problems Amplified AI addresses (or should address). Sourced from practitioner reviews, Reddit threads, and case studies.
Patent attorney conducting a prior art search for a client's invention spends 2-3 days manually searching USPTO, EPO, and non-patent literature databases — reading hundreds of abstracts, mapping claims to prior art references, and still worrying they missed something in a Chinese or Japanese patent that wasn't translated. The search costs the client $5,000-15,000 and the attorney still can't guarantee completeness
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
R&D team submits invention disclosures into a black box — they never hear back about patent decisions, don't understand why some inventions get filed and others don't, and eventually stop submitting because the process feels pointless
Where it fits in your workflow
Community Data
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