A next-generation patent platform needs to do more than just store information—it needs to make that information work for you. The breakthrough that makes this possible is the combination of vector database technology and generative AI. We built Amplified on this foundation, using a language model powered vector database as the core engine powering search, classification, and visualization and seamlessly integrating with generative tools for analysis, information extraction, and data reporting.
Modern search needs demand flexibility. Patent professionals need both AI-powered semantic search for rapidly exploring new technology areas and precise Boolean search for defining legal scope. These approaches shouldn't exist in separate tools—they should work together, with the results of every search contributing to your organization's growing knowledge base.
Large language models act as tireless assistants, automatically extracting key information from patents and connecting it to your existing knowledge. Whether you're identifying technical problems, mapping innovations to your taxonomy, or analyzing competitive positioning, these models handle the heavy lifting while maintaining accuracy. The key to benefiting from this efficiency is processing the information at scale without requiring excessive manual checks, reformatting, or switching between tools.
Because all data lives in a unified space, visualizations and analysis become dynamic rather than static. Patent landscapes update in real time as you refine your search criteria or add new classifications. You can generate competitive analyses on demand, drawing on both public patent data and your organization's knowledge base. This transforms patent analysis from periodic projects into an ongoing process of discovery.
