Search data kept in RAM required unsustainable hardware. A managed vector DB slashed costs and freed engineers to build AI agents.
A patent intelligence platform providing strategic insights on M&A and portfolio analysis for diverse industries, including the automotive sector.
The team initially built semantic search capabilities on a Lucene-based AWS service that required keeping all data in RAM to function. This setup was...
“We were trying to squeeze a vector database into what is not really a vector database, and it was super expensive and just not good. It was just the wrong technology, a complete overkill for the mathematical problem of approximate nearest neighbor search.”
AI-powered patent intelligence and monitoring platform for IP and R&D teams.
Sellers lost hours to manual research. AI agents now prioritize leads and draft briefs, cutting prep time by 80%.
Engineers manually correlated alerts across systems. AI agents now diagnose issues and suggest fixes, cutting recovery time by 35%.
Lab supply orders were handwritten in notebooks. Digital ordering now takes seconds, saving 30,000 hours for research annually.
Experts spent 15 minutes pulling data from scattered systems. Natural language prompts now generate detailed reports instantly.