Investors spent hours combining hundreds of filters. Now, agents turn natural language into precise database queries.
A startup discovery platform aggregating millions of public and private data points to help venture capitalists track company formation and growth.
Users struggled with a complex interface that required combining filters across hundreds of fields to identify relevant companies. This manual...
“This UI is invaluable for debugging—instead of rerunning every node, we can directly inspect graph state at any point, make changes, re-run from that point, and observe the difference. Or open up that execution in Playground, with all the context from execution time already there so you can instantly experiment with different models or instructions.”
AI startup discovery engine for venture capital and sales teams.
Framework and developer platform for building LLM-powered applications.
Related implementations across industries and use cases
Typing friction limited query depth. Now, analysts speak complex requests to screen stocks and analyze filings in real time.
Teams spent days combing private data. Agents now reason across full documents to synthesize cited answers in seconds.
Bankers spent hours manually reviewing files. AI agents now scan 50M documents to generate presentation-ready analysis instantly.
Advisors hunted data across five systems. GenAI now builds traceable meeting packs, saving 4,700 hours a year.
35M customers waited in queues for payment updates. Now, a voice agent resolves routine calls instantly, leaving complex issues to humans.
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.