Similarity search mixed up Q3 and Q4 earnings. Hybrid AI now anchors retrieval with keywords and learns from human fixes instantly.
A US-based software provider enables enterprises to prepare and structure vast amounts of data for large language models and agentic AI workflows.
Data-heavy sectors like law and finance require absolute precision, but standard similarity search often confuses specific details, such as mixing up...
“Organizations often struggle to combine their data in a way that gives LLMs and AI agents the context needed for accurate, reliable results, especially when deploying retrieval augmented generation (RAG) models.”
AI platform for agent memory and context engineering for RAG applications.
Search AI platform for enterprise search, observability, and security solutions.
Related implementations across industries and use cases
Hallucinations capped accuracy at 75%. Hybrid search across 14 million chunks now delivers 90%+ accuracy.
Testing chunking strategies bottlenecked RAG deployment. A real-time sandbox now validates optimal settings instantly.
Engineers struggled to link scattered data. Now, an automated system connects sources instantly, fueling accurate autonomous agents.
Engineers manually correlated alerts across systems. AI agents now diagnose issues and suggest fixes, cutting recovery time by 35%.
Minor edits required days of crew coordination. Now, staff use avatars to modify dialogue and translate languages instantly.
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.