Testing chunking strategies bottlenecked RAG deployment. A real-time sandbox now validates optimal settings instantly.
A developer platform enabling enterprises to transform unstructured data into optimized vector search indexes for retrieval-augmented generation applications.
Developers typically spent days or weeks manually testing different chunking strategies and embedding models to ensure accurate data retrieval. This...
AI platform for agent memory and context engineering for RAG applications.
LPU hardware and cloud platform for high-speed AI inference.
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Manual review of sensitive files took two days. AI agents now finish the work in one hour.
Massive models were too slow to scale. Moving to H100 inference cut latency by 50% and slashed costs by 4x.
Closed models lagged and broke flow. Self-hosting Llama cut latency 3x, letting a single GPU power 1,000 engineers.
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.