Open datasets age on Wikipedia while real questions live on the web. Training on their own web graph taught the model to reason, not loop.
An AI-native search platform building agents that answer complex research questions by searching and browsing the live web, with proprietary search infrastructure and a web graph index.
Training data for web search agents ages quickly: publicly available datasets lean heavily on Wikipedia rather than the live web, making them poor...
AI-powered search platform and API for LLMs and agentic workflows.
GPU cloud infrastructure for large-scale AI training and inference workloads.
You.com's Search model training is part of this use case:
Related implementations across industries and use cases
Researchers lost months debugging network failures. Bare-metal clusters ended the crashes and enabled custom orchestration.
Multi-step research ran one search at a time, burning tokens on extended reasoning. Astra takes fewer steps and fans work across sub-agents.
Isolating data for 50,000 clients was operationally impossible. Multi-tenancy now serves 6.1M queries from a single instance.
Researchers lost months debugging network failures. Bare-metal clusters ended the crashes and enabled custom orchestration.
Isolating data for 50,000 clients was operationally impossible. Multi-tenancy now serves 6.1M queries from a single instance.
On-chain AI agents took weeks of stitching and ran trapped on local machines. BNB Agent Studio ships them globally in 15 minutes.
Same work tracked in four disconnected tools. AI pulls live context from all of them—drafts land ready to sharpen, not start.
Code grew 7x faster than headcount; hundreds of teams ran identical migrations for months. Agents now author and auto-merge the bulk of PRs.
Tournaments running simultaneously meant an hour of manual checks each. AI agents now run them in minutes, freeing the team to be proactive.
Open datasets age on Wikipedia while real questions live on the web. Training on their own web graph taught the model to reason, not loop.
An AI-native search platform building agents that answer complex research questions by searching and browsing the live web, with proprietary search infrastructure and a web graph index.
Training data for web search agents ages quickly: publicly available datasets lean heavily on Wikipedia rather than the live web, making them poor...
AI-powered search platform and API for LLMs and agentic workflows.
GPU cloud infrastructure for large-scale AI training and inference workloads.
You.com's Search model training is part of this use case:
Related implementations across industries and use cases
Researchers lost months debugging network failures. Bare-metal clusters ended the crashes and enabled custom orchestration.
Multi-step research ran one search at a time, burning tokens on extended reasoning. Astra takes fewer steps and fans work across sub-agents.
Isolating data for 50,000 clients was operationally impossible. Multi-tenancy now serves 6.1M queries from a single instance.
Researchers lost months debugging network failures. Bare-metal clusters ended the crashes and enabled custom orchestration.
Isolating data for 50,000 clients was operationally impossible. Multi-tenancy now serves 6.1M queries from a single instance.
On-chain AI agents took weeks of stitching and ran trapped on local machines. BNB Agent Studio ships them globally in 15 minutes.
Same work tracked in four disconnected tools. AI pulls live context from all of them—drafts land ready to sharpen, not start.
Code grew 7x faster than headcount; hundreds of teams ran identical migrations for months. Agents now author and auto-merge the bulk of PRs.
Tournaments running simultaneously meant an hour of manual checks each. AI agents now run them in minutes, freeing the team to be proactive.