Legacy architecture couldn't scale complex parallel queries. Now, multi-agent AI autonomously reasons, plans, and outputs clean JSON.
A search API provider processing hundreds of complex queries daily is evolving its platform from simple information retrieval to long-running, autonomous research tasks.
The original system combined basic language model reasoning with search results without a dedicated coordination framework. As the architecture...
“The observability – understanding the token usage – that LangSmith provided was really important. It was also super easy to set up.”
AI-native search engine and API for developers and LLM applications.
Framework and developer platform for building LLM-powered applications.
Exa's Web research is part of this use case:
Related implementations across industries and use cases
Manual review of sensitive files took two days. AI agents now finish the work in one hour.
Travelers manually mapped out zigzagging AI itineraries. Now, modular AI agents automatically build practical, route-optimized trip plans.
Blind spots in agent chains delayed debugging. Engineers now use one-click tracing to instantly pinpoint and fix errors in production.
Messy ingredient data broke text search, capping matches at 20%. AI semantic search now maps free-form inputs to unlock recipe imports.
Fragmented vendor feeds obscured matching items. Now, AI pipelines parse millions of unstructured records into curated collections.
Sequential AI testing bottlenecked development. Engineers built a concurrent, code-first pipeline to evaluate agent responses in seconds.
Surging calls caused long holds and overtime. A 24/7 AI voice agent handles routine payroll, freeing 700 HR partners for advisory work.
A 200% yearly data expansion bottlenecked global operations. Now, AI accelerates coding, drafts recipe cards, and resolves inquiries.
Legacy keyword search failed on typos and vague queries. Now, semantic AI interprets natural language and images to find exact items.
Legacy architecture couldn't scale complex parallel queries. Now, multi-agent AI autonomously reasons, plans, and outputs clean JSON.
A search API provider processing hundreds of complex queries daily is evolving its platform from simple information retrieval to long-running, autonomous research tasks.
The original system combined basic language model reasoning with search results without a dedicated coordination framework. As the architecture...
“The observability – understanding the token usage – that LangSmith provided was really important. It was also super easy to set up.”
AI-native search engine and API for developers and LLM applications.
Framework and developer platform for building LLM-powered applications.
Exa's Web research is part of this use case:
Related implementations across industries and use cases
Manual review of sensitive files took two days. AI agents now finish the work in one hour.
Travelers manually mapped out zigzagging AI itineraries. Now, modular AI agents automatically build practical, route-optimized trip plans.
Blind spots in agent chains delayed debugging. Engineers now use one-click tracing to instantly pinpoint and fix errors in production.
Messy ingredient data broke text search, capping matches at 20%. AI semantic search now maps free-form inputs to unlock recipe imports.
Fragmented vendor feeds obscured matching items. Now, AI pipelines parse millions of unstructured records into curated collections.
Sequential AI testing bottlenecked development. Engineers built a concurrent, code-first pipeline to evaluate agent responses in seconds.
Surging calls caused long holds and overtime. A 24/7 AI voice agent handles routine payroll, freeing 700 HR partners for advisory work.
A 200% yearly data expansion bottlenecked global operations. Now, AI accelerates coding, drafts recipe cards, and resolves inquiries.
Legacy keyword search failed on typos and vague queries. Now, semantic AI interprets natural language and images to find exact items.