AI Search

2 posts about AI Search. Every summary links to the original.

Databricks · May 14, 2026
From manual to autonomous: how AI agents are transforming electric grid operations
Electric utilities are facing rising demand, retiring generation, extreme weather, aging infrastructure, and fragmented operational data that manual processes cannot manage at scale. AI agents are presented as a human-centered alternative that synthesizes heterogeneous data, learns from outcomes, and progresses from human-approved recommendations to exception-based control and eventually autonomous operations within defined parameters. Hawaiian Electric used a Retrieval Augmented Generation proof-of-concept with Databricks AI Search, Unity Catalog, and Lakeflow Declarative Pipelines to query regulatory documents and provide page-specific citations. The system reduced response times from five minutes to five seconds and was implemented in two weeks, while the article describes broader potential for predictive maintenance, outage response, load forecasting, and customer service.
Julien Debard, Edward Tavares
Databricks · Apr 14, 2026
Building real-time product search on Databricks
Real-time product search for an online marketplace must retrieve, filter, rank, and respond in milliseconds while balancing latency, relevance, revenue, and click-through rate. The Databricks architecture divides the pipeline into ingestion, retrieval, and refinement, using Lakeflow pipelines and AI Functions for source processing, AI Search for embeddings and hybrid retrieval, and Lakebase for sub-10ms operational signals. It recommends validating p95/p99 latency, throughput, retrieval quality, and engagement metrics, with MLflow supporting systematic evaluation and caching helping meet strict latency requirements. As an example, FOX Sports used AI Search for thousands of QPS and achieved a 2x improvement in query success rate, combining streaming ingestion, two-phase retrieval, model serving, caching, and trending searches.
Jiayi Wu, Luke Lefebure, Adam Gurary

From manual to autonomous: how AI agents are transforming electric grid operations

Electric utilities are facing rising demand, retiring generation, extreme weather, aging infrastructure, and fragmented operational data that manual processes cannot manage at scale. AI agents are presented as a human-centered alternative that synthesizes heterogeneous data, learns from outcomes, and progresses from human-approved recommendations to exception-based control and eventually autonomous operations within defined parameters. Hawaiian Electric used a Retrieval Augmented Generation proof-of-concept with Databricks AI Search, Unity Catalog, and Lakeflow Declarative Pipelines to query regulatory documents and provide page-specific citations. The system reduced response times from five minutes to five seconds and was implemented in two weeks, while the article describes broader potential for predictive maintenance, outage response, load forecasting, and customer service.

Julien Debard, Edward Tavares

Building real-time product search on Databricks

Real-time product search for an online marketplace must retrieve, filter, rank, and respond in milliseconds while balancing latency, relevance, revenue, and click-through rate. The Databricks architecture divides the pipeline into ingestion, retrieval, and refinement, using Lakeflow pipelines and AI Functions for source processing, AI Search for embeddings and hybrid retrieval, and Lakebase for sub-10ms operational signals. It recommends validating p95/p99 latency, throughput, retrieval quality, and engagement metrics, with MLflow supporting systematic evaluation and caching helping meet strict latency requirements. As an example, FOX Sports used AI Search for thousands of QPS and achieved a 2x improvement in query success rate, combining streaming ingestion, two-phase retrieval, model serving, caching, and trending searches.

Jiayi Wu, Luke Lefebure, Adam Gurary