Vector Search stories
Enterprise AI teams can now search internal files by meaning and restrict agent access at a finer level across workspaces.
It could cut the cost and delay of bringing AI agents to regulated data by letting firms use live SQL systems without migration.
The preview could let AI search systems handle far larger datasets with p95 latency of 51 milliseconds or less at more than 10 billion vectors.
Businesses will be able to search charts, tables and images in Box content using Gemini Multimodal Embeddings 2, reducing missed context in files.
Developers will be able to build AI agents on live production data without separate vector stores or synchronisation overhead.
Developers can now query live Atlas data from ChatGPT, Claude and coding agents, cutting setup work and reducing stale AI responses.
Search on WordPress sites could get sharper results as WP Engine widens its semantic tool beyond hosting customers, aiming to lift conversions.
Database teams will compare notes on sovereignty, resilience and compliance as the conference returns to Europe this year.
Customers can now build AI apps with live web data and vector search in one AWS stack, reducing database sprawl and latency.
Customer enquiries are now being answered up to 35% faster as Rinnai Australia uses Oracle AI to ease pressure on its call centre.
Customers can now keep document search and embedding work inside BigQuery, as Google rolls out AI search tools to cut extra pipelines and indexes.
Businesses can now keep AI retrieval and live app data in one place, as the service cuts data copying and extra sync pipelines.
Up to 4.16 times faster vector ingestion could help OpenSearch customers cut AI search latency and reduce storage overhead.
The retailer said the overhaul cut infrastructure maintenance by 50% and improved product recommendations, ratings and Gift Finder responses.
The ranking may strengthen Cloudera's pitch to regulated enterprises struggling to move AI pilots into production at scale.
Fashion retailers can now curb costly AI shopping errors as Algolia adds guardrails tied to live stock, prices and business rules.
The deal could let enterprises serve more AI models on fewer GPUs by shifting cache data into DRAM, SSDs and remote storage.
Throughput for AI vector searches can rise fourfold on AlloyDB, as Google's preview aims to cut latency without changing pgvector code.
Metric views and a broader knowledge graph aim to keep AI tools, dashboards and business users aligned on trusted data definitions.
Developers can now improve Chinese, Japanese and Korean search in AlloyDB without external tokenisers, cutting preprocessing overhead for PostgreSQL users.
