thenextweb
BlueConic joins Databricks Marketplace to bring real-time marketing decisions to the lakehouse

Enterprises have spent years and considerable fortunes building data lakehouses, training models, and unifying customer records inside platforms like Databricks. The harder problem, it turns out, is not building the intelligence but deploying it, getting a prediction out of a data warehouse and into a marketing decision before the moment passes. BlueConic, the Boston-based customer […]<br /> This story continues at The Next Web [...]

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venturebeat
Databricks' serverless database slashes app development from months to days as companies prep for agentic AI

Five years ago, Databricks coined the term 'data lakehouse' to describe a new type of data architecture that combines a data lake with a data warehouse. That term and data architecture are n [...]

Match Score: 199.17

venturebeat
Databricks set to accelerate agentic AI by up to 100x with ‘Mooncake’ technology — no ETL pipelines for analytics and AI

Many enterprises running PostgreSQL databases for their applications face the same expensive reality. When they need to analyze that operational data or feed it to AI models, they build ETL (Extract, [...]

Match Score: 156.17

venturebeat
Databricks: 'PDF parsing for agentic AI is still unsolved' — new tool replaces multi-service pipelines with single function

There is a lot of enterprise data trapped in PDF documents. To be sure, gen AI tools have been able to ingest and analyze PDFs, but accuracy, time and cost have been less than ideal. New technology fr [...]

Match Score: 140.10

venturebeat
Databricks' OfficeQA uncovers disconnect: AI agents ace abstract tests but stall at 45% on enterprise docs

There is no shortage of AI benchmarks in the market today, with popular options like Humanity's Last Exam (HLE), ARC-AGI-2 and GDPval, among numerous others.AI agents excel at solving abstract ma [...]

Match Score: 114.62

venturebeat
Databricks research reveals that building better AI judges isn't just a technical concern, it's a people problem

The intelligence of AI models isn't what's blocking enterprise deployments. It's the inability to define and measure quality in the first place.That's where AI judges are now playi [...]

Match Score: 101.87

venturebeat
The modern data stack was built for humans asking questions. Google just rebuilt its for agents taking action.

Enterprise data stacks were built for humans running scheduled queries. As AI agents increasingly act autonomously on behalf of businesses around the clock, that architecture is breaking down — and [...]

Match Score: 95.06

venturebeat
Databricks research shows multi-step agents consistently outperform single-turn RAG when answers span databases and documents

Data teams building AI agents keep running into the same failure mode. Questions that require joining structured data with unstructured content, sales figures alongside customer reviews or citation co [...]

Match Score: 94.43

venturebeat
Databricks' Instructed Retriever beats traditional RAG data retrieval by 70% — enterprise metadata was the missing link

A core element of any data retrieval operation is the use of a component known as a retriever. Its job is to retrieve the relevant content for a given query. In the AI era, retrievers have been used a [...]

Match Score: 84.93

venturebeat
Anthropic launches Claude Marketplace, giving enterprises access to Claude-powered tools from Replit, GitLab, Harvey and more

San Francisco startup Anthropic continues to ship new AI products and services at a blistering pace, despite a messy ongoing dispute with the U.S. Department of War.Today, the company announced Claude [...]

Match Score: 82.36