Curated for content, computing, and digital experience professionals

Category: Content technology news (Page 18 of 624)

Curated information technology news for content technology, computing, and digital experience professionals. News items are edited to remove hype, unhelpful jargon, iffy statements, and quotes, to create a short summary — mostly limited to 200 words — of the important facts with a link back to a useful source for more information. News items are published using the date of the original source here and in our weekly email newsletter.

We focus on product news, but also include selected company news such as mergers and acquisitions and meaningful partnerships. All news items are edited by one of our analysts under the NewsShark byline.  See our Editorial Policy.

Note that we also publish news on X/Twitter. Follow us  @gilbane

Kobai announces partnership with Databricks 

Kobai, a codeless knowledge graph platform, announced a new partnership with Databricks so joint customers can leverage the insights of knowledge graphs with the Databricks Lakehouse Platform. Kobai’s Saturn platform is embedded directly in the data layer, allowing organizations to query data without moving it from the lakehouse, with W3C and Lakehouse standards providing interoperability.

Semantic data, which imparts meaning and context to information, plays a pivotal role in optimizing various aspects of the manufacturing process. By integrating semantic data into factory operations, enterprises can enhance their production efficiency, quality control, and predictive maintenance. This technology enables machines, sensors, and devices to communicate in a more intelligent and coherent manner, facilitating real-time monitoring and analysis of production lines. Semantic data aids in the creation of interconnected systems that can adapt and self-optimize.

Kobai’s codeless platform provides a business-first approach and collaborative environment to rapidly share insights across the entire organization, transforming the way enterprises capture and create their business logic with a whiteboard experience. The Saturn knowledge graph works directly with Kobai’s Studio framework and Tower visualization products to infuse data with meaning and context, creating a semantic data fabric that grows over time.

https://www.kobai.io/articles/strategic-partnership-between-databricks-and-kobai

Merkle launches global composable commerce accelerator for Salesforce Commerce Cloud with Contentful and Magnolia 

Merkle announced the launch of its new global accelerator for Salesforce Commerce Cloud, which enables brands to achieve a modern composable, API-first architecture faster. Developed to work with Contentful and Magnolia, the accelerator extends Salesforce Commerce Cloud and streamlines integration with other enterprise content management platforms.

By joining forces with Contentful and Magnolia, Merkle enables businesses to implement enterprise-ready headless content management capabilities with Salesforce Commerce Cloud. The new accelerator drives improved time to market, a future-ready technology architecture, and greater innovation in front-end consumer experience. It allows brands to manage front-end site experience and web content through the content management platform, in addition to user experience functionality. This reduces the initial front-end development work for brands to implement a headless architecture.

The accelerator brings together Merkle’s Salesforce Commerce Cloud expertise and modern content management platforms Contentful and Magnolia, and is supported by Merkle’s global design system to expedite design and provide brand experience components. This gives businesses the tools to create and deliver exceptional digital commerce experiences while reducing cost and time to implement composable architectures.

https://www.merkle.com/en/merkle-now/press-releases/2023/merkle-launches-global-composable-commerce-accelerator-for-sales.html

InfluxData Announces InfluxDB Clustered

InfluxData, creator of the time series platform InfluxDB, announced InfluxDB Clustered, its self-managed time series database for on-premises or private cloud deployments. With the release of InfluxDB Clustered, InfluxData completes its commercial product line developed on InfluxDB 3.0, its rebuilt database engine optimized for real-time analytics with higher performance, unlimited cardinality, and SQL support. 

InfluxDB Clustered is the evolution of InfluxDB Enterprise, InfluxData’s enterprise software product for on-premises and private cloud environments. Now with the release of InfluxDB Clustered, those same customers gain all the capabilities of the reimagined InfluxDB 3.0, but now specifically packaged and configured for their own unique hosting environments and data storage requirements. Deployed natively in Kubernetes, InfluxDB Clustered combines the scale and flexibility of the cloud with the security and control of a self-managed infrastructure.

InfluxData also recently announced the availability of InfluxDB Cloud Dedicated, a fully managed and scalable single-tenant InfluxDB cluster based on the InfluxDB 3.0 architecture and intended for large-scale time series workloads. Together, InfluxDB Cloud Dedicated and InfluxDB Clustered give enterprises multiple options in how they manage and scale time series workloads, whether in the cloud, in their own environment, or in combination for hybrid environments. 

https://www.influxdata.com

DeltaXML improves CALS table handling to merge products

DeltaXML announced the release of new versions of their products XML Merge and DITA Merge, featuring improved handling of changes in CALS tables, as already available in XML Compare. It also adds the ability to ignore the order of columns, which means that a change in order doesn’t trigger delta markup in the result. Those used to seeing row duplications in their table results will be pleased to know that these have been dramatically reduced.

With XML Merge 11.0.0 and DITA Merge 7.0.0, this new table algorithm is included in merge products. This has added more complexity, but these products, as well as being easy to configure, produce the most rigorous and accurate comparisons. If some of the table versions have different structures, it’s necessary to select a ‘master’ table from which to create the result, which is used as the priority to determine which version to use as the master. The spans in that version are prioritised and other spans are created around them as necessary to ensure table validity. This approach helps to keep the result table as compact as possible while still representing how the table content has changed.

https://www.deltaxml.com

Duet AI for Google Workspace now available

From the Google Workspace Blog…

Today we’re making Duet AI for Google Workspace generally available, and you can get started now with a no-cost trial. … With the introduction of Duet AI, we added AI as a real-time collaborator… that can act as a coach, source of inspiration, and productivity booster — all while ensuring every user and organization has control over their data. 

… Instead of scrambling through forecasts in Sheets, P&L Docs, Monthly Business Review Slides, and reading emails from the regional sales leads, you’ll soon be able to simply ask Duet AI to do the heavy lifting with a prompt like “create a summary of Q3 performance.” Duet AI can create a whole new presentation, complete with text, charts, and images, based on your relevant content in Drive and Gmail. 

We’re putting Duet AI in Google Meet to help ensure you look and sound your best with studio look, studio lighting, and studio sound. … rolling out dynamic tiles and face detection … launching automatic translated captions for 18 languages; Meet will automatically detect when another language is spoken and display the translation in real time.

Duet AI can capture notes, action items, and video snippets in real time with the new “take notes for me” feature and it will send a summary to attendees after the meeting…

https://workspace.google.com/blog/product-announcements/duet-ai-in-workspace-now-available

Introducing ChatGPT Enterprise

From the OpenAI blog…

We’re launching ChatGPT Enterprise, which offers enterprise-grade security and privacy, unlimited higher-speed GPT-4 access, longer context windows for processing longer inputs, advanced data analysis capabilities, customization options, and much more. We believe AI can assist and elevate every aspect of our working lives and make teams more creative and productive. Today marks another step towards an AI assistant for work that helps with any task, is customized for your organization, and that protects your company data.

You own and control your business data in ChatGPT Enterprise. We do not train on your business data or conversations, and our models don’t learn from your usage. ChatGPT Enterprise is also SOC 2 compliant and all conversations are encrypted in transit and at rest. Our new admin console lets you manage team members easily and offers domain verification, SSO, and usage insights, allowing for large-scale deployment into enterprise…

ChatGPT Enterprise removes all usage caps, and performs up to two times faster. We include 32k context in Enterprise, allowing users to process four times longer inputs or files. ChatGPT Enterprise also provides unlimited access to advanced data analysis, previously known as Code Interpreter. This feature enables both technical and non-technical teams to analyze information in seconds, whether it’s for financial researchers crunching market data, marketers analyzing survey results, or data scientists debugging an ETL script. If you’re looking to tailor ChatGPT to your organization, you can use our new shared chat templates to collaborate and build common workflows. If you need to extend OpenAI into a fully custom solution for your org, our pricing includes free credits to use our API as well.

https://openai.com/blog/introducing-chatgpt-enterprise

Bloomreach announces Clarity

Bloomreach announced a conversational shopping product built for a new world of e-commerce. Using generative AI and large language models (LLMs), Bloomreach Clarity engages with shoppers to deliver personalized, product expertise straight from their favorite brands. Businesses connect these conversations directly to product catalogs, and can integrate individual conversations across channels, including website, chat, and SMS.

Bloomreach Clarity is built upon a real-time customer data engine and trained on more than a decade of commerce data. This gives it a understanding of how customers shop and how products perform globally, based on Bloomreach’s rich data and AI, and at the individual level, based on a business’s real-time data. While Clarity is showing customers relevant information and products, it’s also prioritizing what it knows they’ll actually buy. Clarity also offers control and optimizations for businesses as they introduce this conversational experience to customers, including:

  • The ability for merchandisers to combine their instinct and expertise with the speed and scale of AI
  • Real-time targeting that allows Clarity to prompt conversations based on a customer’s current journey or specific segment
  • Personalized conversations, even for unknown visitors based on browsing behavior
  • The ability to refine conversations using brand guidelines and tone of voice

https://www.bloomreach.com/en/products/clarity

Neo4j adds Vector Search within its Native Graph Database

Neo4j, a graph database and analytics company, announced that it has integrated native vector search as part of its core database capabilities. The result enables customers to achieve richer insights from semantic search and generative AI applications, and serve as long-term memory for LLMs, while reducing hallucinations.

Neo4j’s graph database can be used to create knowledge graphs, which capture and connect explicit relationships between entities, enabling AI systems to reason, infer, and retrieve relevant information effectively. The result ensures more accurate, explainable, and transparent outcomes for LLMs and other generative AI applications. By contrast, vector searches capture implicit patterns and relationships based on items with similar data characteristics, rather than exact matches, which are useful when searching for similar text or documents, making recommendations, and identifying other patterns.

This latest advancement follows from Neo4j’s recent product integration with Google Cloud’s generative AI features in Vertex AI in June, enabling users to transform unstructured data into knowledge graphs, which users can then query using natural language and ground their LLMs against factual set of patterns and criteria to prevent hallucinations.

https://neo4j.com/press-releases/neo4j-vector-search/

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