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Category: Computing & data (Page 2 of 97)

Computing and data is a broad category. Our coverage of computing is largely limited to software, and we are mostly focused on unstructured data, semi-structured data, or mixed data that includes structured data.

Topics include computing platforms, analytics, data science, data modeling, database technologies, machine learning / AI, Internet of Things (IoT), blockchain, augmented reality, bots, programming languages, natural language processing applications such as machine translation, and knowledge graphs.

Related categories: Semantic technologies, Web technologies & information standards, and Internet and platforms.

Dataiku launches 575 Lab, its new open source initiative for responsible AI

February 18, 2026 /

As AI moves from pilots to business-critical deployment, the issue is no longer access. It’s trust. Open source tools support that trust by keeping core components inspectable and standardizable, enabling stronger oversight across modern AI systems. Today, Dataiku announced the launch of the 575 Lab, Dataiku’s Open Source Office. The 575 Lab will release two new open-source toolkits designed to help enterprises make AI systems more transparent, governable, and fit for real-world use.

The 575 Lab will focus on delivering deployable tools that strengthen explainability, privacy, and governance across modern AI and agentic systems. The two initial open-source projects will be: 

  • Agent Explainability Tools that will help teams trace and understand decision-making across multi-step agent workflows, making agent decisions transparent for data scientists, compliance teams, and end users.
  • Privacy-Preserving Proxies that will enable safer use of closed-source models by protecting sensitive data end-to-end, and that teams will be able to run locally.

Both projects will be designed to support responsible enterprise AI, with a focus on reliability, security, transparency, and explainability.

The 575 Lab is now available to the community of AI specialists, data scientists, and developers responsible for creating, deploying, and scaling AI agents and applications.

https://www.dataiku.com/press-releases/dataiku-launches-575-lab/

Graphwise announced the immediate availability of GraphRAG

February 17, 2026 /

Graphwise announced the availability of Graphwise GraphRAG, a low-code AI-workflow engine designed to turn “Python prototypes” into production-grade systems instantly. It is based on a trusted semantic layer that reduces hallucinations and delivers precise and verifiable answers. GraphRAG unites LLMs, enterprise data, structured knowledge, and multiple search methods to deliver transparent, verifiable, enterprise-ready answers. Unlike standard RAG that “flattens” data into chunks leading to lost relationships and hallucinations, GraphRAG treats the knowledge graph as a trusted semantic backbone, ensuring AI responses are grounded in verifiable enterprise facts and complex relationships. Graphwise bridges the gap between complex enterprise data and functional AI agents. Features include:

  • Low-Code Visual Engine democratizes AI, enabling subject matter experts to adjust AI logic visually.
  • Out-of-the-Box Templates provide guardrails and support query expansion that deliver the fastest time-to-value.
  • Semantic Metadata Control Plane eliminates hallucinations and improves AI accuracy. AI responses are grounded in an organization’s “enterprise truth,” reducing risk.
  • Explainability and Provenance Panels support regulatory compliance. Built-in traceability affords transparency into how an AI response was produced.
  • Visual Debugging and Monitoring reduce maintenance costs by eliminating black box code.
  • SKOS-style Concept Enrichment harnesses domain-specific intelligence. This means AI understands company specific jargon, acronyms, and synonyms out-of-the-box.

https://graphwise.ai/news/new-graphrag-solution-moves-beyond-vector-only-rag-knowledge-graphs-provide-context-and-common-sense-to-ai

300-node clusters now supported in CockroachDB

February 12, 2026 /

From the CockroachDB Blog…

As AI-driven and agentic applications push data platforms into new territory, data architects are increasingly forced to choose between correctness, simplicity, and scale. To remove that tradeoff we’re announcing support for 300-node clusters with 2.2M tpmC and 1.2PB of data in CockroachDB v25.4.4 and beyond. Also, On CockroachDB Cloud, we’re announcing support for 64 vCPU per node. All customers will be able to self-serve and select these larger instance types if desired.

Highlights include:

  • ~610K QPS, which when compared to PUA on a 9-node cluster with 17K QPS shows that CockroachDB near linearly scales with the size of the cluster.
  • Compared to a previous run on 25.2, a run with the same amount of imported data on 25.4 took 30% less storage space than the previous run and enhanced compression.
  • Imports for this run on 25.4 were 2× faster compared to 25.1, for migrations to CockroachDB.
  • ADD COLUMN across 120 B rows completed without regression.
  • 330TB backup and 6 concurrent changefeeds completed in 2 hours and 40 min with no impact on foreground traffic.

Start with $400 in free credits. Or get a free 30-day trial of CockroachDB Enterprise on self-hosted environments.

https://www.cockroachlabs.com/blog/300-node-clusters-supported-cockroachdb

Snowflake makes enterprise data AI-ready with Snowflake Postgres

February 3, 2026 /

Snowflake, an AI Data Cloud company, announced advancements that make data AI-ready by design, allowing enterprises to rely on data that is continuously available, usable, and governed as AI transitions from experimentation into production systems. With new enhancements to Snowflake Postgres, the database now runs natively in the AI Data Cloud so enterprises can consolidate their transactional, analytical, and AI use cases onto a single, secure platform. To help ensure AI systems are trusted at enterprise scale, Snowflake is embedding enhanced interoperability, governance, and resilience features into its platform.

Powered by pg_lake, a set of PostgreSQL extensions that allow Postgres to easily work within an organization’s open and interoperable lakehouse grounded in Apache Iceberg, enterprises can leverage Snowflake Postgres to directly query, manage, and write to Apache Iceberg tables using standard SQL. This capability is delivered within a Postgres environment, so enterprises can eliminate data movement between transactional and analytical systems.

Enterprises need data that remains open, governed, and resilient as it flows across engines, formats, and environments. Snowflake is expanding how customers access, share, and govern their data. Open Format Data Sharing extends Snowflake’s zero-ETL sharing model to include formats such as Apache Iceberg and Delta Lake.

https://www.snowflake.com/en/news/press-releases/snowflake-makes-enterprise-data-ai-ready-with-snowflake-postgres-and-advanced-innovations-for-open-data-interoperability

Elastic adds high-precision multilingual reranking to new Elastic Inference Service

February 3, 2026 /

Elastic, a Search AI Company, made two Jina Rerankers available on Elastic Inference Service (EIS), a GPU-accelerated inference-as-a-service that makes it easy to run fast, high-quality inference without complex setup or hosting. These rerankers bring low-latency, high-precision multilingual reranking to the Elastic ecosystem.

Rerankers improve search quality by reordering results based on semantic relevance, helping surface the most accurate matches for a query. They improve relevance across aggregated, multi-query results, without reindexing or pipeline changes. This makes them valuable for hybrid search, RAG, and context-engineering workflows where better context boosts downstream accuracy. The two new Jina reranker models are optimized for different production needs:

Jina Reranker v2 (jina-reranker-v2-base-multilingual)
Built for scalable, agentic workflows.

  • Low-latency inference with strong multilingual performance.
  • Ability to select relevant SQL tables and external functions that best match user queries..
  • Scores documents independently to handle arbitrarily large candidate sets.

Jina Reranker v3 (jina-reranker-v3)
Optimized for high-precision shortlist reranking.

  • Optimized for low-latency inference and efficient deployment in production settings.
  • Strong multilingual performance; maintains stable top-k rankings under permutation.
  • Cost-efficient, cross-document reranking: v3 reranks up to 64 documents together in a single inference call, reasoning across the full candidate set to improve ordering when results are similar or overlapping.

https://ir.elastic.co/news/news-details/2026/Elastic-Adds-High-Precision-Multilingual-Reranking-to-Elastic-Inference-Service-with-Jina-Models/default.aspx

Upland announces BA Insight Platform with integrated AI search experiences for enterprises

February 3, 2026 /

Upland Software, Inc., a provider of AI-powered knowledge and content management software, announced the Upland BA Insight Platform. The new BA Insight Platform incorporates SmartHub, ConnectivityHub, AutoClassifier, Smart Preview, and Connectors to deliver search experiences that are more connected, more contextual, and more actionable. Features include:

  • Knowledge Graphs to deliver deeper, connected, and more contextualized insights by mapping relationships across complex datasets
  • Agentic Retrieval-Augmented Generation (RAG) to provide more accurate answers to complex questions through conversational AI interfaces
  • Amazon Q Business Integration to enable users to connect and perform generative actions against organizational content via the seamless AI-powered assistant

BA Insight introduces native integrations with Amazon Q Business and AWS generative AI assistant, enabling organizations to unlock conversational search and gain actionable insights across all content sources, securely. By working closely with AWS, BA Insight ensures customers benefit from seamless deployment, robust security, and continuous innovation, empowering organizations to maximize the value of their information and accelerate their AI journey. The unified BA Insight enterprise search and AI enablement platform is available in AWS Marketplace.

https://investor.uplandsoftware.com/news/news-details/2026/New-Upland-BA-Insight-Platform-Delivers-Integrated-AI-Search-Experiences-for-Enterprises/default.aspx

MongoDB expands MongoDB for startups

January 15, 2026 /

MongoDB, Inc. announced an upcoming expansion to MongoDB for Startups, designed to help founders and builders take applications from prototype to global deployment. MongoDB for Startups companies now represent more than $200 billion in combined valuation, and this expansion gives early-stage companies a faster, more reliable path to scale by providing a production-ready data foundation and an integrated stack that works from day one.

With initial launch partners Temporal and Fireworks AI, MongoDB for Startups introduces a founder-first ecosystem to help startups avoid early infrastructure decisions that slow them down over time. In the AI era, founders face unprecedented complexity when selecting their infrastructure; choosing the wrong stack early can create long-term AI debt that stalls innovation. Through a curated partner ecosystem, the program gives startups access to infrastructure designed to scale without constant rework, by delivering a cohesive, production-ready stack through matched credits, coordinated onboarding and enablement content, and joint events across complementary technologies.

This expansion to MongoDB for Startups creates a simple, opt-in experience designed to help founders scale without assembling and maintaining disparate technologies. Eligible MongoDB for Startups organizations can access matched credit offers across a curated set of complementary technologies, including Fireworks and Temporal.

https://www.mongodb.com/press/mongodb-for-startups-expands-to-give-founders-a-faster-and-smarter-start-from-day-one

Box announces general availability of Box Extract

January 15, 2026 /

Box, an Intelligent Content Management (ICM) platform, released Box Extract. Powered by generative AI models from companies like Google, Anthropic, and OpenAI, and combined with agentic capabilities, Box Extract enables enterprises to intelligently and securely pull valuable information from content and save it as metadata in Box. With Box Extract, it is now easier for enterprises to automate workflows, accelerate decision-making, and get faster access to information and insights.

Organizational knowledge resides in the collection of contracts, product specifications, policy documents, charts, and other forms of unstructured content involved in day-to-day business operations. This content provides the critical context AI models and agents require to unlock meaningful business value.

Box’s agentic approach enables Box Extract to understand document structure and meaning, break it down into components, such as paragraphs, tables, or charts, and then pull out the most important information. Teams can create custom Extract Agents tailored to their business needs. These Box Extract Agents give customers the flexibility to store structured data alongside unstructured content as custom metadata, which can also be exported or synced to other systems such as Databricks and Snowflake.

https://blog.box.com/introducing-box-extract-get-actionable-data-enterprise-content-scale

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