Workato has launched Workato GO, an AI super app designed to help employees search, act, and orchestrate work across all systems, apps, and data sources. Workato GO connects to all business tools, providing a single starting point for intelligent, secure, and scalable work. It combines three essential capabilities into a single experience: searching across every system and data source, receiving intelligent assistance tailored to each user, and taking secure, role-based actions. Workato GO is built on Workato ONE, the leading enterprise platform that powers integration, automation, and trusted agent execution. It enables users to complete workflows, kick off processes, and orchestrate results directly from a single interface. With GO, customers will have access to: Enterprise Search: Search across all your systems, data, and workflows. A single place to search across all your applications, documents, and processes, so employees can find exactly what they need, fast. Enables employees to instantly find information across all of their company’s documents, data, applications, and business processes from a single, unified interface. Employee Assistant: The personalized starting point for every employee. Combines intelligent assistance with full enterprise context, and the ability to take action without switching between tools. Deep Action™: Move from knowledge to execution. Take multi-step actions across multiple systems, trigger automations, and launch workflows—including human-in-the-loop processes—right from the search results or assistant. Agents at the Core: Flexible, open agent architecture that integrates Workato and third-party agents, enabling automation and orchestration across all systems. Extensibility: Easily extend and customize capabilities with recipes and agents—adapting Workato GO to your unique business needs.
TELUS Digital’s off-the-shelf STEM datasets including coding and reasoning data are curated by diverse pool of experts to offer enterprises access to high-quality, AI-ready data that has been cleaned, labeled and formatted
A new TELUS Digital survey of 1,000 U.S. adults found that 87% respondents (up from 75% in 2023) believe companies should be transparent about how they source data for GenAI models. Additionally, 65% believe that the exclusion of high-quality, verified content, such as information from trusted media sources (e.g. New York Times, Reuters, Bloomberg), can lead to inaccurate and/or biased large language model (LLM) responses. “As AI systems become more specialized and embedded in high-stakes use cases, the quality of the datasets used to optimize outputs is emerging as a key differentiator for enterprises between average performance and having the potential to drive real-world impacts,” said Amith Nair, Global VP and General Manager, Data & AI Solutions, TELUS Digital. “We’re well past the era where general crowdsourced or internet data can meet today’s enterprises’ more complex and specialized use cases. This is reflected in the shift in our clients’ requests from ‘wisdom of the crowd’ datasets to ‘wisdom of the experts’. Experts and industry professionals are helping curate such datasets to ensure they are technically sound, contextually relevant and responsibly built. In high-stakes domains like healthcare or finance, even a single mislabelled data point can distort model behavior in ways that are difficult to detect and costly to correct.“ In response to evolving industry dynamics, TELUS Digital Experience has launched 13 off-the-shelf STEM (science, technology, engineering and mathematics) datasets, including coding and reasoning data that is critical for LLM advancements. The datasets have been expertly-curated by a diverse pool of contributors, including Ph.D. researchers, professors, graduate students and working professionals from around the world. This gives enterprises access to high-quality data that has been cleaned, labeled and formatted for immediate integration into AI training workflows.
Treasure Data has released its MCP Server that allows AI assistants like Claude, GitHub Copilot Chat, and Windsurf to interact directly with intelligent CDP
Treasure Data, the Intelligent Customer Data Platform (CDP) built for enterprise scale and powered by AI, has released its MCP Server, a new open-source connector that allows AI assistants like Claude, GitHub Copilot Chat, and Windsurf to interact directly with your Treasure Data environment. Powered by the open Model Context Protocol (MCP), this solution gives data teams a new superpower: the ability to explore and analyze customer data in an easy and effective way, using plain language and a conversation window. With the Treasure Data MCP Server, teams can query parent segments and segments, explore tables, and analyze data using natural language, making data insights more accessible than ever. The MCP Server acts as a local bridge between your LLM-enabled tools and the Treasure Data platform. Once configured, it allows AI agents to securely interact with your CDP through structured tool calls. Instead of spending an hour writing multi-step SQL and debugging joins, the AI does it for you, writing, refining, and executing the query directly within Treasure Data. The MCP Server handles the permissions, safely limits results, and ensures your API keys and environment variables are managed securely. For most enterprises, the biggest barrier to using AI effectively isn’t the model, it’s the data. If an LLM can’t access high-quality, governed data, it can’t generate useful insights. The Treasure Data MCP Server removes that barrier. The AI accesses the CDP directly, securely and intelligently, so teams can finally start having productive conversations with their customer data.
‘Elliptic’s platform offers enterprises the ability to ingest data streams directly into internal data lakes, customized workflows and AI models via subscription enabling them to directly query data
Elliptic has announced an industry first, offering direct access to its market-leading datasets and intelligence, ‘Elliptic Data Fabric,’ via subscription. ‘Elliptic Data Fabric’ offers customers the ability to ingest and subscribe to data streams directly, enabling access to Elliptic’s data and intelligence in the format, schema, and delivery method that best meets their specific needs. Elliptic’s data and intelligence feeds the customer’s internal data lakes, customized workflows and AI models — accelerating decision-making, modernizing connectivity and letting enterprises and agencies directly query the data, run internal analytics and compose leaner data workflows. Elliptic Data Fabric has use cases for multiple industries. Elliptic Blocklist is a direct plug-in data and intelligence subscription service used by exchanges, stablecoin issuers, and payments providers. The Blocklist is regularly updated with the latest intelligence. This enables customers to directly query data to either permit or block withdrawals to unhosted wallets without adding friction to the transaction flow. Elliptic Counterparty Risk is being used by banks and financial institutions to help them easily assess indirect digital asset risk stemming from their customers by enriching their fiat transaction screening workflows with custom intelligence on thousands of VASPs. By seamlessly integrating Elliptic’s VASP data into internal screening workflows, organizations uncover hidden risks by detecting when customers interact with high-risk or unregistered crypto platforms. Government agencies are already leveraging Elliptic Data Fabric to access operation-ready blockchain data and intelligence, seamlessly integrated into their environments, mission-specific use cases, and analyst workflows.
Snowflake’s hybrid search capability combines semantic search and keyword search to do retrieval on unstructured data and orchestrates it amongst those two multiple data sets of text and structured data using AI agents
Snowflake introduced several platform updates designed to expand interoperability, improve performance and reduce operational cost. The focus of these enhancements was artificial intelligence and how Snowflake intends to help customers embrace the agentic revolution. A key focus for Snowflake has been providing tools for unstructured data. The company unveiled new additions in June to its Cortex portfolio, which expanded capabilities to query data across diverse formats, including unstructured image, audio or long-form text files, according to Christian Kleinerman, executive vice president of product at Snowflake. “Cortex Analyst is our ability to do text-to-structured data. Cortex Search is our hybrid search capability that does semantic search and keyword search to do retrieval on unstructured data and to orchestrate it amongst those two multiple data sets of … Cortex agents.” Snowflake is also seeking to improve the ease and speed of structured and unstructured data integration. The company announced Openflow, a managed service that’s designed to reduce the time and effort spent wrangling ingest pipelines while supporting batch and streaming workloads. Openflow can be implemented in multiple environments, according to Kleinerman. “Openflow has two deployment models. One is typical Snowflake; it’s Snowflake-managed resources. It’s in the cloud, but there’s also BYOC, bring your own cloud, which can be deployed in the customer’s virtual private cloud.” Snowflake’s role in the enterprise is evolving from providing data management tools to serving as a platform on which other businesses can be built. Kleinerman cited Capital One Financial Corp. as an example, which recently announced two new features, built on the Snowflake platform, for its enterprise B2B Capital One Software division.
Data governance platform Relyance AI allows organizations to precisely detect bias by examining not just the immediate dataset used to train a model, but by tracing the potential bias to its source
Relyance AI, a data governance platform provider that secured $32.1 million in Series B funding last October, is launching a new solution aimed at solving one of the most pressing challenges in enterprise AI adoption: understanding exactly how data moves through complex systems. The company’s new Data Journeys platform addresses a critical blind spot for organizations implementing AI — tracking not just where data resides, but how and why it’s being used across applications, cloud services, and third-party systems. Data Journeys provides comprehensive view, showing the complete data lifecycle from original collection through every transformation and use case. The system starts with code analysis rather than simply connecting to data repositories, giving it context about why data is being processed in specific ways. Data Journeys delivers value in four critical areas: First, compliance and risk management: The platform enables organizations to prove the integrity of their data practices when facing regulatory scrutiny. Second, precise bias detection: Rather than just examining the immediate dataset used to train a model, companies can trace potential bias to its source. Third, explainability and accountability: For high-stakes AI decisions like loan approvals or medical diagnoses, understanding the complete data provenance becomes essential. Finally, regulatory compliance: The platform provides a “mathematical proof point” that companies are using data appropriately, helping them navigate increasingly complex global regulations. Customers have seen 70-80% time savings in compliance documentation and evidence gathering.
Apache Airflow 3.0’s event-driven data orchestration makes real-time, multi-step inference process possible at scale across various enterprise use cases
Apache Airflow community is out with its biggest update in years, with the debut of the 3.0 release. Apache Airflow 3.0 addresses critical enterprise needs with an architectural redesign that could improve how organizations build and deploy data applications. Unlike previous versions, this release breaks away from a monolithic package, introducing a distributed client model that provides flexibility and security. This new architecture allows enterprises to: Execute tasks across multiple cloud environments; Implement granular security controls; Support diverse programming languages; and Enable true multi-cloud deployments. Airflow 3.0’s expanded language support is also interesting. While previous versions were primarily Python-centric, the new release natively supports multiple programming languages. Airflow 3.0 is set to support Python and Go with planned support for Java, TypeScript and Rust. This approach means data engineers can write tasks in their preferred programming language, reducing friction in workflow development and integration. Instead of running a data processing job every hour, Airflow now automatically starts the job when a specific data file is uploaded or when a particular message appears. This could include data loaded into an Amazon S3 cloud storage bucket or a streaming data message in Apache Kafka.
Datadog unifies observability across data and applications, combining AI with column-level lineage to detect, resolve and prevent data quality problems from occurring
Cloud security and application monitoring giant Datadog is looking to expand the scope of its data observability offerings after acquiring a startup called Metaplane. By adding Metaplane’s tools to its own suite, Datadog said, it will enable its users to identify and take instant action to remedy any data quality issues affecting their most critical business applications. Metaplane has built an end-to-end data observability platform that combines AI with column-level lineage to try and detect, resolve and also prevent data quality problems from occurring. It’s an important tool for any company that’s trying to make data-driven decisions, since “bad” data means those decisions are being made based on the wrong insights. This allows it to notify customers of any issues with the tools that are creating their data, such as Slack, PagerDuty and the like. Datadog Vice President Michael Whetten said, Metaplane’s offerings will help the company to unify observability across data and applications so its customers can “build reliable AI systems.” When the acquisition closes, Metaplane will continue to support its existing customers as a standalone product, though it will be rebranded as “Metaplane by Datadog.” Of course, Datadog will also look to integrate Metaplane’s capabilities within its own platform, and likely do its utmost to get Metaplane’s customers onboard.
Candescent and Ninth Wave’s integrated open data solution to facilitate secure, API-based, consumer-permissioned data sharing for banks and credit unions of all sizes and enable compliance to US CFPB Rule 1033
US digital banking platform Candescent has expanded its partnership with Ninth Wave to launch an integrated open data solution for banks and credit unions. The new offering is designed to facilitate secure, API-based, consumer-permissioned data sharing for banks and credit unions of all sizes. The development aims to support institutions in enhancing customer experience, operational efficiency, and regulatory compliance, including adherence to the US Consumer Financial Protection Bureau’s Rule 1033. The expanded collaboration seeks to replace traditional data-sharing practices—such as screen scraping and manual uploads—with modern, transparent alternatives. The new solution offers seamless integration with third-party applications used by both retail and business banking customers. Candescent chief product officer Gareth Gaston said: “With our integrated solution, banks and credit unions will be able to access Ninth Wave open data capabilities from within the Candescent digital banking platform. By adopting this model, financial institutions are expected to gain improved control over shared data, as well as stronger compliance with evolving regulatory standards. Ninth Wave founder and CEO George Anderson said “This partnership will allow financial institutions of all sizes to gain the operational efficiencies, reliability, and scalability of a single point of integration to open finance APIs and business applications.”
Reducto’s ingestion platform turns unstructured data that’s locked in complex documents into accurate LLM-ready inputs for AI pipelines
Reducto, the most accurate ingestion platform for unlocking unstructured data for AI pipelines, has raised a $24.5M series A round of funding led by Benchmark, alongside existing investors First Round Capital, BoxGroup and Y Combinator. “Reducto’s unique technology enables companies of all sizes to leverage LLMs across a variety of unstructured data, regardless of scale or complexity,” said Chetan Puttagunta, General Partner at Benchmark. “The team’s incredibly fast execution on product development further underscores their commitment to delivering state-of-the-art software to customers.” Reducto turns complex documents into accurate LLM-ready inputs, allowing AI teams to reliably use the vast data that’s locked in PDFs and spreadsheets. Ingestion is a core bottleneck for AI teams today because traditional approaches fail to extract and chunk unstructured data accurately. These input errors lead to inaccurate and hallucinated outputs, making LLM applications unreliable for many real-world use cases such as processing medical records and financial statements. In benchmark studies, Reducto has been proven to be significantly more accurate than legacy providers like AWS, Google and Microsoft – in some cases by a margin of 20+ percent, alongside significant processing speed improvements. This is critical for high-stakes, production AI use cases.