As global enterprises undergo a massive generational shift toward generative AI, data engineering teams are facing a severe operational hurdle: the inability to prepare, govern, and pipeline vast proprietary data reserves quickly enough to sustain production environments. Resolving this delay requires a structural pivot toward bringing accelerated computing resources directly to the data source. DataPelago addresses this architecture gap by rethinking the boundary between storage and compute, embedding processing engines directly inside the data environment rather than forcing data migrations to external clusters.

“As AI models and the chips that power them get ever more effective, enterprises need data infrastructure that is just as intelligent and powerful to harness the potential of their data,” said George Kurian, Chief Executive Officer at NetApp. “NetApp is leading the industry in helping customers drive innovation and generate business value by giving them full command of their most important asset: their data. With DataPelago, we are extending our ability to help customers understand and process their data with the agility required to unleash competitive advantage.”

Also Read: Ninety Launches Ask Maz, an AI Assistant for EOS Teams

Eliminating Tool Sprawl and Infrastructure Costs via the Nucleus Engine

The technological foundation of the acquisition centers on Nucleus, DataPelago’s universal data processing software engine. The architecture leverages heterogeneous computing capabilities across both CPU and GPU hardware to analyze complex structures exactly where they reside. By optimizing operational pipelines without shifting files to external staging environments, the engine slashes underlying infrastructure expenditures by up to 80% while accelerating execution speeds up to ten times faster than legacy methodologies.

By removing the standard requirement to duplicate large data structures from transactional environments into secondary AI systems, NetApp eliminates the primary compliance and security friction point slowing down modern corporate machine learning rollouts. The technology is already demonstrating distinct commercial validity, stabilizing high-throughput workloads for multi-national corporations while maximizing hardware utilization across diverse environments.

“DataPelago is on a mission to eliminate the data processing bottlenecks that prevent AI innovation from reaching its full potential,” said Rajan Goyal, Founder and Chief Executive Officer of DataPelago. “Joining NetApp gives us the opportunity to combine our breakthrough processing technology with the industry’s best data infrastructure portfolio. Enterprises have invested billions in GPUs and AI models, but their data remains fragmented, leaving valuable computing resources to sit idle rather than putting these investments to work. Together, we’re positioned to help customers simplify and accelerate AI deployment at scale.”

Driving Zero-Copy Enterprise Data Activation

By collapsing fragmented operations into an optimized, software-defined framework, the combined entity provides enterprise engineering teams with a secure, highly auditable layout to govern raw datasets before model ingestion. This capability allows Chief Information Officers to extract rapid insights from dark data reserves without incurring steep egress fees or violating strict data sovereignty laws.

“DataPelago’s Nucleus engine brings software-defined acceleration directly to the storage layer, processing data across CPUs and GPUs so enterprises can prepare, govern, and activate their data for AI without moving it. This is true zero-copy activation,” said Syam Nair, Chief Product Officer at NetApp. “NetApp manages more enterprise data across more environments than anyone in the industry. The next phase of AI will be won by those who make that data work at the source, and the DataPelago team brings the technical depth and velocity to get us there faster.”

Following the closing of the transaction, DataPelago will maintain its operational momentum as a wholly owned subsidiary of NetApp. The integration marks a continuation of NetApp’s broader market expansion strategy, following recently finalized cloud and network ecosystem alliances with Cisco, Google Cloud, Red Hat, and SK Telecom.

The integrated GPU-accelerated infrastructure capabilities are actively being positioned for product rollout. Enterprise data architects, AI engineering directors, and cloud infrastructure operations leads can evaluate technical whitepapers, analyze performance benchmarks, and explore hybrid-cloud deployment models by visiting NetApp’s digital media and corporate technology hub.

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The inaugural Spotfire Quarterly Update highlights advancements across four areas that are increasingly critical to industrial organizations: AI-Augmented Investigation, Investigation at Scale, Trusted Industrial Decisions, and Industry-Native Analytics. Together, these innovations help engineers, scientists, analysts, and operational teams investigate complex systems more effectively, understand data in context, and make decisions with greater confidence.

As industrial organizations continue to navigate growing operational complexity, increasing data volumes, and rapidly evolving AI technologies, the ability to move from fragmented information to actionable understanding has become a competitive necessity. This quarter’s innovations are designed to address this challenge by bringing together AI, advanced analytics, scalable data architectures, and industry expertise within a single visual analytics environment.

Also Read: AtScale Announces Integration for Snowflake Semantic Views to Extend Governed Metrics to Power BI and Excel 

“Industrial organizations don’t need more dashboards or more disconnected tools; they need a better way to understand complex systems and make confident decisions,” said Tobias Lehtipalo, VP of Product Management at Spotfire. “The Spotfire Quarterly Update reflects how we’re evolving the platform to help customers investigate more effectively, validate findings with greater confidence, and operationalize analytics in ways that reflect the realities of industrial operations.”

Highlights from this quarter’s update include continued advancements in Spotfire AI, including new capabilities that help users navigate investigations, surface relevant insights, and accelerate analytical workflows. The update also introduces innovations that enable organizations to investigate larger and more complex datasets where they reside, reducing data movement while maintaining governance and performance.

Additional enhancements strengthen the analytical foundations required for trusted decision-making through expanded statistical methods, contextual analysis, and integrated workflows. Spotfire also continues to invest in industry-native experiences designed specifically for manufacturing, energy, and other operational environments where domain expertise is critical to decision-making.

“Industrial analytics is not about applying AI to data in isolation,” said Michael O’Connell, CAO of Spotfire. “It is about AI and analytics in operations, with deep domain expertise to understand what is happening, why, and what to do next.”

The Spotfire Quarterly Update serves as the foundation of a broader quarterly communication program that includes webinars, customer resources, product updates, and educational content designed to help organizations understand how innovation translates into real-world outcomes.

Source: BusinessWire

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Modern executive teams generate immense volumes of raw operational telemetry during standard corporate planning cycles. This data routinely spans multi-tier organizational structures, varying from quarterly performance milestones and weekly metric scorecards to unresolved systemic challenges, cross-department task logs, historical meeting notes, and high-level strategic playbooks. While this distributed documentation remains essential for day-to-day corporate alignment, drawing actionable operational insights frequently forces company leaders to navigate fragmented digital tools, execute manual cross-referencing loops, and synthesize missing context before establishing high-impact choices.

Dissolving Fragmented Reporting Loops via Centralized AI Analysis

The software update eliminates manual reporting overhead by providing a unified gateway to interrogate active business intelligence databases. Because the system resides natively inside Ninety’s existing business operating environment, it references real-time workplace configurations rather than generic external datasets.

Also Read: Xero Launches AI Industry Benchmarks for Small Businesses

The context-aware engine acts upon natural language queries to immediately surface critical organizational trends, allowing leaders to execute complex internal diagnostic checks on demand:

• Strategic Priority Tracking: Assessing whether specific active quarterly targets are structurally driving the organization’s overarching one-year corporate milestones.

• Operational Variance Detection: Instantly identifying off-track scorecard metrics across decentralized business divisions before they disrupt core deliverables.

• Predictive Risk Mitigation: Highlighting unresolved operational challenges and systemic vulnerabilities that require immediate evaluation prior to high-stakes quarterly planning sessions.

• Accountability Mapping: Reviewing functional ownership records within live corporate accountability structures to eliminate role confusion across the enterprise.

Turning Workspace Data into Immediate Operational Action

Unlike generalized external generative artificial intelligence platforms that require operators to manually clean, prepare, and upload confidential corporate background files, the system functions inside a secure data boundary. It references live core business tools—such as priority trackers, digital scorecards, issue repositories, open task lists, team news flashes, structured organizational charts, and long-range strategic plans—to provide contextual, company-specific answers.

When the system uncovers an underlying operational anomaly or a risk pattern during a user interaction, leaders can instantly initiate remedial workflows. Users can generate structured follow-up items, document operational issues, issue team updates, and log discussion points directly from the conversation interface, enabling complete operational follow-through without exiting their active digital workspace.

“Nobody comes to software because they want AI,” said TJ Kneale, Head of Data and AI Products at Ninety. “They come because they’re trying to build a great company. Ask Maz acts as a thought partner, helping leaders understand what’s happening, uncover risks and opportunities, and move from information to action faster.”

“Ask Maz helps leaders ask better questions, see what’s really happening inside their businesses, and make better decisions without spending hours assembling information from multiple places.”

The new conversational artificial intelligence functionalities are live and immediately available to organizations running on the Ninety operating system. Corporate executive officers, operational integrators, mid-market business owners, and enterprise organizational growth specialists can review system architecture blueprints, evaluate strict user access security configurations, and request a tailored platform workflow demonstration by visiting the company’s official digital workspace portal.

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Designed to fulfill the needs of evolving Capital Markets, AtlasInsight V5 represents a structural evolution in tracking and optimization functionality. The version incorporates an array of updated operational capabilities engineered to grant trading firms instantaneous metrics, granular structural transparency, and frictionless cross-environment management.

Precision Analytics at Scale via 200 Gb/s Packet Capture

Central to this latest version is the native integration of Capture 200, an innovative, high-performance capture framework capable of maintaining 200 Gb/s packet capture on standard commodity hardware. This addition allows financial institutions to continuously monitor and parse immense volumes of real-time network traffic with exceptional precision. The framework expansion guarantees that AtlasInsight keeps pace with increasingly data-heavy and sensitive capital market trading infrastructures.

Along with the hardware updates, the platform also features a redesigned GUI. The redesigned dashboard is characterized by rapid performance, clear paths for navigating through the data, and high level of granularity of the data that enables users to identify key metrics.

Also Read: Speridian Launches FinOps for AI to Optimize Enterprise AI Investments

Streamlined Workflows and Reduced Operational Friction

AtlasInsight V5 eliminates complex integration processes by simplifying initial setup and configuration protocols, letting firms deploy the system with nominal operational overhead.

To improve active diagnostic cycles, the platform adds pre-built troubleshooting workflows designed to guide network administrators through daily maintenance and unexpected systemic anomalies. Combined with advanced features like decode-on-demand and customizable visualization panels, operators can quickly isolate specific packet bottlenecks, track essential latency metrics, and configure analytics to match unique trading strategies.
Danny Moore, President and CEO at Options, said: “AtlasInsight V5 marks a major step forward in our commitment to delivering high-performance, intelligent infrastructure solutions for our clients. As trading environments continue to evolve and data volumes increase, our focus remains on equipping firms with the tools they need to gain deeper visibility, act faster, and operate with confidence at scale.”

Jon Axon, Founder of AtlasInsight said added: “With V5, we have focused on both performance and usability. The addition of decode on demand, combined with a significantly enhanced user interface and integrated workflows, enables our clients to troubleshoot faster and extract meaningful insights with greater ease. AtlasInsight continues to evolve as a powerful, user-driven platform designed for the demands of modern financial infrastructure.”

This product launch expands upon several notable milestones recently recorded by Options Technology. These developments include the selection of AtlasInsight by the Texas Stock Exchange (TXSE) for its next-generation packet capture and live data analytics, alongside the comprehensive global integration of the AtlasInsight suite across the entire worldwide network footprint managed by Options.

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In the past But the ability to access high-fidelity compare data has been a competitive advantage almost exclusively for huge companies with huge data science budgets. Small businesses and boutique operators have had to manage performance metrics based on broad macroeconomic trends or manual guesswork. Xero’s new framework addresses this imbalance by providing hyper-localized, industry-specific comparisons directly within the daily workflow of the business and its advisors.

“The ability to have a comprehensive view of your business performance compared to industry peers is a strategic advantage that’s historically been reserved for larger companies,” said Diya Jolly, Chief Product and Technology Officer at Xero. “Our Industry Benchmarking capabilities deliver actionable AI-generated insights that move a business beyond just knowing where they stand to feeling empowered to make clearer decisions backed by industry-specific data.”

Translating Real-Time Cash Signals into Clear Market Context

The platform’s underlying engine aggregates transactional histories across matching cohorts at the regional and country levels. This ensures that a boutique retail store or a plumbing contractor is evaluated strictly against identical sector structures rather than broad, unhelpful national averages.

Also Read: OpenArt Launches ‘Director’ for Conversation-Driven Long-Form AI Video Creation 

The tracking matrix evaluates small business health across nine essential performance dimensions:

Comparative Performance Scorecards: Uses predictive modeling to automatically analyze critical financial ratios, clearly labeling metrics as leading, lagging, or in line with regional peer groups.

Debtor Days Optimization: Delivers instant clarity on whether slow customer payments represent a unique internal operational bottleneck or a wider, systemic industry-wide cash trend.

Sales Revenue Metrics: Measures localized revenue expansion to help small businesses accurately evaluate if their scaling efforts are keeping pace with or outstripping the industry median.

Operating Efficiency Tracking: Examines cost-to-income ratios in real time, determining how effectively incoming expenses are translating into tangible bottom-line performance.

Empowering Advisories with the AI-Native Xero OS

Beyond providing static baseline scorecards, the system embeds an AI-powered prioritization engine to bridge the gap between financial tracking and operational action. Powered by Xero’s financial superagent, Just Ask Xero (JAX), the platform automatically prioritizes complex financial inputs and surfaces tailored steps business owners can take to mitigate risks or capture immediate market opportunities.

The release also functions as a strategic advisory launchpad for financial professionals. By establishing a standardized, objective baseline of peer-group metrics, the platform removes administrative guesswork from monthly consultations. This allows accountants and bookkeepers to quickly shift away from retrospective data entry toward proactive, data-driven strategy conversations with their clients.

The new Industry Benchmarks feature is natively integrated and currently rolling out globally within the Xero Analytics suite. Small business owners, boutique enterprise directors, certified public accountants, and independent bookkeepers can explore system integration parameters, check regional data availability schedules, and review operational training modules by visiting Xero’s official digital platform.

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The technological release targets increasingly complex, data-heavy modern corporate landscapes. As multinational client data expands exponentially across decoupled enterprise repositories, manual administrative processes can stall validation pipelines. The new connected agentic layer addresses this fragmentation by equipping Deloitte’s nearly 85,000 Audit & Assurance professionals worldwide with deep risk telemetry and automated information gathering directly inside their daily workflows.

“Our continued investments in AI and innovation are central to how we deliver quality and build trust in the capital markets,” said Dipti Gulati, chair and chief executive officer of Deloitte & Touche LLP. “As complexity increases, the combination of advanced technology and our professionals’ judgment and experience enables us to deliver confidence at scale.”

Also Read: LatentView Analytics Unveils BrickShift Migration Framework at Databricks Data + AI Summit

Orchestrating Collaborative AI Agents for Enhanced Evidence Analysis

Rather than relying on human prompt engineers to bridge different software programs, Omnia’s interconnected agents automatically pass operational context, data structures, and methodology constraints to one another. This multi-agent framework streamlines several high-intensity audit use cases:

Comprehensive Risk Factor Identification: Cross-references vast datasets simultaneously to highlight underlying anomalies and potential financial risk indicators early.

Context-Aware Decision Support: Generates real-time, situational insights and data mappings to augment the professional judgment of human audit teams.

Automated Preliminary Procedures: Speeds up execution by taking care of data extraction, evidence parsing, draft reporting, and building preliminary evaluation summaries.

Regulatory Compliance Verification: Reviews vast volumes of transactional files against rigorous disclosure requirements and shifting statutory frameworks.

“Our clients are operating in an environment defined by speed, complexity and constant change,” said Eric Johnson, U.S. Audit & Assurance chief strategy and transformation officer at Deloitte. “With Omnia we’re enabling our professionals to meet an ever-changing environment, enhance information gathering and validation and deliver a more connected, insight-driven experience across engagements.”

Embedding Strict System Governance via Trustworthy AI

Because financial reporting operates within strict regulatory environments, Deloitte’s agentic framework is built entirely in alignment with its proprietary Trustworthy AI framework. This architecture enforces strict security controls, transparent audit trails, and consistent compliance protocols across the entire technological lifecycle, ensuring that probabilistic AI generations remain securely bound to human-led validation gates.

“Omnia has evolved to become a unified agentic platform where our professionals, data, methodology and AI work together,” added Will Bible, U.S. Audit & Assurance digital products leader at Deloitte. “Our technology absorbs time-intensive workstreams, elevating critical thinking and analysis. This is only the beginning.”

The new connected agentic intelligence network is natively integrated into the Deloitte Omnia ecosystem and is currently scaling across active global engagements. Corporate financial directors, compliance officers, and enterprise risk management professionals can explore deployment frameworks, review data validation methodologies, and analyze platform governance structures by visiting Deloitte’s official digital technology portal.

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To bridge this performance gap, data teams were historically forced to copy data out of their primary repository and maintain a separate, highly expensive real-time serving database. This fragmented approach introduced severe vendor lock-in, data security risks, and complex data pipeline maintenance.

Eliminating this structural bottleneck, data and AI leader Databricks announced the launch of Lakehouse//RT.

Powered by a groundbreaking compute engine named Reyden, the platform delivers ultra-low millisecond query speeds directly on open table formats like Delta Lake and Apache Iceberg. This release completes the performance lifecycle for the Data Infrastructure, Cloud Analytics, and Enterprise Software industry, altering how modern organizations store, secure, and monetize enterprise intelligence.

Technical Integration: True Millisecond Execution on an Open Foundation

The structural breakthrough behind Lakehouse//RT is its ability to eliminate the data movement phase entirely. Rather than relying on extract, transform, load (ETL) routines or change data capture (CDC) pipelines to shift copies of data to separate storage, the Reyden engine queries data lakes directly where they reside.

The software architecture addresses heavy, complex concurrent workloads across three main vectors:

Massive Concurrency at Low Latency: The platform delivers sub-100 millisecond response times while processing up to 12,000 queries per second under heavy load, ensuring analytics dashboards remain responsive even with tens of thousands of simultaneous users or autonomous AI agents.

Complex Analytical Handling: Unlike legacy real-time acceleration stacks designed purely for simple data lookups, the engine executes deep multi-table joins, window functions, and complex aggregations without crashing or experiencing latency spikes.

Unified Governance via Unity Catalog: Every single query executes natively within Databricks’ existing Unity Catalog governance framework. This enforces consistent security policies, access controls, and data auditing logs without requiring a separate permissions management layer.

Also Read: CrowdStrike Targets Autonomous Risks with Continuous Identity Architecture for AI Agents

Transforming the Data Infrastructure and Analytics Market

The arrival of a native, ultra-low latency tier within the data lakehouse fundamentally resets the competitive dynamics of the enterprise software ecosystem.

The Obsolescence of the “Side-Stack” Serving Layer
For the past decade, specialized real-time database vendors carved out lucrative market share by pointing out the latency limitations of the cloud data lakehouse. Databricks’ deployment of Lakehouse//RT challenges the economic model of those specialized, external side-stacks.

When a single platform can natively handle data pipelines, AI modeling, business intelligence, and real-time app serving on an open format, the justification for purchasing and maintaining expensive, separate real-time serving platforms shrinks significantly. The market will increasingly favor complete, unified data platforms over fragmented point solutions.

Acceleration of the Agentic AI Runtime Stack
The timing of this release corresponds with the enterprise shift from simple conversational chatbots toward autonomous AI agents. AI agents operate by constantly reasoning in loops, calling external tools, and executing data checks behind the scenes. For an AI agent to take smart actions, it must query massive enterprise datasets in real time; a three-second latency delay completely stalls its execution flow. By providing a reliable millisecond speed layer, Databricks is effectively establishing the necessary plumbing for high-velocity, production-ready AI agents.

Overall Operational Impact on Businesses

For enterprise corporations navigating strict technology budgets and thin operating margins, consolidating real-time workloads onto a unified platform delivers clear business advantages.

Slashing Total Cost of Ownership and Architectural Debt
Maintaining separate databases for analytical storage and real-time serving creates massive data replication costs and burns significant engineering hours just to keep data synchronized. Early adopters of Lakehouse//RT have reported performance improvements of up to 16x compared to their specialized real-time tools, allowing them to completely dissolve their separate analytics side-stacks. This compression recovers massive amounts of capital and engineering capacity, liberating data teams to focus on revenue-generating applications rather than infrastructure plumbing.

Enforcing Single-Source Security and Governance
Data protection officers have a difficult time keeping track of compliance when corporate information is proprietary and constantly copied across independent database systems. One unsecured copy or unmonitored data synchronization is enough to put an organization at risk of serious data privacy breaches and regulatory fines.

By integrating real-time business processes in one controlled environment, it is possible to guarantee that security measures are consistently enforced across all the workloads. The members of a corporate board can be sure of creating and expanding automated data services, their foundational intellectual property being securely protected, thoroughly checked, and compliant with global standards.

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The software introduction builds directly upon LatentView’s established, multi-year strategic partnership with Databricks as a gold-tier integration partner. By standardizing extraction protocols, the migration toolkit allows companies to modernize their legacy analytical applications, preserve complex historical governance rules, and secure data lineage frameworks without manual, code-heavy reconstruction.

As global enterprises look to deploy scalable artificial intelligence across their commercial divisions, operational progress is frequently hindered by highly fragmented legacy platforms and steep data-governance hurdles. Traditional migration models regularly demand months of manual developer intervention to painstakingly trace data lineage and rebuild corporate dashboards from scratch. BrickShift directly resolves these bottlenecks by automating the end-to-end extraction, structural translation, and validation of legacy dashboards, turning them into native, optimized Databricks assets.

Maximizing Cloud Infrastructure Through Gold-Tier Specialization

The development of the specialized migration platform underscores LatentView’s continued investment within the Databricks data engineering ecosystem. As an active Databricks Gold Partner, the firm operates an extensive technical modernization practice backed by more than 500 professional cloud certifications and a shared portfolio of over 30 global enterprise clients. This engineering footprint is validated by four distinct Databricks specialization badges covering advanced AI implementation, comprehensive security and governance setups, cloud data warehouse migrations, and dedicated solution delivery across the retail, consumer packaged goods (CPG), and travel sectors.

“We see that many organizations want to use AI across commercial and marketing functions—from pricing and promotions to marketing effectiveness and supply chain operations—but their data isn’t ready for it,” said Sunil Kalra, Head Data Engineering Practice & Databricks CoE at LatentView. “That’s where we step in by migrating their data and BI to Databricks. As a Databricks Gold Partner, this is how we best support our clients. At Databricks AI Summit, we look forward to meeting with decision-makers, who can also get a preview of Genie Ignite, our AI/BI Modernization offering through a one-day workshop and a proof of concept on their own data, powered by Databricks Genie.”

Also Read: WisdomAI Unveils Analytics Agents to Turn Enterprise Insights into Autonomous Action

Showcasing a Broad Suite of Specialized AI Applications

Serving as a sponsor at the Explorer stage for the Data + AI Summit 2026, LatentView will be showcasing its live demos and architectural reviews at Booth #531. These include a series of highly-specialized AI solutions designed to enhance retail logistics, pricing operations, B2B customer engagement, and multi-channel marketing:

MigrateMate: AI-enabled data migration system aimed at automating schema mapping processes and reducing complications during transition of enterprise warehouses from Snowflake to Databricks.

Connected Business Planning: Integration of corporate planning processes ranging from early demand sensing upstream to procurement downstream, all the while ensuring target service levels are maintained and total holding costs are minimized.

Hubble: A dedicated pricing and promotion analytics ecosystem optimized for retail and CPG enterprises. The platform pairs customer behavior data with machine learning models to surface deep insights across price pack architecture (PPA), elasticity modeling, brand premiumization, and real-time market response.

RADius: An intelligent B2B account lifecycle engine that centralizes behavioral and transactional data into unified customer profiles. The software generates continuous client health scores and recommends automated, next-best actions to maximize account expansion and retention.

Synapse: A real-time agentic AI personalization engine designed to translate customer touchpoints into hyper-personalized user segments, serving immediate recommendations to lift active marketing conversion metrics.

Impact AI: An intelligent business intelligence platform built especially for corporate marketing departments, designed for querying campaign metrics (paid and owned) via natural language processing to maximize return on investment in advertising.

Smart Innovation: An innovative product development platform powered by AI/Machine Learning algorithms that analyzes external signals about consumers, markets, and behavior to identify new products and accelerate time-to-market.

AURA: A fully AI-powered retail media intelligence platform to assist brands with planning, monitoring, optimizing, and scaling their advertising investments through the retail media network.

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The XMLA Endpoint helps customers extend governed business definitions from Snowflake Semantic Views to Microsoft Power BI and Excel without mirroring data into another analytics environment, relying on stale extracts, or recreating metric logic outside Snowflake. An analyst can ask Snowflake CoWork or CoCo about gross margin and see the same answer in a Power BI dashboard or Excel pivot table, with data and compute remaining on Snowflake.

Snowflake Semantic Views provide a trusted semantic layer for Snowflake AI applications, including Snowflake CoWork and Cortex Agents, by defining consistent business metrics and dimensions. AtScale extends those same definitions to Power BI and Excel through live connections, giving AI agents, dashboards, and spreadsheets a shared semantic foundation.

“AI did not create the metrics consistency problem. It exposed it,” said Chris Lynch, CEO of AtScale. “For years, companies got away with different numbers in dashboards, spreadsheets, and reports because humans could reconcile the gaps. Agents won’t get the benefit of the doubt. XMLA Endpoint for Semantic Views, powered by AtScale, is a statement about where enterprise AI and analytics are headed: governed semantics in Snowflake, live access from the tools business users already trust, and data and compute in Snowflake. Customers own their semantics, choose their tools, choose their compute platform, and build for whatever comes next.”

Also Read: Silobreaker Introduces Agentic AI for Intelligence Workflows With Embedded Analysis and Built-in Governance 

For Snowflake customers, the embedded AtScale product is designed to:

• Bring governed Snowflake metrics to Microsoft analytics users through live access from Power BI and Excel.
• Use XMLA-based connectivity so Power BI and Excel users can query trusted Snowflake metrics without fragile extracts or disconnected semantic logic.
• Keep analytics workloads on Snowflake without mirroring data into another platform, creating duplicated extracts, or moving compute away from Snowflake.
• Deliver consistent answers across Cortex Agents, CoWork, dashboards, and spreadsheets by grounding each experience in the same governed definitions.

“Snowflake customers should not have to move or duplicate semantic context to give business users access to governed metrics in the tools they already use,” said Josh Klahr, Product Manager at Snowflake. “With Snowflake’s XMLA Endpoint for Snowflake Semantic Views, powered by AtScale, customers can extend trusted business definitions to Power BI and Excel while keeping Snowflake as the foundation for consistent AI and analytics.”

For enterprises with broader semantic requirements, AtScale Enterprise extends this foundation beyond the embedded Snowflake use case to support time intelligence, hierarchies, dimensional modeling, advanced business calculations, additional analytic tools, AI agents, and data platforms. This keeps Snowflake at the center while allowing trusted business logic to reach the AI agents, warehouses, analytic tools, and SaaS applications enterprises depend on.

Source: BusinessWire

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This release features the solution to one of the biggest pain points of data-driven enterprises: how to turn the insights into actual business success. Analytics Agents from WisdomAI don’t just show the analytics or give recommendations, but actually take the enterprise actions within the systems to directly marrying decision-making and operational execution.

With the support for more than 200 native integrations and Model Context Protocol (MCP) connectors for different data sources, the solution interacts with existing data environments of enterprises, thereby negating the need for expensive data migrations or complicated ETL processes. As a result, companies will be able not only to use their existing investments but also to open the doors to AI much more rapidly.

One important distinguishing factor is the use of WisdomAI’s Adaptive Context Engine for supplying the agents with organization-wide knowledge, business rules, and contextual information. As a result, data stays organized and uniform in all workflows, guaranteeing reliable and repeatable results from them. According to the company, this feature helps preserve data schema, format, and business context, improving accuracy in automated operations.

Also Read: Wolters Kluwer Unveils AI-Driven Enhancements to Streamline Lien and UCC Filing Processes

Another important distinguishing factor is the inclusion of enterprise-level features in the Analytics Agents, such as self-correction that allows agents to automatically detect and address issues with data consistency, deterministic results for generating consistent output regardless of multiple runs of an operation, and observability for monitoring, auditing, and analyzing an automated workflow.

To make agents easy to use, WisdomAI created an agent builder based on prompting that allows building a workflow using natural language descriptions. This system then creates all the required logic, connections, and other components automatically, allowing further adjustments through drag and drop interfaces.

“The gap between insight and action is where most analytics investments stall. Analytics Agents close it,” said Soham Mazumdar. “They reason across the data stack with enterprise context and turn analysis into outcomes automatically.”

Organizations including Trumid and PropertyFinder are already using the platform to enhance business intelligence and decision-making. According to Trumid, the technology has helped teams uncover actionable insights more quickly and deliver targeted intelligence to customer-facing employees, enabling faster and more informed engagement in dynamic market environments.

With Analytics Agents, WisdomAI aims to move enterprises beyond traditional business intelligence and toward fully autonomous, context-aware analytics operations.

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