News

Amazon Web Services (AWS) and Microsoft have announced a new collaboration designed to simplify private, high-performance connectivity between AWS and Microsoft Azure.

The solution combines AWS Interconnect – multicloud with Azure Multicloud Interconnect, enabling enterprises to connect workloads across the two cloud platforms through a streamlined, cloud-native experience. The collaboration addresses a longstanding multicloud challenge: establishing secure connectivity without manually coordinating physical connections, routing configurations, monitoring tools and operational processes across providers.

Built on an open API specification for network interoperability, the solution allows organizations to provision resilient private connections while reducing the infrastructure complexity traditionally associated with multicloud networking.

Simplifying Enterprise Multicloud Operations

Many enterprises distribute applications, infrastructure and data across multiple clouds to access specialized services, improve resilience and avoid dependence on a single platform. However, managing connectivity between environments can require significant time, specialist expertise and ongoing oversight.

The new AWS–Azure connection is designed to abstract much of this complexity. Enterprises can establish private connectivity more quickly while gaining improved network predictability, scalability and operational consistency.

The solution also extends to Azure Private Link, supporting an end-to-end private path between the two cloud environments. At general availability, Microsoft expects customers to be able to deploy connectivity at speeds of up to 100 Gbps, supporting data-intensive applications and demanding AI workloads. Microsoft Azure

AWS Interconnect – multicloud with Microsoft Azure is currently available in public preview in four AWS regions: US East (Northern Virginia), US West (Northern California), Asia Pacific (Sydney) and Europe (Frankfurt). Connections can be created through the AWS Management Console, Command Line Interface or API. Amazon Web Services

What This Means for Prolifics Clients

As a trusted partner of both AWS and Microsoft, Prolifics helps enterprises turn advances like this into secure, scalable and business-aligned cloud architectures.

Prolifics can support organizations with multicloud strategy, workload assessment, network and integration architecture, application modernization, data-platform engineering, security, governance and FinOps. This combination of AWS and Azure expertise can help enterprises determine where workloads should run, how data should move and which controls are required to maintain performance, compliance and cost visibility.

The AWS–Microsoft collaboration represents an important step toward a more open cloud ecosystem—giving enterprises greater flexibility to use the capabilities of both platforms without allowing networking complexity to slow innovation.

Media Contact:  Chithra Sivaramakrishnan | +1(646) 362-3877 |  chithra.sivaramakrishnan@prolifics.com

Frequently Asked Questions

IBM has announced that the Dun & Bradstreet Commercial Graph™ is now available through a Model Context Protocol (MCP) server in the IBM watsonx Orchestrate Agent Catalog. The integration gives enterprises a more secure and standardised way to connect AI agents with verified commercial information and automate business-critical workflows.

As organisations move agentic AI from pilot projects into production, access to accurate and current information has become essential. Agents working with fragmented, incomplete or outdated data can generate unreliable results, particularly in areas such as credit assessment, procurement, regulatory compliance and supplier risk management.

The new integration addresses this challenge by connecting AI agents to Dun & Bradstreet’s trusted business identities and commercial context. Anchored by the DUNS® Number, the D&B Commercial Graph helps agents recognise the same verified organisation across multiple systems and records.

Connecting AI Agents Through MCP

Through the MCP server, organisations can enable agents to discover, retrieve and analyse Dun & Bradstreet’s commercial intelligence within watsonx Orchestrate.

MCP provides a standard, vendor-neutral method for connecting AI systems to external data and tools. It can reduce dependence on custom connectors that take considerable time to develop, secure and maintain.

IBM states that the D&B MCP server can be obtained directly from the Agent Catalog, potentially reducing agent-to-data integration timelines from months to days. Watsonx Orchestrate provides the central environment for building, monitoring, governing and scaling agents across cloud and on-premises systems.

Security and governance are built into the integration. Agents accessing the Commercial Graph operate within Dun & Bradstreet’s established data-quality, permission and audit frameworks, helping businesses align agent activity with their internal security and compliance policies.

Accelerating Adoption Through the IBM–Prolifics Partnership

As an established IBM Business Partner with recognised proficiency in watsonx Orchestrate, Prolifics can help organisations translate this new capability into production-ready business solutions.

Prolifics combines expertise in AI, data engineering, integration, automation and governance to help businesses assess suitable agentic AI use cases, connect watsonx Orchestrate with existing enterprise applications and establish the controls required for responsible deployment.

For example, Prolifics can help procurement teams design agents that monitor supplier stability, sales teams automate prospect research and risk professionals accelerate company verification. Its implementation and integration expertise can also help organisations embed D&B intelligence into end-to-end workflows rather than deploying AI agents as isolated tools.

Prolifics’ wider collaboration with IBM demonstrates its ability to turn IBM technologies into measurable business outcomes. In 2025, it received an IBM Partner Plus Award for North America in the Sustainability category for an AI-powered quality-control solution developed with IBM.

From Business Data to Confident Decisions

The D&B and IBM integration creates opportunities across sales, finance, procurement, compliance and operations. With implementation support from partners such as Prolifics, enterprises can build agents that securely apply trusted commercial context to daily decisions.

The announcement represents an important step towards practical enterprise agentic AI—combining authoritative data, open connectivity, centralised orchestration and experienced implementation support.

Media Contact:  Chithra Sivaramakrishnan | +1(646) 362-3877 |  chithra.sivaramakrishnan@prolifics.com

Frequently Asked Questions

IBM has introduced Granite 4.2, a new family of open large language models designed to meet growing enterprise demand for AI that can run across cloud, on-premises and edge environments.

Available in 3-billion, 8-billion and 30-billion parameter variants, Granite 4.2 combines native reasoning, coding, instruction-following and tool-calling capabilities. The models are intended to support agentic AI systems that can do more than generate responses, they can evaluate problems, plan actions, select tools and complete multi-step tasks.

Flexible AI for Different Enterprise Needs

Granite 4.2 arrives as interest in local LLMs continues to grow. Organizations increasingly want greater control over sensitive data, deployment architecture, operating costs and model customization. Locally deployable models can help businesses keep selected workloads within their own infrastructure while reducing dependence on external AI services.

The three model sizes allow organizations to align AI capacity with each use case. The compact 3B model is suited to edge devices and resource-constrained environments. The 8B model balances efficiency with general-purpose enterprise performance, while the 30B model targets complex reasoning, coding and specialized workflows.

All three models support a 128,000-token context window, while the 30B model can be extended to 512,000 tokens. They also offer full, low-effort and non-thinking modes, enabling developers to balance reasoning depth, response speed and computational cost for every interaction.

Built for Agentic AI

A central feature of Granite 4.2 is reasoning-augmented tool calling. Before invoking a tool or enterprise application, the model can reason about which resource it needs and how it should be used. This capability can support AI agents across software engineering, IT operations, research, customer service and process automation.

IBM says the models underwent a multi-stage training process incorporating supervised fine-tuning, reinforcement learning and agent-focused training. They were also trained on one trillion tokens of synthetic code generated through IBM’s CodeAlchemy pipeline. Speculative decoding is used to accelerate output and support more efficient inference.

Released under the Apache 2.0 license, Granite 4.2 can be downloaded, customized and deployed for commercial or academic use. The models are available through platforms including Hugging Face, Ollama, GitHub and IBM watsonx.

What Granite 4.2 Means for Enterprises

Granite 4.2 reflects a broader shift from AI experimentation toward governed, task-oriented enterprise systems. Its range of model sizes and flexible deployment options could help organizations build AI architectures that combine local processing with cloud scalability.

As an IBM Business Partner, Prolifics helps organizations assess, design and operationalize enterprise AI solutions using IBM technologies. From selecting the right models and integrating enterprise data to implementing governance and agentic workflows, Prolifics can help businesses move Granite-powered use cases from experimentation to measurable business outcomes.

Ready to explore secure, scalable enterprise AI with IBM Granite and watsonx? Connect with Prolifics to identify and accelerate your highest-value AI opportunities.

Media Contact:  Chithra Sivaramakrishnan | +1(646) 362-3877 |  chithra.sivaramakrishnan@prolifics.com

Frequently Asked Questions

Snowflake has announced dynamic model routing within Cortex AI Gateway and its flagship AI products, advancing its efforts to help enterprises control AI costs while improving model performance, governance and scalability.

The capability automatically selects an appropriate AI model for each request based on factors such as quality, speed, cost and customer preferences. Instead of relying on a single high-cost model for every workload, organizations can direct routine or repetitive tasks to more efficient models while reserving advanced frontier models for work requiring deeper reasoning.

The announcement builds on Cortex AI Gateway, which Snowflake introduced in July 2026 as a unified foundation for governing agent connections, routing AI requests and managing AI consumption. As enterprises move more AI applications and autonomous agents into production, managing an expanding range of models can create operational complexity and unpredictable costs. Dynamic routing is designed to handle much of that complexity automatically.

Bringing Intelligent Routing Across Snowflake AI

Dynamic model routing will be integrated into Snowflake CoCo and Snowflake CoWork and made available to third-party AI agents connected through Cortex AI Gateway. Organizations can control which models and providers their users may access, helping them accommodate regional availability, internal policies and industry-specific compliance requirements.

The gateway can also adapt its routing decisions as model capabilities, availability and pricing evolve. This means businesses can take advantage of new models without repeatedly redesigning applications or rebuilding agent infrastructure.

Snowflake’s internal evaluations indicate that this multi-model approach could produce meaningful efficiency improvements. In one test, agents using dynamic routing built a dbt pipeline with up to three times greater token efficiency than an approach relying exclusively on a frontier model while maintaining comparable quality. Another internal evaluation reported 25% greater token efficiency for engineering teams completing the same number of pull requests. Results may vary by workload and configuration.

Expanding Access to Open AI Models

Snowflake also plans to add DeepSeek-V4-Flash 0731 and GLM-5.3 to Cortex AI. These models will join offerings from providers including Anthropic, Google, Meta, Mistral and OpenAI.

Expanding the model portfolio gives enterprises greater flexibility to balance cost, performance and workload requirements. At the same time, organizations can continue applying Snowflake’s access controls and governance framework to their proprietary data and AI activities.

Administrators will also gain tools to monitor token consumption, allocate costs to teams or cost centres, establish user quotas and define spending limits. These controls are increasingly important as AI agents execute more tasks across enterprise environments.

The announcement reflects a broader shift in enterprise AI priorities—from rapid experimentation to measurable business value. By combining automated model selection, broader model choice and centralized cost governance, Snowflake aims to help organizations improve what it calls “intelligence efficiency”: the ability to convert models, computing resources, enterprise data and context into meaningful outcomes.

The new capabilities could make multi-model AI architectures easier to operate, but their ultimate value will depend on real-world workload performance, governance requirements and pricing. Dynamic model routing is expected to enter private preview, while availability for the newly announced open models will also be introduced through preview programmes.

Media Contact:  Chithra Sivaramakrishnan | +1(646) 362-3877 |  chithra.sivaramakrishnan@prolifics.com

Frequently Asked Questions

As enterprises move artificial intelligence from experimentation into production, strong governance, consistent data context, and secure access are becoming essential. Snowflake has announced new capabilities across its Horizon Catalog designed to help organisations govern, contextualise, and secure AI systems at scale.

The updates address several challenges that can undermine enterprise AI initiatives, including inconsistent data definitions, fragmented business logic, limited visibility, and concerns about how AI agents access sensitive information. By bringing governance, context, and security together, Snowflake aims to help businesses deploy trusted AI while maintaining compliance and control.

Creating Consistent Business Context with Horizon Context

A key addition is Horizon Context, a context layer that helps organisations maintain consistent business definitions and data interpretations across databases, data lakes, business intelligence tools, applications, and AI systems.

Without a shared semantic foundation, different teams and AI agents may interpret the same business metric differently. For example, “active customer,” “net revenue,” or “product availability” may have varying definitions across departments. These inconsistencies can lead to inaccurate analysis, unreliable AI outputs, and poor decision-making.

Horizon Context enables enterprises to create and maintain semantic views and shared business logic. This allows analytics tools, applications, and AI agents to work from a common understanding of organisational data. Snowflake is also supporting interoperability initiatives such as Open Semantic Interchange, helping enterprises extend consistent context across a broader technology ecosystem.

Expanding Security for Enterprise AI Agents

Snowflake is also introducing security capabilities designed for emerging AI environments. These include agent identity management, AI security posture monitoring, and centralised controls for governing how AI systems access enterprise data.

Agent identity management can help organisations identify individual AI agents, determine what information they can access, and apply appropriate permissions. Centralised governance also gives security teams greater visibility into AI activity, supporting stronger oversight and accountability.

These controls are particularly important as AI agents become capable of independently analysing data, initiating actions, and participating in operational workflows. Applying existing enterprise security principles to agents can help reduce unauthorised access, data exposure, and compliance risks.

Adaptive Compute Simplifies AI Workload Management

Alongside the Horizon Catalog enhancements, Snowflake introduced Adaptive Compute. The capability automatically adjusts compute and software resources in real time based on workload requirements.

This can reduce the need for teams to manually provision and optimise infrastructure for dynamic AI and application workloads. It also supports Snowflake’s broader vision for a serverless environment where performance, resource allocation, governance, and security are managed within a unified platform.

Turning Trusted Data into Trusted AI

For enterprises, scalable AI requires more than powerful models. It requires governed data, shared business meaning, secure access, continuous monitoring, and efficient infrastructure.

As a Snowflake partner, Prolifics helps organisations establish trusted data foundations, modernise analytics environments, strengthen governance, and operationalise AI use cases. By combining Snowflake’s evolving AI Data Cloud capabilities with Prolifics’ data engineering, governance, security, and AI expertise, enterprises can progress from isolated pilots to secure, business-ready AI solutions.

Snowflake’s latest updates signal a clear direction for enterprise AI: intelligence must be built on governed data, consistent context, and security by design.

Media Contact:  Chithra Sivaramakrishnan | +1(646) 362-3877 |  chithra.sivaramakrishnan@prolifics.com

Frequently Asked Questions

IBM has announced webMethods Integration Flow Pilot, an AI-assisted capability designed to help enterprise integration teams create, modernise, document and test webMethods Flow Services faster, without compromising governance or disrupting existing runtime investments.

Integration teams are under growing pressure to connect new applications, APIs, data sources, partners and automated workflows. At the same time, many face expanding backlogs, limited specialist capacity, inconsistent standards and outdated documentation. Generic AI coding tools can accelerate development, but business-critical integrations require stronger controls, greater transparency and enterprise-grade governance.

IBM webMethods Integration Flow Pilot addresses this challenge by bringing AI assistance directly into established integration development workflows. Developers can use natural-language instructions to create and refine Flow Services while retaining responsibility for review, validation and approval.

From Integration Intent to Governed Flow Services

A key innovation is Flow Script, a human-readable and version-control-friendly representation of webMethods Flow Services. It enables developers, architects and AI assistants to understand, review, compare and improve integration logic more easily.

Flow Pilot can also help teams:

• Generate and enhance Flow Service logic
• Create documentation and parameter descriptions
• Produce flow diagrams and unit-test scenarios
• Apply naming, logging and error-handling standards earlier
• Maintain greater consistency across integration projects
• Preserve compatibility with existing webMethods Integration runtimes

The capability supports IBM Bob, Claude and other enterprise-approved AI assistants. This flexibility allows organisations to adopt AI according to their security requirements, technology strategy and governance policies rather than being restricted to one AI environment.

Turning IBM Innovation into Enterprise Outcomes

As an experienced IBM partner, Prolifics helps enterprises translate platform innovation into secure, scalable and measurable business value. Its integration specialists support organisations across integration strategy, architecture, implementation, modernisation, API management, governance, quality engineering and managed services.

Together, IBM technology and Prolifics expertise can help organisations evaluate where Flow Pilot fits within their integration landscape, identify high-value use cases and establish appropriate development and governance practices. Prolifics can also support modernisation initiatives designed to reduce technical debt, improve maintainability and accelerate delivery while protecting mission-critical operations.

For enterprises with established webMethods environments, this provides a practical route to AI-assisted development without forcing a disruptive replacement of the runtime foundation that supports core processes.

IBM webMethods Integration Flow Pilot represents an important step towards faster, more intelligent integration delivery. With the right implementation strategy and expert guidance, organisations can move beyond experimentation and begin applying AI to integration development with greater confidence and control.

Ready to accelerate integration development with AI? Connect with Prolifics to assess your webMethods environment and build a practical modernisation roadmap.

Media Contact:  Chithra Sivaramakrishnan | +1(646) 362-3877 |  chithra.sivaramakrishnan@prolifics.com

Frequently Asked Questions

Microsoft has officially announced the general availability of Copilot Cowork, its latest AI-powered workplace assistant designed to move beyond simple chat interactions and actively execute business tasks across Microsoft 365 environments. This marks a significant leap toward autonomous workplace productivity, enabling organizations to automate complex, multi-step workflows while maintaining enterprise-grade security and governance.

What Is Microsoft Copilot Cowork?

Unlike traditional AI assistants that generate content or answer questions, Copilot Cowork functions as a digital teammate capable of handling tasks such as:

• Creating Word, Excel, and PowerPoint documents
• Scheduling meetings and managing calendars
• Sending Outlook emails
• Posting updates in Microsoft Teams
• Conducting research across organizational data
• Managing long-running workflows across multiple applications

The platform operates within Microsoft’s secure Microsoft 365 ecosystem and leverages enterprise permissions, governance controls, and compliance frameworks already in place.

Microsoft reports that more than half of Fortune 500 organizations have already experimented with Copilot Cowork during its preview phase, highlighting the growing demand for agentic AI solutions in the enterprise.

What Does It Cost?

Copilot Cowork requires a Microsoft 365 Copilot subscription and introduces a usage-based pricing model through Copilot Credits. Organizations are charged based on the complexity and volume of tasks executed, allowing businesses to scale AI adoption according to their needs. Microsoft also claims that Copilot Cowork can be 30-40% more cost-effective than comparable solutions in certain enterprise scenarios.

Why This Matters for Enterprises

The launch of Copilot Cowork signals a shift from AI-assisted productivity to AI-driven execution. Organizations that successfully integrate AI agents into their operations stand to gain:

• Faster decision-making
• Increased employee productivity
• Reduced manual workload
• Improved process automation
• Enhanced operational efficiency

However, achieving these outcomes requires more than simply deploying AI tools. Businesses must modernize their data infrastructure, establish governance frameworks, optimize integrations, and ensure secure AI adoption across the enterprise.

How Prolifics Helps Organizations Maximize Microsoft AI Investments

As a trusted digital transformation and AI solutions partner, Prolifics helps organizations unlock the full potential of Microsoft’s AI ecosystem through:

• Microsoft 365 Copilot readiness assessments
• AI governance and compliance frameworks
• Data modernization and integration services
• Intelligent automation strategies
• Enterprise AI implementation and optimization
• Cloud migration and modernization initiatives

Whether you’re evaluating Copilot, building AI-powered workflows, or scaling enterprise automation, Prolifics provides the expertise needed to accelerate adoption while minimizing risk.

The future of work is rapidly evolving from AI assistance to AI collaboration. Organizations that prepare today will be best positioned to lead tomorrow.

Media Contact:  Chithra Sivaramakrishnan | +1(646) 362-3877 |  chithra.sivaramakrishnan@prolifics.com

FAQs

New release helps organisations accelerate AI adoption, strengthen GRC workflows and simplify governance at scale.

IBM has introduced OpenPages 9.2.1, marking an important step in the evolution of AI-powered governance, risk and compliance (GRC). Announced on 23 July 2026, the release builds on the AI foundation established in OpenPages 9.2 and focuses on three enterprise priorities: accelerating AI adoption, improving everyday governance work and simplifying administration at scale.

As enterprises move artificial intelligence from experimentation into production, governance teams must oversee more documentation, complex risk relationships, regulatory changes and AI-driven processes. Traditional, labour-intensive approaches can create bottlenecks precisely when organisations need faster, better-informed decisions.

OpenPages 9.2.1 addresses this challenge with eight predefined AI model configurations covering common GRC use cases. These include issue similarity detection, audit-plan creation, Basel classification, vendor analysis, document summarisation, personally identifiable information detection and questionnaire-response generation. Organisations can activate and customise these capabilities without extensive coding or prompt engineering, helping shorten the path from AI exploration to operational use.

The release also advances the growing trend towards agentic AI in enterprise governance. A fully managed, cloud-native OpenPages Model Context Protocol server is now available to SaaS customers on AWS and IBM Cloud. Combined with expanded REST APIs, this infrastructure enables AI agents to interact securely with governed GRC data and use richer information about objects and relationships.

Another significant enhancement is multi-file AI analysis. Users can analyse policies, reports, evidence and supporting documents together within a single interaction. AI-assisted questionnaires can generate initial answers from existing information and uploaded documents, while retaining human review and approval. This combination of automation and oversight is critical for organisations seeking greater productivity without weakening accountability.

OpenPages 9.2.1 also introduces enhanced GRC Canvas capabilities, dashboard heatmaps, workflow-copy features, tag-based reporting and improved configuration packaging. Version comparison, dependency validation and packaging automation can help administrators move configurations between environments more confidently and reduce deployment risk.

Turning IBM Innovation into Business Outcomes with Prolifics

As a long-standing IBM Platinum Business Partner, IBM Partner Plus participant and original watsonx beta partner, Prolifics helps enterprises translate IBM innovation into secure, governed and scalable business solutions.

The IBM and Prolifics partnership combines IBM’s platforms across AI, automation, integration and hybrid cloud with Prolifics’ strategy, engineering, governance and implementation expertise. Prolifics can help organisations assess GRC maturity, design an OpenPages roadmap, integrate governed data and workflows, deploy AI capabilities responsibly and support continued optimisation.

For enterprises, the opportunity is clear: governance must evolve at the same speed as AI. IBM OpenPages 9.2.1 provides the technology foundation, while Prolifics helps turn that foundation into controlled, measurable enterprise value.

Media Contact:  Chithra Sivaramakrishnan | +1(646) 362-3877 |  chithra.sivaramakrishnan@prolifics.com

Ready to scale AI-powered governance with confidence? Connect with a Prolifics IBM expert.

Frequently Asked Questions

Microsoft has officially announced the general availability of Copilot Cowork, its latest AI-powered workplace assistant designed to move beyond simple chat interactions and actively execute business tasks across Microsoft 365 environments. This marks a significant leap toward autonomous workplace productivity, enabling organizations to automate complex, multi-step workflows while maintaining enterprise-grade security and governance.

What Is Microsoft Copilot Cowork?

Unlike traditional AI assistants that generate content or answer questions, Copilot Cowork functions as a digital teammate capable of handling tasks such as:

• Creating Word, Excel, and PowerPoint documents
• Scheduling meetings and managing calendars
• Sending Outlook emails
• Posting updates in Microsoft Teams
• Conducting research across organizational data
• Managing long-running workflows across multiple applications

The platform operates within Microsoft’s secure Microsoft 365 ecosystem and leverages enterprise permissions, governance controls, and compliance frameworks already in place.

Microsoft reports that more than half of Fortune 500 organizations have already experimented with Copilot Cowork during its preview phase, highlighting the growing demand for agentic AI solutions in the enterprise.

How Much Does Microsoft Copilot Cowork Cost?

Copilot Cowork requires a Microsoft 365 Copilot subscription and introduces a usage-based pricing model through Copilot Credits. Organizations are charged based on the complexity and volume of tasks executed, allowing businesses to scale AI adoption according to their needs. Microsoft also claims that Copilot Cowork can be 30-40% more cost-effective than comparable solutions in certain enterprise scenarios.

Why Microsoft Copilot Cowork Matters for Enterprise Productivity

The launch of Copilot Cowork signals a shift from AI-assisted productivity to AI-driven execution. Organizations that successfully integrate AI agents into their operations stand to gain:

• Faster decision-making
• Increased employee productivity
• Reduced manual workload
• Improved process automation
• Enhanced operational efficiency

However, achieving these outcomes requires more than simply deploying AI tools. Businesses must modernize their data infrastructure, establish governance frameworks, optimize integrations, and ensure secure AI adoption across the enterprise.

How Prolifics Helps Organizations Maximize Microsoft AI Investments

As a trusted digital transformation and AI solutions partner, Prolifics helps organizations unlock the full potential of Microsoft’s AI ecosystem through:

• Microsoft 365 Copilot readiness assessments
• AI governance and compliance frameworks
• Data modernization and integration services
• Intelligent automation strategies
• Enterprise AI implementation and optimization
• Cloud migration and modernization initiatives

Whether you’re evaluating Copilot, building AI-powered workflows, or scaling enterprise automation, Prolifics provides the expertise needed to accelerate adoption while minimizing risk.

The future of work is rapidly evolving from AI assistance to AI collaboration. Organizations that prepare today will be best positioned to lead tomorrow.

Media Contact:  Chithra Sivaramakrishnan | +1(646) 362-3877 |  chithra.sivaramakrishnan@prolifics.com

FAQs

Organizations today expect instant access to data for AI, business intelligence, customer applications, and operational decision-making. However, delivering real-time performance has traditionally required multiple serving databases, duplicated datasets, and complex data pipelines that increase both cost and operational overhead.

Databricks is changing that paradigm with the introduction of Lakehouse//RT, a next-generation real-time data warehouse that delivers millisecond query performance directly on unified lakehouse data. Powered by the new Reyden engine, Lakehouse//RT enables enterprises to support operational analytics, application serving, observability, and AI workloads without moving data into separate systems.

This innovation represents another significant milestone in Databricks’ vision of creating a single platform where analytics, AI, governance, and operational workloads coexist on one trusted source of data.

Why Traditional Real-Time Analytics Falls Short

Many organizations still rely on separate serving databases or specialized query engines to achieve low-latency analytics. While these architectures deliver fast response times, they often introduce additional complexity.

Common challenges include:

• Multiple copies of the same data
• Complex ETL and synchronization pipelines
• Increased infrastructure and licensing costs
• Governance inconsistencies across platforms
• Higher maintenance overhead for IT teams

These fragmented environments also make it difficult to scale AI applications that depend on fresh, trusted enterprise data.

Introducing Lakehouse//RT

Lakehouse//RT brings real-time responsiveness directly into the Databricks Lakehouse.

Instead of copying data into another database for application serving, organizations can query governed Delta Lake and Apache Iceberg tables directly with millisecond performance. The platform is designed to support high-concurrency workloads while maintaining a unified architecture across analytics, AI, and operational applications.

Key capabilities include:

• Millisecond query performance directly on lakehouse data
• High concurrency for thousands of users and AI agents
• No separate serving infrastructure
• Native integration with Unity Catalog governance
• Support for Delta Lake and Apache Iceberg open table formats
• Simplified architecture with reduced operational complexity

Powered by the Reyden Engine

At the heart of Lakehouse//RT is Reyden, Databricks’ new query engine purpose-built for real-time workloads.

Reyden is engineered to provide ultra-low latency while efficiently handling large numbers of concurrent requests. This makes it well suited for applications that require immediate responses, including:

• Customer-facing applications
• Interactive dashboards
• AI copilots
• Operational monitoring
• Fraud detection
• Real-time recommendation systems

By eliminating the need for separate serving layers, organizations can reduce infrastructure complexity while improving performance and scalability.

Unified Governance Remains Intact

One of the biggest advantages of Lakehouse//RT is that real-time workloads continue to operate under Unity Catalog.

This means organizations maintain:

• Centralized access control
• Consistent data governance
• Unified metadata management
• End-to-end lineage
• Enterprise security policies

Rather than managing governance separately across multiple systems, enterprises gain a single trusted platform for both analytical and operational workloads.

What This Means for Enterprise AI

As enterprises build more AI-powered applications, access to fresh operational data becomes increasingly important.

Lakehouse//RT enables organizations to power AI agents, intelligent applications, and real-time decision engines directly from governed enterprise data without introducing duplicate databases or synchronization delays.

The result is:

• Faster AI responses
• Lower infrastructure costs
• Simplified data architecture
• Better governance
• Improved scalability

For organizations pursuing modern data strategies, Lakehouse//RT represents a significant step toward unified analytics, AI, and operational intelligence on a single platform.

How Prolifics Helps Organizations Modernize Their Data Platforms

As enterprises continue modernizing their data estates, technology alone is not enough. Success depends on building an architecture that balances performance, governance, scalability, and AI readiness.

Prolifics helps organizations accelerate their data modernization journey by delivering:

• Modern lakehouse architecture assessments
• Databricks platform implementation and optimization
• Data engineering and migration services
• AI and analytics modernization
• Unity Catalog governance implementation
• Enterprise data strategy and modernization consulting

Whether organizations are modernizing legacy data warehouses or building AI-ready data platforms, Prolifics helps unlock the full value of the Databricks Data Intelligence Platform.

Media Contact:  Chithra Sivaramakrishnan | +1(646) 362-3877 |  chithra.sivaramakrishnan@prolifics.com

Frequently Asked Questions