Palantir Technologies is pushing deeper into financial services with a new strategic partnership aimed at modernizing one of the industry’s most complex and paper-heavy domains: mortgage operations.

The company has teamed up with Moder to co-develop an AI-powered mortgage operations platform, with Freedom Mortgage serving as the first pilot customer. The initiative combines Palantir’s data and AI infrastructure with Moder’s domain expertise in mortgage servicing and operations.

The goal is ambitious: automate and orchestrate mortgage workflows end-to-end while improving speed, accuracy, and ultimately, borrower experience.

Turning Mortgage Complexity Into Structured AI Workflows

Mortgage operations are notoriously fragmented, governed by complex regulations, and dependent on manual processes.

The new platform addresses that by leveraging Palantir’s Ontology—a system designed to map data, workflows, and business logic into a unified operational model. In this case, it translates mortgage guidelines and policies into structured, testable, and auditable rules.

That foundation enables what Palantir describes as an “agentic AI framework,” where intelligent agents can execute tasks across systems while maintaining compliance and traceability.

In practice, that could mean faster loan processing, more consistent decision-making, and fewer operational bottlenecks.

Early Results Show Speed and Accuracy Gains

While still in early deployment, the platform is already live across several processes at Freedom Mortgage.

According to the companies, initial results include improved processing speed and accuracy—two metrics that directly impact both operational efficiency and customer satisfaction.

For mortgage servicers, even incremental improvements can translate into significant cost savings and faster turnaround times. For borrowers, it can mean quicker approvals and a smoother experience overall.

Why This Matters for the Mortgage Industry

The mortgage sector has lagged behind other industries in digital transformation, in part due to regulatory complexity and legacy infrastructure.

At the same time, pressure is mounting:

• Rising customer expectations for digital-first experiences

• Increasing compliance requirements

• Margin pressures pushing lenders to cut operational costs

AI-driven platforms like this one aim to address all three by automating repetitive processes while maintaining strict governance.

The potential upside goes beyond efficiency. By reducing operational costs, lenders may be able to offer more competitive rates or expand access to homeownership—an outcome both companies emphasize.

Palantir’s Expanding AI Footprint

For Palantir, the partnership is another example of its strategy to embed AI into core industry workflows.

The company has increasingly positioned its platforms as operational systems—not just analytics tools—capable of running mission-critical processes across sectors like defense, healthcare, and finance.

By applying its Ontology framework to mortgages, Palantir is extending that model into a highly regulated, high-volume industry where data fragmentation has long been a barrier to innovation.

How It Stacks Up

The mortgage tech space is crowded, with platforms like ICE Mortgage Technology and Blend Labs focusing on digitizing loan origination and servicing.

What differentiates the Palantir-Moder approach is its emphasis on orchestration and agentic AI—automating not just interfaces, but the underlying decision-making processes.

If successful, it could shift the industry from digitized workflows to truly autonomous operations.

The Bigger Picture: AI Meets Regulated Workflows

One of the more interesting aspects of this partnership is how it handles compliance.

By encoding policies into auditable rules within the platform, the system aims to ensure that AI-driven decisions remain transparent and traceable—a critical requirement in financial services.

That approach could serve as a model for other regulated industries exploring AI adoption, where explainability and governance are non-negotiable.

The Bottom Line

Palantir and Moder’s partnership signals a deeper push toward AI-driven operations in the mortgage industry.

By combining structured data modeling with agentic AI, the platform aims to streamline complex workflows while maintaining compliance—a balance that has historically been difficult to achieve.

If the early results at Freedom Mortgage scale, the initiative could mark a meaningful step toward a more efficient, accessible, and automated future for home lending.

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Reply and Mistral AI are joining forces to accelerate enterprise adoption of secure, locally deployed, and fully customizable generative AI solutions. The partnership aims to help organizations in regulated sectors harness AI while ensuring data control, privacy, and compliance.

The collaboration leverages Mistral AI’s high-performance models alongside Reply’s expertise in designing Large Language Models (LLMs) trained on proprietary and domain-specific datasets, allowing AI deployments to integrate seamlessly into operational workflows across industries like finance, healthcare, public administration, defence, telecommunications, and energy & utilities.

Custom AI for Regulated Industries

A central goal of the partnership is enabling organizations to deploy generative AI solutions that meet stringent regulatory and operational requirements. By combining model performance with operational governance, organizations can:

• Maintain strict control over sensitive data

• Ensure compliance with local and European regulations

• Deploy AI solutions on sovereign infrastructures

Filippo Rizzante, CTO of Reply, emphasized that the initiative allows enterprises to scale AI deployments while keeping governance, data sovereignty, and security front and center.

Mistral Forge: Tailored Models for Operational Use

Reply will act as a global launch partner for Mistral Forge, enabling the creation of custom LLMs for complex, data-intensive domains. The platform allows teams to design, train, and deploy models on proprietary datasets—turning generic AI into enterprise-grade tools that are both specialized and operationally ready.

This level of customization is particularly important in sectors where standard AI models may not capture domain-specific knowledge or regulatory nuances, such as financial compliance or industrial operations.

Highlight Project: Ancient Greek LLM

The partnership’s capabilities are already being demonstrated through a collaboration with the Austrian Academy of Sciences. Reply and Mistral AI are developing a customized LLM for the Greek language, spanning ancient, medieval, and modern texts.

The model is trained on a highly curated corpus, including:

• Published ancient Greek literature

• Digitized inscriptions and papyri

• Selected modern Greek texts from scholarly and public sources

Designed to assist researchers, it provides advanced text search, completion, and analysis capabilities. This initiative highlights how sovereign AI infrastructure can support highly specialized, data-intensive use cases while maintaining accuracy and reliability.

Why It Matters

Generative AI adoption in enterprise and research environments is often constrained by regulatory, privacy, and operational risks. By combining sovereign infrastructure, model customization, and domain expertise, the Reply–Mistral AI partnership addresses three of the biggest adoption barriers:

1. Control: Organizations retain ownership and oversight of proprietary data and AI models.

2. Compliance: Models operate in alignment with strict privacy and regulatory requirements.

3. Performance: Custom-tailored LLMs deliver relevant outputs for specialized tasks and operational processes.

Marjorie Janievicz, Chief Revenue Officer at Mistral AI, noted that the collaboration will help organizations deploy AI solutions that meet enterprise expectations for control, customization, and performance.

The Bigger Picture

This partnership reflects a growing trend toward “sovereign AI”—locally deployed, regulated, and fully customizable models that allow organizations to unlock AI capabilities without sacrificing compliance or data protection.

By integrating Mistral AI’s high-performance models with Reply’s expertise in domain-specific customization, organizations gain a scalable path to deploy generative AI in operational environments, research settings, or highly regulated industries.

The Bottom Line

Reply and Mistral AI are demonstrating how enterprise-grade generative AI can be both high-performance and compliant. From mortgage and healthcare operations to specialized research like ancient Greek texts, the partnership shows that secure, customized, and scalable AI deployments are now achievable—without compromising data sovereignty or governance.

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Senior marketing leaders overwhelmingly believe that marketing drives growth—but when it comes to explaining how marketing is measured and defending budget decisions in the boardroom, confidence falters. That’s the key takeaway from Haus’ inaugural Decision Confidence Index, which surveyed senior marketing and finance executives on their ability to measure, defend, and optimize marketing investments.

According to the report, only 49% of leaders can clearly articulate their measurement approach to the board. While dashboards, AI tools, and data analytics are abundant, many organizations lack the clarity required for high-stakes decisions, leaving financial accountability unclear and long-term growth strategies vulnerable.

“Massive marketing budgets are being allocated based on methods that leaders themselves don’t fully trust,” said Zach Epstein, CEO of Haus. “That uncertainty drives wasted spend and cautious, short-term decisions that can limit long-term growth.”

Confidence Drops as Stakes Rise

While 90% of respondents believe marketing drives growth, 35% admit that over 20% of their budget is inefficiently allocated. Confidence erodes further in scenarios with real financial implications:

• Only 49% say they measure what truly drives growth and business outcomes

• 51% admit they focus on metrics expected by leadership or easily accessible, rather than strategic impact

• More than 20% lack confidence evaluating ROI for large-scale brand initiatives

Underlying these gaps are inconsistent measurement systems: 34% cite reliability concerns, while 33% report conflicting data sources, making it difficult to act decisively on insights.

Measurement Gaps Shape Strategic Decisions

Uncertainty in measurement affects which marketing initiatives are pursued. Nearly three in four (74%) report abandoning or scaling back campaigns due to unclear impact, while 69% feel pressure to deprioritize brand-building initiatives in favor of immediate performance metrics.

“The risk is a structural shift toward short-termism,” Epstein explains. “Organizations end up favoring easier-to-measure returns over initiatives that drive long-term brand value and innovation.”

AI Accelerates Execution—but Not Accountability

AI adoption is widespread, and most leaders express confidence in leveraging AI tools. Yet when it comes to financial accountability, confidence drops sharply:

• Only 51% feel confident explaining AI-driven ROI to the board

• 71% believe AI tools prioritize short-term performance over long-term growth

• 63% report pressure to deliver more with fewer resources due to AI

AI is changing how marketing operates, but execution is outpacing clarity. “AI systems learn from observed data, which biases them toward short-term metrics,” Epstein notes. “Without linking investment to actual financial outcomes, organizations risk scaling the wrong objectives.”

Implications for Growth

For brands managing multimillion-dollar budgets under increasing scrutiny, these findings are a wake-up call: confidence in marketing measurement is not the same as clarity or accountability. Organizations that fail to connect performance to financial outcomes risk undermining strategic growth, misallocating capital, and missing opportunities to innovate.

Effective measurement is no longer just about tracking activity—it’s about creating systems that allow leaders to defend decisions, allocate resources confidently, and invest in initiatives that drive durable growth.

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Arctiq, a leading provider of IT solutions and managed services, has acquired Shadow-Soft, an Atlanta-based technology firm known for its expertise in observability, automation, and modern platform operations. The deal strengthens Arctiq’s engineering capabilities across North America, particularly for organizations adopting AI-driven applications and complex digital environments.

Shadow-Soft brings deep technical experience with Dynatrace, Red Hat, Kubernetes, containers, and cloud-native platforms. The company has built a reputation for engineering-led delivery, helping clients implement automation, DevOps practices, and platforms that enhance operational efficiency and reliability. Shadow-Soft’s team will join Arctiq’s broader engineering and services organization, continuing to serve existing customers while extending expertise across a global client base.

“Shadow-Soft adds exceptional technical depth to our team and strengthens our delivery capabilities globally,” said Paul Kerr, CEO of Arctiq. “As organizations adopt AI-driven applications and operate more complex digital environments, visibility and automation across infrastructure, applications, and security become essential. The expertise this team brings in observability and platform operations will help our customers run more resilient, intelligent environments at scale.”

Engineering Expertise Meets Global Scale

James Chinn, Founder & CEO of Shadow-Soft, noted that modern platforms generate enormous operational data, but many organizations struggle to translate it into actionable insights. “That is the challenge Shadow-Soft has been focused on solving through observability, automation, and deep platform engineering expertise around Kubernetes, containers, and cloud-native architectures,” Chinn said.

He added that joining Arctiq gives Shadow-Soft the opportunity to deliver its engineering capabilities to a larger customer base while providing clients access to a global services platform, expanded infrastructure and security capabilities, and innovation in AI-driven operations.

The acquisition reflects a broader trend in IT services: as enterprises increasingly rely on AI and cloud-native architectures, providers that can combine observability, automation, and platform engineering are positioned to help clients run more intelligent, resilient, and scalable environments.

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Corelight, a leading network detection and response (NDR) provider, is rolling out category-first agentic AI capabilities designed to dramatically improve security operations center (SOC) efficiency. The launch includes Agentic Triage, a new suite of machine learning models to detect encrypted threats, and integrations across AI-enabled security ecosystems that allow immediate containment of compromised accounts.

“By pairing Corelight’s high-fidelity network telemetry with an expert-governed AI agent, security teams receive evidence they can trust, verify, and act on,” said Vijit Nair, Corelight VP of Product. “Corelight uniquely transforms overwhelming alert queues into verified, defensible investigations, drastically reducing time-to-triage and equipping analysts with definitive answers.”

Accelerating SOC Workflow with Agentic Intelligence

Modern SOCs face relentless pressure as adversaries leverage generative AI to automate attacks, while most triage processes remain manual and repetitive. Corelight’s Agentic Triage automates investigation of the highest-risk entities, consolidating signals into entity-centric investigations and providing single, evidence-backed triage verdicts.

Unlike black-box AI solutions, Corelight exposes every playbook step, query, and piece of evidence used to reach a conclusion. This transparency is critical for enterprises requiring AI to be accountable, reviewable, and defensible during audits and incident response.

“Only Corelight delivers true agentic AI triage in NDR, applying expert playbooks to industry-leading network evidence with AI reasoning,” Nair added.

Connecting and Empowering the AI-Enabled Ecosystem

Once alerts are triaged, SOCs need to act quickly. Corelight now ingests real-time identity data to correlate “who” is involved with “what” is happening on the network. Integrations with Microsoft Azure AD/Entra and CrowdStrike allow one-click actions such as universal logouts or password resets without switching tools. Analysts can also quarantine endpoints or trigger firewall blocks directly from the Corelight platform.

Additionally, Corelight’s new integration with CrowdStrike Charlotte AI enables cross-agent collaboration. Charlotte AI can automatically pull Corelight ground-truth data, validating host behavior against network reality, accelerating evidence-backed response.

“The question for CISOs isn’t whether to adopt AI, but how quickly and comprehensively,” said Andrew Braunberg, principal analyst at Omdia. “Explainability isn’t optional—it’s a requirement, particularly in regulated environments.”

Detecting Multi-Stage Intrusions with Advanced ML

Corelight is also expanding its machine learning and behavioral detections to uncover evasive post-exploitation tactics without decryption. New models detect anomalies in tunneling and VPN usage, credential theft attempts, and unauthorized lateral movement.

By analyzing behavioral metadata and traffic patterns, Corelight can expose covert C2 channels, lateral movement, and brute-force attacks across Kerberos, RDP, SMB, and SSH. This empowers SOCs with high-fidelity visibility even in encrypted or otherwise opaque environments.

With these updates, Corelight positions itself at the forefront of agentic AI in NDR, combining automation, explainability, and advanced detection to help security teams respond faster and more confidently to evolving threats.

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Consensus, the leading platform for automated product demos, has partnered with Opine, an AI-powered technical deal orchestration platform, to modernize the enterprise technical buying journey. The collaboration integrates Consensus’s demo automation with Opine’s orchestration tools, creating a continuous, buyer-led workflow from product discovery to proof-of-concept and beyond.

Enterprise buying often stalls in the so-called “messy middle,” where disconnected demos, evaluations, and stakeholder coordination slow down deals. By feeding buyer intent data from Consensus demos directly into Opine’s platform, revenue teams gain immediate visibility into stakeholder interests, feature priorities, and buyer intent, enabling precise technical evaluations from day one.

“Today's buyers want to experience the product immediately,” said Adam Freeman, Global Partnerships & Strategic Alliances at Consensus. “By partnering with Opine, we’re ensuring momentum from automated demos translates into a highly tailored sales engineering process that accelerates deals and builds trust with buyers.”

Key Benefits for Joint Customers

• A Warm Start for Every Opportunity: Sales engineers no longer start from scratch. Consensus captures feature interests and buyer pain points that automatically flow into Opine, enabling tailored engagement from the first interaction.

• Reduced Time-to-Value: Automating early product education through Consensus and coordinating complex technical validation with Opine shortens sales cycles and delivers value faster.

• Total Deal Visibility: Revenue leaders can track a continuous thread of buyer intelligence from the first demo click to final technical sign-off, identifying where deals accelerate or stall.

The partnership also integrates Consensus’s Demolytics analytics with Opine’s AI-powered deal signals and sentiment analysis, giving teams a unified view of stakeholder engagement and deal health throughout the technical evaluation process.

“Enterprise deals are won or lost in the technical evaluation phase,” said Akash Ganapathi, CEO of Opine. “Our partnership with Consensus connects product education directly to the technical sales workflow, enabling revenue teams to act faster and more precisely, while providing buyers a smoother, more informed experience.”

By uniting product experience, buyer intelligence, and technical sales orchestration into a single workflow, the Consensus–Opine integration transforms fragmented processes into an insight-driven system—scaling expertise, accelerating deals, and building lasting buyer trust.

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Neoclouds, MSPs, sovereign AI providers, and IT service companies are under mounting pressure to deploy AI-ready environments quickly, onboard tenants on demand, and maintain strict uptime and isolation—all without months of custom integration. Spectro Cloud and Netris are tackling that challenge head-on with a new integrated solution combining PaletteAI and Netris NAAM (Network Automation, Abstraction, and Multi-Tenancy).

The joint platform lets teams provision tenant-ready AI environments with coordinated changes across cluster, workload, and network layers in a single, automated workflow. The result: faster time-to-revenue, instant safe onboarding, lower operational risk, and a future-ready architecture for NVIDIA GPU ecosystems.

From Months to Days: Key Benefits

• Faster Time-to-Revenue: Pre-validated full-stack deployments—from bare metal to billable tenant—replace months of manual assembly.

• Instant, Safe Tenant Onboarding: Hard isolation enforced at the network layer with automated tenant provisioning, no cross-team tickets required.

• Lower Operational Risk: API-driven orchestration ties network changes directly to cluster lifecycle, reducing outages and strengthening SLAs.

• Multi-Fabric Consistency: Unified control plane across Ethernet, DPUs, InfiniBand, and NVLink fabrics.

• Secure Multi-Tenancy: Netris DPU agents enable NVIDIA DOCA HBN for network isolation and concurrent multi-tenant workloads on bare-metal GPU hosts.

• Future-Ready Architecture: Validated today on NVIDIA BlueField-3 DPUs and designed for upcoming NVIDIA BlueField-4 and Vera Rubin architectures.

How the Integration Works

• PaletteAI: Delivers curated, deployable blueprints across OS, Kubernetes, storage, and the NVIDIA AI Enterprise ecosystem with governance and self-service for AI teams.

• Netris NAAM: Automates network orchestration, enforces hard isolation, and removes manual ticket-driven bottlenecks.

The integration ensures end-to-end tenant orchestration: PaletteAI deploys validated platform blueprints, while Netris provisions matching network constructs. Day-two operations—expansions, onboarding, decommissioning—trigger automatic network updates via Netris APIs. Compliance-aligned, air-gapped deployments are supported for regulated environments.

Built on the NVIDIA Ecosystem

The joint solution is validated across NVIDIA Spectrum-X Ethernet, Quantum-X InfiniBand, BlueField DPUs, NVLink, and the NVIDIA AI Enterprise software stack. By unifying infrastructure, networking, and AI workloads, the platform enables operators to move from infrastructure to inference in days, not months.

Executive Perspectives

“Building AI infrastructure at scale has always required stitching together cluster and network manually. Spectro Cloud and Netris eliminate that—meeting at the DPU to deliver a production-ready AI factory,” said Alex Saroyan, CEO and Co-Founder of Netris.

“The next generation of cloud infrastructure is the AI factory. PaletteAI automates full-stack management, and Netris extends that to network orchestration and hard multi-tenancy. Together, we deliver a turnkey platform for operators to move from bare metal to revenue quickly,” said Tenry Fu, CEO of Spectro Cloud.

This solution positions operators to standardize deployments, accelerate production readiness, and confidently scale multi-tenant AI workloads, transforming GPU infrastructure into a fully automated “AI factory.”

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UiPath (NYSE: PATH), a global leader in agentic automation, has unveiled a new security automation capability built in collaboration with Microsoft. The solution streamlines security operations by automating threat detection, enrichment, and response workflows across Microsoft Defender for Cloud, Microsoft Sentinel, and integrated Microsoft threat intelligence.

The integration combines UiPath’s automation platform with Microsoft’s security ecosystem to enrich alerts with business context, allowing enterprises to detect, investigate, and remediate threats faster while minimizing disruption to business workflows. The solution will be available via the UiPath Solutions Marketplace, enabling organizations to deploy and operationalize security automation alongside existing Microsoft security investments.

Embedding Security into Operational Workflows

“This collaboration brings security automation closer to where work actually happens,” said Andrei Oros, Director of IT Automation at UiPath. “By combining our automation capabilities with Microsoft Defender, Sentinel, and Security Copilot, enterprises can embed security controls directly into operational processes—protecting data and maintaining compliance without slowing business.”

Ruthy Kaidar, Managing Director Solutions, Software Companies, Microsoft EMEA, added: “UiPath’s integration fuses automation with built-in security and governance, enriching signals with business context, empowering human-in-the-loop decisions, and accelerating detection and response—so enterprises can scale agentic automation with confidence.”

How the Solution Works

Files and alerts generated from automated workflows are automatically:

1. Scanned with Microsoft Defender for Cloud for threats

2. Enriched with business context before being forwarded to Microsoft Sentinel

3. Investigated using Microsoft Security Copilot, with guided human-in-the-loop analysis

4. Remediated via UiPath automations, executing follow-up actions such as quarantining files, pausing workflows, or escalating incidents

This approach reduces mean time to respond (MTTR), enhances SOC productivity, and integrates security directly into enterprise workflows, providing a defensible, enterprise-scale solution for modern threat landscapes.

“Security teams need solutions that move at the speed of modern threats,” said Steven Spirou, Senior Product Manager, Microsoft Security. “UiPath’s collaboration with Sentinel and Security Copilot extends automation with richer context and faster response, delivering real, production-grade value to SOC teams.”

The solution reflects a growing trend of agentic automation in cybersecurity, where AI-driven orchestration reduces manual triage, ensures compliance, and enables faster, more accurate security responses across enterprise environments.

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