Vuzix Corporation has shipped a new production order for its waveguide-based augmented reality display systems to a major U.S. aerospace and defense technology provider, signaling continued momentum for wearable display technology in advanced military head-mounted display programs.

Augmented reality hardware supplier Vuzix Corporation announced it has fulfilled a six-figure follow-on production order for waveguide-based AR display systems destined for a next-generation head-mounted display (HMD) program used in the aerospace and defense sector.

The order was placed by a U.S. company specializing in advanced aerospace and defense technologies. While the customer was not publicly named, the display components will support the initial production rollout of an advanced wearable display platform designed for mission-critical environments.

The shipment marks another step in the gradual commercialization of AR-based head-mounted systems in defense applications. These systems combine compact optical displays, ruggedized hardware, and real-time data overlays to deliver contextual information directly within a user’s field of view.

Waveguide displays sit at the center of this emerging hardware category. The optical technology enables lightweight augmented reality glasses by projecting digital images through transparent lenses while maintaining situational awareness—an essential requirement in military and industrial environments.

According to Vuzix CEO Paul Travers, the follow-on order indicates continued progress toward broader deployment of the customer’s wearable display platform.

Travers said the order reflects the growing demand for advanced optical systems in high-performance environments where display quality, durability, and secure manufacturing processes are essential.

Vuzix’s waveguide technology forms the optical core of several AR and smart-glasses platforms. The company manufactures waveguide components, display engines, and wearable computing systems used across enterprise, healthcare, logistics, and defense sectors.

Unlike traditional display technologies used in consumer electronics, waveguide optics distribute projected images across transparent glass substrates. This approach enables thin, lightweight AR devices capable of projecting high-resolution digital overlays without blocking the real-world view.

That design has made waveguide displays increasingly attractive for military head-mounted systems, where pilots, technicians, and field operators require instant access to mission data while maintaining full situational awareness.

Defense and aerospace companies are investing heavily in wearable computing platforms designed to deliver navigation, targeting information, maintenance instructions, and communications overlays directly through AR headsets.

In recent years, major technology firms including Microsoft, Google, and Amazon have expanded research and development efforts in augmented reality infrastructure, cloud-connected devices, and enterprise wearable computing.

Enterprise AR platforms are increasingly integrated with cloud services, analytics tools, and artificial intelligence systems used to process operational data. For example, AR-enabled maintenance tools can overlay instructions generated from AI-driven diagnostics systems connected to enterprise platforms such as Salesforce or digital experience platforms from Adobe.

For Vuzix, the defense sector represents a strategic growth opportunity. Military and aerospace programs often require domestically produced optical systems with strict performance and security requirements, creating demand for specialized suppliers with deep expertise in display optics.

The company currently holds more than 500 patents and patent applications covering optical waveguides, head-mounted displays, and augmented-reality wearable technologies.

Beyond defense, Vuzix continues to develop smart-glasses platforms for enterprise use cases such as warehouse logistics, remote assistance, telemedicine, and field service operations. These applications rely on similar optical display architectures but are optimized for commercial workflows.

Industry analysts expect the broader AR hardware market to expand significantly over the next decade as enterprise adoption accelerates.

Research from International Data Corporation estimates global spending on augmented and virtual reality technologies could surpass $165 billion by 2026 as enterprises integrate immersive technologies into training, design, and operational workflows.

Meanwhile, Gartner has projected that wearable AR devices will increasingly become part of enterprise digital infrastructure, particularly in sectors requiring real-time data visualization such as manufacturing, logistics, and defense.

The follow-on order suggests that the unnamed aerospace customer is moving closer to broader production of its next-generation head-mounted display system.

If that rollout expands as expected, waveguide suppliers like Vuzix could play a growing role in the hardware ecosystem supporting the next generation of wearable computing platforms.

For enterprise technology leaders, the development highlights how advanced optics, wearable displays, and AI-driven data systems are converging to reshape how information is delivered to workers operating in complex environments.

Market Landscape

The augmented reality hardware ecosystem is rapidly evolving as enterprises explore wearable computing systems capable of delivering contextual data directly within a user’s field of view.

Waveguide optics have emerged as a critical component of modern AR glasses because they enable lightweight form factors without sacrificing display clarity or brightness. This makes them suitable for enterprise deployments in sectors such as aerospace, field services, logistics, healthcare, and defense.

Technology vendors including Microsoft, Google, and several specialized optical manufacturers are competing to define the next generation of AR hardware platforms.

As enterprise adoption grows, analysts expect the AR device market to shift from experimental deployments toward large-scale operational infrastructure integrated with cloud analytics, artificial intelligence, and enterprise data platforms.

Top Insights

• Vuzix shipped a six-figure follow-on order for waveguide-based AR display systems supporting a next-generation aerospace and defense head-mounted display program.

• Waveguide optics enable lightweight augmented-reality glasses capable of projecting high-resolution digital overlays while maintaining full situational awareness for military and industrial users.

• Defense and aerospace organizations are increasingly adopting wearable computing platforms to deliver mission data, navigation information, and maintenance instructions directly within operators’ field of view.

• Analysts from IDC and Gartner project strong enterprise growth for AR hardware as immersive technologies become integrated into operational workflows across manufacturing, logistics, and defense sectors.

• The order reinforces Vuzix’s position as a supplier of advanced optical systems for enterprise and defense wearable display platforms.

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Swoogo has introduced a native Model Context Protocol (MCP) server designed to connect live event management data directly to AI tools such as OpenAI’s ChatGPT, Google Gemini, and Anthropic Claude. The launch positions the B2B event platform as an early adopter of open AI infrastructure that allows enterprise event teams to interact with operational data using conversational AI and external developer tools.

Event management platform Swoogo has rolled out a native server based on the Model Context Protocol (MCP), enabling AI applications to securely access live event data stored within the platform.

The release marks one of the first deployments of MCP technology within the event technology sector. MCP is an emerging open protocol designed to allow AI models and applications to connect with external systems, retrieve contextual data, and perform actions across integrated platforms.

With the new MCP server, Swoogo customers can connect AI tools—including ChatGPT, Claude AI, Google Gemini, Replit, Cursor AI, and Lovable AI—directly to their event data without building custom integrations or APIs.

The company says implementation takes only a few minutes and does not require engineering support, a design decision aimed at event teams that lack dedicated development resources.

Opening Event Data to the AI Ecosystem

Historically, event management platforms have treated AI features as closed capabilities embedded inside their own products—typically chatbots or automated reporting tools limited to internal dashboards.

Swoogo’s strategy diverges from that model. Instead of confining AI to its platform, the company is positioning itself as a data infrastructure layer that external AI tools can access.

According to CEO Chris Sykes, the goal is to make event data available wherever teams already work.

Rather than forcing planners to adopt a proprietary AI interface, the MCP server enables them to use familiar tools while retrieving real-time operational insights from Swoogo.

In practice, that means event organizers can ask natural language questions about registrations, session attendance, or attendee segmentation directly inside an AI assistant.

For example, planners could ask which companies have registered the most attendees, which conference sessions are nearing capacity, or which participants require follow-up outreach.

The AI tool queries the MCP server, retrieves the relevant data from Swoogo, and returns the results conversationally.

From Reporting to AI-Assisted Event Development

Beyond analytics, the MCP connection also enables AI-assisted event site creation and registration workflows.

Developer-focused AI tools such as Replit, Lovable, or Cursor can generate event registration pages through conversational prompts. Event teams simply describe the desired experience—such as a branded event site or custom registration flow—and the AI system generates the code.

Through the MCP server, the registration data generated by those tools feeds directly back into Swoogo, which continues to serve as the system of record.

The approach aligns with a growing trend in enterprise software known as “AI-assisted development,” where generative AI platforms generate applications, interfaces, or automation scripts based on natural-language instructions.

Breaking Down Event Data Silos

Another capability enabled by the MCP server is cross-event analytics.

Most event management platforms treat each event as an isolated dataset. Reporting typically happens on an event-by-event basis, which makes it difficult for marketing and revenue teams to identify long-term patterns.

By connecting AI tools directly to the platform’s underlying data, Swoogo enables analysis across multiple events simultaneously.

Teams can also combine event data with external systems such as customer relationship management platforms like Salesforce.

This allows organizations to link event attendance with revenue outcomes—for example identifying which attendees later converted into customers, which conferences generated the most pipeline, or which sessions influenced purchasing decisions.

According to Swoogo CTO Mike Olivieri, the company intentionally avoided building another proprietary chatbot.

Instead, the goal was to create a connection layer that allows event data to interact with the broader ecosystem of AI tools already used by marketing and operations teams.

Why the AI Integration Matters

Event technology is increasingly becoming part of enterprise marketing infrastructure rather than a standalone operational tool.

As B2B organizations rely more heavily on conferences, webinars, and customer events to generate pipeline, the data generated from those interactions has become valuable to sales, marketing, and customer success teams.

Research from Gartner suggests that by 2027, more than 60% of enterprise marketing teams will integrate AI-driven analytics into event marketing strategies to improve campaign performance and customer engagement.

Meanwhile, analysts at International Data Corporation estimate global spending on AI-enabled enterprise software will surpass $300 billion within the next several years, reflecting the growing role of AI infrastructure in business operations.

In that context, connecting event data directly to AI tools could help marketing teams move beyond static reports toward real-time insights and automation.

The launch also highlights a broader shift toward open AI integration standards.

If protocols like MCP gain traction across enterprise software platforms, AI systems may increasingly function as universal interfaces capable of accessing data from multiple applications simultaneously.

For event teams, that could mean interacting with their entire event portfolio—registrations, attendee behavior, CRM records, and revenue impact—through a single AI conversation.

Swoogo’s MCP server is available immediately and will remain fully accessible to customers through the summer of 2026 as the company expands support for additional AI tools.

Market Landscape

Event technology platforms are evolving into data hubs that connect marketing, sales, and customer experience systems. As AI adoption accelerates across enterprise software, vendors are racing to integrate generative AI and analytics directly into operational workflows.

The emergence of open AI protocols such as the Model Context Protocol could reshape how enterprise applications interact with artificial intelligence. Instead of building isolated AI features, software vendors may increasingly expose structured data layers that external AI systems can access.

For B2B marketing teams, the shift could transform event programs from operational activities into measurable revenue channels integrated with CRM, marketing automation, and analytics platforms.

Top Insights

• Swoogo launched a native Model Context Protocol server enabling AI tools like ChatGPT, Claude, and Gemini to directly access live event management data.

• The open AI integration allows event teams to query registrations, session capacity, and attendee profiles through conversational interfaces instead of traditional dashboards.

• By connecting event data with CRM platforms such as Salesforce, organizations can track revenue impact, pipeline generation, and customer conversion tied to event participation.

• Analysts from Gartner and IDC expect enterprise AI integration across marketing platforms to accelerate as organizations seek real-time analytics and automation capabilities.

• The release signals a broader industry shift toward open AI infrastructure where enterprise software platforms expose data to external AI tools instead of building isolated proprietary assistants.

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Onix has expanded its strategic collaboration with Google Cloud to accelerate enterprise adoption of cloud, data modernization, and agentic AI platforms. The partnership centers on Onix’s proprietary Wingspan platform, which the company says enables enterprises to deploy AI-powered systems and modern data infrastructure significantly faster than traditional consulting-led transformation programs.

Enterprise data and AI services provider Onix is deepening its collaboration with Google Cloud as enterprises move from experimental AI deployments to large-scale production systems.

The expanded partnership aims to help organizations modernize their data infrastructure and deploy agentic AI systems capable of automating complex business workflows. At the center of the initiative is Onix’s proprietary Wingspan platform, which combines data modernization tools, automation frameworks, and AI orchestration capabilities.

The company says Wingspan allows enterprises to realize business value up to three times faster than traditional consulting-driven transformation approaches.

From AI Experimentation to Production

Over the past two years, many enterprises have experimented with generative AI tools across customer support, analytics, and marketing automation. However, turning those pilots into production-grade systems has proven difficult due to fragmented data architectures and limited organizational readiness.

The expanded collaboration between Onix and Google Cloud reflects a broader industry shift toward operationalizing AI across enterprise infrastructure.

According to Sanjay Singh, AI transformation succeeds only when organizations can run systems at scale that solve real operational workflows.

Instead of relying solely on large consulting teams, Onix is promoting an AI-assisted delivery model built around automation and proprietary intellectual property.

That approach is designed to accelerate the transition from concept to production deployments.

The Role of Agentic AI in Enterprise Systems

A central component of the Wingspan platform is what Onix calls a “Semantic Twin” model. The framework creates a structured representation of enterprise knowledge—including data relationships, workflows, and business ontology—that AI agents can use to understand organizational context.

Agentic AI systems rely on this contextual layer to autonomously perform tasks such as analyzing datasets, generating insights, and orchestrating operational workflows.

In practical terms, the Semantic Twin model enables AI agents to operate within enterprise systems while understanding industry-specific terminology, data structures, and business processes.

This capability is becoming increasingly important as enterprises deploy AI agents to manage tasks previously handled by human analysts or operational teams.

Onix says thousands of AI agents built using its platform are already running in production environments across several Fortune 500 companies.

Automating Data Modernization

Another key component of the partnership involves automating data modernization projects—an area that traditionally requires lengthy consulting engagements.

Through the Wingspan platform, Onix aims to streamline the process of transforming legacy datasets into AI-ready infrastructure capable of supporting machine learning models and analytics systems.

The platform integrates with Google Cloud’s data and AI services, allowing organizations to build scalable pipelines for data ingestion, transformation, and AI deployment.

Enterprise platforms from vendors such as Microsoft, Amazon, and Salesforce have similarly invested in AI-enabled data infrastructure, reflecting a growing industry emphasis on unified cloud-based analytics ecosystems.

For Onix, the goal is to combine its proprietary automation capabilities with Google Cloud’s generative AI services and data platforms.

A Shift Toward Outcome-Based Transformation

The partnership also introduces a new delivery model focused on measurable business outcomes rather than traditional consulting engagements.

Instead of deploying large project teams, Onix plans to use smaller “AI-assisted delivery pods” supported by automation and proprietary tools.

These pods combine technical engineers, AI specialists, and outcome-focused consultants responsible for delivering projects tied to defined business KPIs.

The approach reflects a growing demand among enterprise leaders for transformation initiatives that deliver measurable return on investment rather than open-ended consulting engagements.

Victor Morales said the collaboration builds on Google Cloud’s generative AI capabilities, which are increasingly being integrated into enterprise systems to improve productivity and operational insights.

Why the Collaboration Matters

The expanded partnership highlights how enterprise cloud platforms are evolving into the operational backbone for AI-powered business transformation.

Research from Gartner suggests that by 2028, more than 75% of enterprise applications will incorporate AI capabilities, requiring organizations to modernize data infrastructure to support intelligent automation.

Meanwhile, International Data Corporation estimates that global spending on AI technologies could exceed $500 billion by the end of the decade as companies invest in data platforms, automation tools, and AI-driven decision systems.

For industries such as telecommunications, retail, healthcare, and financial services, the ability to deploy AI agents that operate on enterprise data could reshape how organizations manage operations, customer interactions, and decision-making.

The expanded collaboration between Onix and Google Cloud signals a growing shift toward AI-native enterprise infrastructure—where data platforms, cloud services, and intelligent agents work together to automate complex workflows.

If that model continues to gain traction, agentic AI systems may become a central component of the next generation of enterprise digital transformation initiatives.

Market Landscape

Enterprise adoption of generative AI is entering a new phase focused on operational deployment rather than experimentation. Organizations are increasingly seeking platforms that integrate cloud infrastructure, data pipelines, and AI agents capable of automating workflows.

Cloud providers such as Google Cloud, Microsoft Azure, and Amazon Web Services are investing heavily in AI infrastructure to support enterprise demand for scalable data platforms and machine learning environments.

At the same time, consulting and services firms are shifting toward automation-driven delivery models that use proprietary platforms to accelerate implementation timelines.

The combination of agentic AI, cloud infrastructure, and modern data architectures is expected to define the next stage of enterprise digital transformation.

Top Insights

• Onix expanded its strategic collaboration with Google Cloud to accelerate enterprise adoption of cloud infrastructure, data modernization, and agentic AI platforms.

• The partnership centers on Onix’s Wingspan platform, which includes a Semantic Twin model designed to provide enterprise context and ontology for AI agents operating within business systems.

• The collaboration introduces an outcome-based delivery model that uses AI-assisted engineering teams to deploy enterprise data and AI platforms faster than traditional consulting engagements.

• Thousands of AI agents built with the Wingspan platform are already operating in production environments across Fortune 500 organizations.

• Analysts from Gartner and IDC expect enterprise spending on AI infrastructure and cloud-based analytics platforms to grow rapidly over the next several years.

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GoDaddy has unveiled a redesigned mobile application aimed at helping entrepreneurs manage their digital businesses from anywhere. The updated app combines website creation, domain management, customer communication, and marketing tools into a single mobile-first platform built for small business owners operating on the go.

Small business technology provider GoDaddy has introduced a reimagined version of its mobile app designed to centralize key business operations within a single mobile interface.

The update reflects the company’s broader strategy to position itself as an all-in-one platform for entrepreneurs who increasingly manage their businesses through mobile devices.

The redesigned app allows users to create websites, manage domains, produce branded marketing content, communicate with customers, and monitor business activity directly from a smartphone.

According to Tristan Krause, the goal is to simplify business management for entrepreneurs who need tools that can operate across multiple workflows without requiring separate platforms.

A Mobile Hub for Digital Business Operations

Mobile-first business management tools have become increasingly important as entrepreneurs adopt flexible work environments and rely on cloud platforms to operate online businesses.

The GoDaddy app integrates several services traditionally accessed through desktop dashboards, enabling users to perform tasks such as launching a website, registering a domain name, and updating online content through a mobile interface.

Business owners can also monitor activity within their accounts, receive alerts about important updates, and manage security settings such as multi-factor authentication.

The unified environment is designed to reduce the need for multiple applications while giving entrepreneurs greater visibility into the performance of their digital presence.

Content Creation and Marketing Tools

Alongside operational features, the app includes creative tools that allow business owners to generate logos, marketing graphics, and short promotional videos using templates designed for small businesses.

These tools support social media marketing workflows by enabling users to create branded content and schedule posts directly from the mobile platform.

Social content scheduling has become a key requirement for small businesses that rely on digital channels to reach customers without maintaining large marketing teams.

By integrating these features into a single application, GoDaddy aims to reduce the complexity often associated with managing website platforms, marketing tools, and communication systems separately.

Customer Communication and Messaging

Another feature of the redesigned app is a unified inbox that aggregates customer messages across communication channels.

The inbox allows entrepreneurs to respond quickly to inquiries, improving responsiveness and potentially increasing customer engagement.

Centralized messaging systems are becoming increasingly common in small business platforms as companies look to streamline customer interactions across email, messaging apps, and website forms.

Competing in the Small Business Platform Market

GoDaddy’s mobile expansion comes as several technology platforms compete to provide integrated digital infrastructure for entrepreneurs.

Companies such as Shopify, Squarespace, and Wix have also invested in mobile tools that allow users to manage online storefronts, websites, and marketing operations from smartphones.

Meanwhile, broader digital ecosystems from technology providers like Google and Microsoft continue to influence how small businesses interact with productivity tools, cloud services, and digital marketing platforms.

For GoDaddy, integrating its services into a single mobile experience may strengthen its role as a digital operations hub for entrepreneurs managing domains, websites, and online marketing campaigns.

Why the Mobile Strategy Matters

Mobile-first software design has become a priority for companies targeting small business customers.

Many entrepreneurs now manage operations while traveling, meeting clients, or working remotely, making mobile access to business systems increasingly essential.

Research from Statista indicates that global mobile internet traffic now accounts for more than half of all web usage, highlighting the shift toward mobile-driven digital activity.

Meanwhile, Gartner has reported that small businesses are increasingly adopting unified SaaS platforms that combine website management, digital marketing tools, and customer communication features into integrated ecosystems.

The redesigned GoDaddy app reflects this broader trend by consolidating tools previously spread across multiple services.

Available on both iOS and Android, the mobile application provides entrepreneurs with a single entry point for managing their digital presence, engaging customers, and maintaining business operations while away from their desks.

For small business owners balancing marketing, customer support, and operational tasks, the ability to manage these responsibilities through a mobile device may significantly simplify day-to-day workflows.

Market Landscape

Small business platforms are evolving into integrated digital ecosystems that combine website creation, domain management, marketing tools, and customer communication features.

As entrepreneurs increasingly rely on mobile devices to manage operations, SaaS providers are prioritizing mobile-first interfaces that replicate the capabilities of desktop business management systems.

Competition in this space continues to intensify, with platforms like Shopify, Wix, and Squarespace offering similar all-in-one tools aimed at helping entrepreneurs build and scale digital businesses without extensive technical expertise.

The shift toward unified mobile platforms suggests that future small business software may operate more like digital operating systems—providing centralized access to commerce, marketing, and communication tools.

Top Insights

• GoDaddy launched a redesigned mobile-first application that allows entrepreneurs to manage websites, domains, customer communications, and marketing tools from a single smartphone interface.

• The platform combines operational and creative tools including logo generation, video creation, and social content scheduling to support small business marketing workflows.

• A unified inbox allows entrepreneurs to respond to customer inquiries quickly across messaging channels, improving communication efficiency and engagement.

• The mobile strategy positions GoDaddy alongside competitors such as Shopify, Wix, and Squarespace that are expanding mobile management tools for small business users.

• Analysts from Statista and Gartner highlight the growing importance of mobile-first SaaS platforms as entrepreneurs increasingly manage operations through smartphones.

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Levanta has acquired Perch+, an early affiliate network built specifically for Amazon sellers, in a move aimed at expanding its creator-driven commerce marketplace. The deal brings hundreds of Amazon-focused brands and publishers into Levanta’s platform, strengthening its position in the rapidly growing affiliate and creator economy for e-commerce.

Affiliate and creator commerce platform Levanta is expanding its marketplace through the acquisition of Perch+, an affiliate network originally designed to connect Amazon sellers with publishers and creators.

The acquisition adds Perch+’s network of brands, creators, and affiliate partners to Levanta’s ecosystem, providing those participants with access to a broader creator marketplace and enhanced affiliate infrastructure.

Levanta says the integration will give Perch+ brands the ability to recruit creators at scale, run paid creator campaigns, and track performance across multiple e-commerce platforms including Amazon, Shopify, and Walmart.

Expanding a Creator-Driven Commerce Marketplace

Levanta has been positioning itself as a unified affiliate and creator platform designed for modern e-commerce brands. The company says its platform connects more than 60,000 vetted creators and publishers with brands seeking performance-based marketing partnerships.

The addition of Perch+ expands that network with sellers and affiliates already familiar with Amazon’s affiliate ecosystem.

According to Ian Brodie, Perch+ was among the first platforms to focus on the needs of Amazon-native brands looking to use affiliate marketing as a scalable growth channel.

By incorporating that network into Levanta’s marketplace, the company aims to increase opportunities on both sides of the ecosystem—giving creators more brands to collaborate with while providing sellers with expanded creator partnerships.

Affiliate Infrastructure for Modern E-commerce

Affiliate marketing has evolved significantly over the past decade, shifting from traditional publisher partnerships toward creator-driven commerce powered by social media platforms.

Influencers, content creators, and niche publishers now play a central role in product discovery and purchase decisions across platforms like YouTube, Instagram, and TikTok.

Levanta’s platform attempts to streamline that ecosystem by combining affiliate tracking, creator discovery, campaign management, and payment infrastructure within a single system.

Brands can recruit creators, launch affiliate campaigns, distribute product samples, and measure performance through tools integrated with programs such as Amazon Attribution and Creator Connections.

Paid Placements and Creator Partnerships

Another feature gaining traction within the platform is Levanta’s Paid Placements model.

The feature allows brands to negotiate flat-rate creator partnerships while still tracking performance metrics typically associated with affiliate marketing, such as attributed sales and conversions.

This hybrid model reflects a growing trend in the creator economy where brands blend traditional influencer marketing deals with performance-based affiliate commissions.

For creators, the expanded marketplace offers new revenue opportunities across multiple compensation structures, including commission-based partnerships, sponsored placements, and product sampling campaigns.

Transition for Perch+ Sellers and Creators

For sellers and publishers previously using Perch+, the move to Levanta introduces a more robust set of tools for managing partnerships and campaigns.

Creators gain access to a larger marketplace of brands seeking partnerships, along with improved tracking systems and faster payment processing.

Brands migrating from Perch+ can also identify creators who are already discussing their products across social media and activate partnerships through Levanta’s platform.

The original Perch+ network was developed by Infinite Commerce, which positioned affiliate marketing as a key growth channel for Amazon-native brands.

According to Jason Baer, the transition to Levanta’s infrastructure enables the network to expand further within a larger marketplace built for creator-driven commerce.

Why the Acquisition Matters

The acquisition highlights how affiliate marketing is converging with the creator economy.

Rather than relying solely on traditional publishers, brands are increasingly partnering with independent creators who influence purchasing decisions through social media content, product reviews, and niche communities.

Research from Statista estimates the global influencer marketing market will exceed $30 billion within the next few years, driven by brands seeking more authentic and measurable engagement with consumers.

Meanwhile, Gartner has reported that performance-based marketing channels such as affiliate partnerships are gaining renewed interest among e-commerce brands looking for measurable return on advertising spend.

By expanding its marketplace through the Perch+ acquisition, Levanta is positioning itself at the intersection of these two trends—creator commerce and performance marketing.

If adoption continues to grow, platforms like Levanta may increasingly serve as the infrastructure layer connecting brands, creators, and e-commerce platforms within the digital commerce ecosystem.

Market Landscape

Affiliate marketing platforms are evolving rapidly as the creator economy reshapes how consumers discover and purchase products online.

Traditional affiliate networks built around publishers and comparison sites are increasingly being supplemented—or replaced—by platforms that connect brands directly with creators and influencers.

E-commerce companies are also integrating affiliate marketing with creator tools, social commerce features, and performance analytics platforms.

This shift is creating a new category of creator-driven commerce platforms designed to unify influencer marketing, affiliate tracking, and campaign management within a single ecosystem.

Top Insights

• Levanta acquired Perch+, adding an established Amazon-focused affiliate network to its creator-driven e-commerce marketplace.

• The integration gives Perch+ sellers access to Levanta’s network of more than 60,000 creators and publishers across Amazon, Shopify, and Walmart ecosystems.

• Levanta’s platform combines affiliate marketing, creator partnerships, and campaign management tools designed for modern e-commerce brands.

• The acquisition reflects broader industry trends where affiliate marketing is merging with influencer and creator-driven commerce models.

• Analysts expect continued growth in creator partnerships as brands seek measurable marketing channels with performance-based returns.

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Nexxen has introduced a new AI-native interface for its demand-side platform (DSP), alongside major upgrades to the nexAI DSP Assistant designed to support every phase of programmatic campaign management. The update aims to help media buyers improve full-funnel advertising performance through AI-powered insights, automation, and real-time campaign diagnostics.

Advertising technology platform Nexxen has rolled out an AI-native redesign of its demand-side platform (DSP), introducing an upgraded user interface and expanded capabilities for the nexAI DSP Assistant.

The release marks a broader effort by the company to integrate artificial intelligence more deeply into programmatic advertising workflows—from campaign planning and setup to optimization and post-campaign analysis.

The updated nexAI DSP Assistant now supports multiple phases of campaign execution, helping media buyers identify configuration errors before launch, troubleshoot delivery issues, and optimize campaigns while they are running.

AI Expands Across the Campaign Lifecycle

Demand-side platforms have traditionally relied on data dashboards and manual campaign management tools for planning, activation, and performance analysis.

With the latest update, Nexxen is embedding AI across the campaign lifecycle to automate operational tasks and deliver recommendations based on real-time performance data.

The nexAI DSP Assistant now provides pre-campaign quality assurance by automatically detecting potential misconfigurations that could disrupt campaign performance.

Once campaigns are live, the assistant can diagnose delivery issues related to supply sources, audience targeting, or deal configurations, allowing media buyers to resolve problems more quickly.

The system also provides mid-campaign optimization recommendations based on performance signals within the platform.

A New AI-Native DSP Interface

Alongside the AI assistant upgrades, Nexxen has introduced a redesigned DSP interface intended to simplify workflows and reduce onboarding time for new users.

The company describes the UI as “AI-native,” meaning it was designed with AI-driven insights and recommendations integrated directly into the user experience rather than layered onto existing dashboards.

The streamlined interface aims to accelerate campaign launches while reducing the amount of training required for traders and planners.

According to Karim Rayes, the platform’s AI capabilities are built on high-quality datasets gathered through Nexxen’s proprietary data ecosystem.

These include behavioral data from Nexxen Discovery, transaction-level insights, and exposure data tied to campaign outcomes.

Rayes argues that AI models trained on deterministic signals—such as verified behavioral data—produce more reliable optimization outcomes than models trained on broader but less precise datasets.

Balancing Automation With Human Control

One of the distinguishing aspects of Nexxen’s approach is its emphasis on human oversight.

While the nexAI DSP Assistant provides recommendations and automation features, campaign decisions remain under the control of traders and planners.

Users can decide how much autonomy the AI assistant should have, maintaining transparency over changes to bids, budgets, audiences, and inventory sources.

This hybrid approach reflects a broader industry debate over the role of automation in programmatic advertising.

Some DSP providers have moved toward fully autonomous AI-driven campaign management systems. Others are adopting augmented intelligence models designed to support, rather than replace, human decision-making.

Industry partners appear to favor the latter model.

Joey D’Alesio said the upgraded assistant helps agencies connect planning, activation, and measurement workflows through a single interface.

The assistant can answer feasibility questions during planning stages, surface optimization opportunities during campaign execution, and accelerate performance analysis once campaigns conclude.

Future AI Capabilities in Development

Nexxen is also planning additional AI agents that will expand the DSP assistant’s capabilities.

Upcoming features include an optimization recommendation agent capable of suggesting adjustments to bids, budgets, and targeting strategies.

Another planned capability is an audience discovery agent that will help advertisers identify new audience segments using insights from Nexxen Discovery and the company’s data marketplace.

The platform will also introduce deeper integrations with the Nexxen supply-side platform (SSP), allowing advertisers to apply supply signals and performance insights across the full programmatic ecosystem.

Open API connections will further enable brands and agencies to integrate third-party data sources for campaign planning, measurement, and optimization.

Why AI in DSPs Matters

Artificial intelligence has become a central battleground among programmatic advertising platforms as advertisers demand greater efficiency and transparency from digital media buying systems.

Research from Statista estimates global programmatic advertising spending will surpass $700 billion by the end of the decade as brands increasingly rely on automated buying platforms.

Meanwhile, Gartner reports that AI-driven marketing platforms are rapidly evolving to combine data analytics, campaign management, and optimization capabilities within unified ecosystems.

For advertisers and agencies managing increasingly complex digital campaigns across channels such as connected TV, mobile, and display, AI-powered tools may play a crucial role in simplifying decision-making and improving performance outcomes.

By embedding AI directly into its DSP interface, Nexxen is attempting to position its platform as a unified environment where planning, activation, and measurement workflows converge.

As programmatic ecosystems continue to expand, the companies that successfully combine high-quality data infrastructure with AI-assisted campaign management tools are likely to shape the next phase of advertising technology innovation.

Market Landscape

Demand-side platforms are evolving beyond automated ad buying systems into full marketing intelligence platforms.

Modern DSPs increasingly integrate data management, AI-driven optimization, and measurement tools within a single environment to help advertisers manage campaigns across multiple channels.

Technology providers including Google, Amazon, and Microsoft have also invested heavily in AI-driven advertising infrastructure as brands seek better attribution, audience insights, and performance optimization.

The integration of artificial intelligence across programmatic advertising platforms is expected to accelerate as marketers demand faster decision-making and improved ROI from digital campaigns.

Top Insights

• Nexxen launched a redesigned AI-native DSP interface alongside major upgrades to its nexAI DSP Assistant to support campaign planning, optimization, and performance analysis.

• The AI assistant now provides pre-campaign configuration checks, mid-flight optimization insights, and troubleshooting diagnostics to improve campaign delivery.

• The platform combines deterministic behavioral data, transaction insights, and exposure measurement signals to train AI models designed for more accurate optimization outcomes.

• Nexxen plans additional AI agents for audience discovery and automated optimization across bids, budgets, and supply sources.

• The release reflects broader industry momentum toward AI-powered programmatic advertising platforms that integrate planning, activation, and measurement.

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NeuBird AI has secured $19.3 million in an oversubscribed funding round to expand its agentic AI platform designed for enterprise production operations. The investment, led by Xora Innovation with participation from Mayfield, StepStone Group, Prosperity7 Ventures, and M12—the venture arm of Microsoft—will support product innovation and global expansion of NeuBird’s AI-driven production operations platform.

Enterprise infrastructure complexity is growing rapidly as organizations operate across hybrid and multi-cloud environments. To address this challenge, NeuBird AI has raised $19.3 million in new funding to accelerate the adoption of its AI-powered production operations agent designed for DevOps, SRE, and IT operations teams.

The funding round was led by Xora Innovation, with continued backing from existing investors including Mayfield, StepStone Group, Prosperity7 Ventures, and M12.

The company plans to use the capital to accelerate product innovation, expand global go-to-market operations, and broaden enterprise adoption of its AI-driven platform.

The Growing Challenge of Production Operations

As enterprise technology stacks become more complex, engineering teams are spending increasing amounts of time troubleshooting production incidents rather than building new products.

According to the 2026 State of Production Reliability and AI Adoption Report, engineers spend approximately 40% of their time managing incidents instead of focusing on innovation. Alert fatigue, insufficient automation, and rising system complexity have contributed to widespread burnout among on-call engineering teams.

Gou Rao explained that modern IT infrastructure generates massive volumes of alerts, logs, and telemetry data across distributed environments.

Many organizations still rely on manual investigation workflows to identify root causes, which slows innovation and consumes valuable engineering resources.

Introducing Agentic AI for Production Ops

NeuBird AI’s platform introduces an autonomous production operations agent designed to replace manual troubleshooting with AI-powered reasoning across telemetry signals.

Unlike traditional monitoring tools that focus on alert management alone, the system correlates signals across multiple infrastructure sources to automatically identify root causes and recommend or execute remediation.

The platform acts as a continuously learning AI engineer, capable of diagnosing incidents and resolving issues without waiting for human intervention.

This approach aims to transform production operations from a reactive process into a proactive and predictive system.

Launch of NeuBird AI Falcon

Alongside the funding announcement, the company introduced NeuBird AI Falcon, the next-generation engine powering its production operations agent.

Falcon expands the platform’s capabilities beyond incident resolution to include:

• Predictive risk detection
• Infrastructure cost optimization
• End-to-end production operations automation

Vinod Jayaraman noted that engineering productivity losses caused by incident response are becoming unsustainable.

By automating troubleshooting and root cause analysis, AI-powered operations agents can help teams focus more on innovation and system design rather than firefighting infrastructure issues.

Enterprise Adoption and Market Momentum

Since launching general availability in December 2024, NeuBird AI has transitioned from pilot deployments to production environments across a growing base of enterprise customers.

The company reports measurable outcomes from its platform deployments, including:

• Resolution of over 1 million infrastructure alerts
• Savings of more than $2 million in engineering hours
• Up to 90% reduction in mean time to resolution (MTTR)

These results highlight the growing demand for AI-powered tools that can manage increasingly complex enterprise systems.

Strategic Partnerships and Leadership Expansion

NeuBird AI has also strengthened its enterprise ecosystem through partnerships with major cloud providers.

The company recently earned the AWS Generative AI Competency across both applications and infrastructure categories from Amazon Web Services and joined the Generative AI Accelerator program.

It is also part of the Microsoft for Startups Pegasus Program, backed by M12, providing access to enterprise customers through the Microsoft Azure ecosystem.

To accelerate commercial growth, NeuBird appointed Venkat Ramakrishnan as president and chief operating officer. Ramakrishnan previously helped scale Portworx through its acquisition phase.

The Future of AI in IT Operations

Investor interest in NeuBird reflects the growing importance of AI-driven infrastructure management.

Phil Inagaki noted that enterprise production environments are becoming significantly more complex, creating opportunities for intelligent systems that can manage reliability at scale.

Industry analysts expect AI agents to become a foundational component of enterprise infrastructure operations, especially as organizations adopt distributed architectures spanning cloud providers, edge computing environments, and microservices platforms.

With its agentic AI model, NeuBird aims to position itself as the intelligence layer for modern production environments, helping organizations maintain reliability while freeing engineering teams to focus on innovation.

Key Industry Insight

AI-powered operations platforms represent the next stage of enterprise infrastructure management. By combining telemetry analysis, root cause detection, and automated remediation, agentic AI systems can significantly reduce operational overhead and improve system reliability.

As organizations scale across multi-cloud ecosystems, AI-driven operations platforms like those developed by NeuBird AI may become essential for maintaining uptime and optimizing infrastructure performance.

Top Insights

• NeuBird AI raised $19.3 million in funding to expand its agentic AI platform for enterprise production operations.
• The round was led by Xora Innovation with participation from Mayfield, StepStone Group, Prosperity7 Ventures, and M12.
• Engineers spend around 40% of their time managing incidents, highlighting the need for AI-driven automation in IT operations.
• The company launched NeuBird AI Falcon, a next-generation engine that expands capabilities to predictive risk detection and cost optimization.
• Enterprise adoption of agentic AI tools is increasing as organizations manage more complex multi-cloud environments.

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Ridge AI has emerged from stealth with $2.6 million in pre-seed funding to build an AI-native analytics platform that allows B2B software companies to deploy interactive dashboards and AI-powered data agents in hours. The funding round was led by Madrona with participation from TheFounderVC and angel investors from leading data analytics companies including Tableau, Trifacta, and Streamlit.

Data analytics has become a core capability for modern SaaS products, but building embedded analytics tools often requires months of engineering work.

To address this challenge, Ridge AI has launched a platform designed to help software companies quickly integrate interactive dashboards and AI-driven analytics directly into their products.

Founded by Ellie Fields and Jeffrey Heer, the company aims to simplify how product teams demonstrate value to their customers through data insights.

The startup announced a $2.6 million pre-seed funding round led by Madrona, with participation from TheFounderVC and a group of angel investors that includes former leaders from analytics companies such as Tableau, Trifacta, and Streamlit.

Addressing a Common SaaS Challenge

For many product leaders, one of the biggest challenges is proving the value their software delivers to customers.

Before founding Ridge, CEO Ellie Fields spent over a decade at Tableau and later served as Chief Product and Engineering Officer at Salesloft.

During that time, she observed how teams frequently diverted engineering resources away from product innovation in order to build custom analytics dashboards.

Fields said existing analytics solutions are often difficult to implement, expensive to maintain, and still fail to provide customers with the answers they need.

Ridge AI was created to eliminate that trade-off by enabling product teams to ship analytics experiences quickly without building them from scratch.

AI-Native Analytics Built for the Modern Web

The Ridge platform allows SaaS companies to embed analytics dashboards into their applications while enabling users to ask follow-up questions in natural language.

These questions are processed by an AI data agent that analyzes the dataset and returns insights instantly.

The technology powering Ridge is built on years of academic research in data visualization and interactive analytics.

Chief Scientist Jeffrey Heer is a professor of computer science at the University of Washington and co-director of the UW Interactive Data Lab.

He is also known for developing influential open-source data visualization technologies including D3.js and Vega.

He previously co-founded Trifacta, which was acquired by Alteryx in 2022.

Browser-Native Analytics Architecture

At the core of Ridge’s platform is Mosaic, an open-source analytics framework developed by Heer’s research group.

Mosaic enables interactive analytics directly within a web browser by combining technologies such as DuckDB and WebAssembly.

This architecture allows Ridge dashboards to process large datasets locally in the browser rather than sending queries to a server.

As a result, users can interact with millions of rows of data with sub-second response times without requiring expensive cloud infrastructure.

The approach also reduces operational costs for SaaS companies since analytics queries do not require continuous cloud processing.

The Rise of AI-Driven Embedded Analytics

Industry experts believe the convergence of three major technologies has enabled a new generation of analytics tools:

• Large language models capable of reasoning over structured data
• Browser-based high-performance computing enabled by WebAssembly
• Advanced visualization frameworks such as Mosaic

Together, these technologies allow analytics tools to deliver conversational interfaces alongside traditional visual dashboards.

According to Mark Nelson, the Ridge platform combines advanced data visualization with AI-driven analytics in a way that allows software companies to offer insights to their customers without building complex analytics infrastructure internally.

Support for the startup also comes from leaders in the analytics ecosystem.

Chris Stolte, co-founder of Tableau, described Ridge as the type of data platform needed in the emerging AI-driven analytics era.

Beta Launch and Early Access

Ridge AI is currently accepting applications for beta access to its platform.

The company plans to onboard a limited number of teams each week as it scales the product and gathers feedback from early enterprise users.

With embedded analytics becoming increasingly essential for SaaS platforms, Ridge aims to help software companies deliver insights faster while reducing engineering overhead.

If successful, the platform could significantly change how product teams build analytics experiences within modern applications.

Key Industry Insight

AI-driven analytics platforms are reshaping how companies interact with data. By combining conversational AI with high-performance browser computing, platforms like Ridge AI are enabling faster deployment of embedded analytics while lowering infrastructure costs.

This shift reflects a broader industry trend toward AI-native data experiences, where analytics becomes a built-in capability rather than a separate product feature.

Top Insights

• Ridge AI emerged from stealth with $2.6 million in pre-seed funding led by Madrona.

• The platform enables SaaS companies to deploy interactive dashboards and AI data agents in hours instead of months.

• Ridge’s technology is built on the Mosaic analytics framework, combining DuckDB and WebAssembly for browser-native analytics.

• The company was founded by Ellie Fields and data visualization pioneer Jeffrey Heer.

• Ridge AI is currently onboarding early beta users as it expands its AI-native analytics platform.

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