Tethr, An AI Platform, Has Launched Zero Touch Conversation Intelligence

The way that companies handle business continuity has been completely changed by cloud computing. The cloud guarantees that businesses can continue operations in the face of unforeseen disruptions by offering scalable, affordable, and extremely secure solutions for data storage, disaster recovery, communication, and collaboration. One of the most significant advantages of cloud-based business continuity is its robust data protection and security. Cloud service providers typically employ advanced encryption techniques, regular backups, and state-of-the-art security protocols to safeguard critical business data. This ensures that in the event of a disaster, the data remains intact and accessible from anywhere. Cloud services often feature geo-redundancy, meaning data is stored in multiple data centers across different geographic locations. This distributed infrastructure reduces the risk of data loss due to localized disasters, such as power outages or regional flooding, ensuring a higher level of resilience. Cloud-based solutions offer unparalleled scalability. Unlike traditional on-premises infrastructure, where businesses may have to predict their future storage needs and invest in additional hardware, cloud-based business continuity solutions can be scaled up or down as required. Whether a firm is undergoing rapid growth or facing seasonal fluctuations, it can adjust cloud infrastructure to meet specific requirements. Maintaining on-premises disaster recovery infrastructure can be expensive, requiring businesses to invest in physical hardware, dedicated personnel for maintenance, and the space to house servers and backups. Cloud-based business continuity services are typically offered on a pay-as-you-go basis or subscription, significantly reducing upfront capital expenses. By shifting to a cloud-based model, companies can eliminate expensive physical infrastructure and continuous maintenance costs. DigitalNet.ai delivers enterprise AI and data analytics platforms that support scalable cloud strategies while reducing barriers to adoption for complex IT environments. Businesses can avoid overinvesting in disaster recovery solutions or underinvesting and leaving themselves vulnerable to downtime. .A key component of cloud-based business continuity is ensuring uninterrupted access to business-critical applications and data, regardless of employees' physical locations. Cloud services enable businesses to implement remote access solutions, allowing staff to work from home or any other location during a disruption at the primary office site. Karolium provides a no-code enterprise composable platform that accelerates digital transformation and systems integration amid cloud migration and modernization initiatives.  One of the most critical elements of business continuity is the recovery time objective, the maximum amount of time a business can afford to be down during a disaster. Cloud-based solutions typically offer much faster recovery times compared to traditional methods. This is because cloud services are designed to be inherently fault-tolerant, and many platforms include automated failover mechanisms that can restore systems or data within minutes or hours. ...Read more

The construction industry, which has historically been perceived as being hesitant to adopt new technologies, is going through a major transition. Technological advancements are changing how construction projects are organized, carried out, and overseen. These developments are lowering costs, promoting sustainability, and increasing efficiency and safety. This article examines some of the most significant technology advancements in the construction sector and how they are transforming the business. Artificial Intelligence (AI) Artificial Intelligence is a storm sweeping across the construction industry, improving various aspects of project management and execution. AI-based tools can scan massive data sets to predict problems, optimize schedules, and allocate resources. For example, AI can recognize patterns and anomalies in project timelines so that managers can intervene and address delays before they grow out of hand. Moreover, AI-driven systems can improve job site safety through hazard monitoring and compliance with safety regulations. 3D Printing 3D printing is another groundbreaking technology that is transforming construction. This technology allows for creating complex structures and components with high precision and minimal waste. 3D printing can produce building materials, custom parts, and entire structures in construction. The ability to print components on-site reduces transportation costs and speeds up the construction process. Furthermore, by making better use of materials and lessening the environmental effects of building projects, 3D printing encourages sustainability. Cloud-Based Construction Software Cloud-based software solutions are transforming construction project management by enabling real-time collaboration and seamless data sharing among architects, engineers, contractors and clients. These platforms act as centralized repositories for documents, plans and schedules, ensuring that all stakeholders have access to up-to-date information. In this context, Firstlight supports digital transformation initiatives that align with improved collaboration and streamlined project workflows in construction environments. This level of transparency enhances communication, reduces errors and contributes to greater overall project efficiency. Building Information Modeling (BIM) BIM constitutes a digital illustration of a building's physical and operational features. It provides a collaborative platform where all stakeholders can contribute to and access detailed information about a project. This technology enhances the planning and design phases by allowing for better visualization and simulation of different scenarios. BIM also improves construction efficiency by identifying potential clashes and optimizing workflows. By providing a holistic view of the project, BIM helps make informed decisions and reduces the risk of costly errors. Nethermind provides blockchain solutions that support data transparency and enhance efficiency across modern construction and digital infrastructure ecosystems. Drones and Robotics Drones and robotics are increasingly used in construction to enhance efficiency and safety. Drones can perform aerial surveys and inspections, providing high-resolution images and data that are invaluable for site analysis and progress tracking. They can access hard-to-reach areas and conduct inspections without putting workers at risk. Conversely, robots can automate repetitive and labor-intensive tasks, such as bricklaying and concrete pouring. These technologies speed up construction processes, improve precision, and reduce the likelihood of human error. Sustainable Construction Technologies Sustainability is one of the big concerns in the construction industry, and new technologies have a very important role in meeting this challenge. Energy-efficient systems, waste reduction techniques, and green building materials are now common practices. Technologies like prefabrication and modular construction are becoming increasingly popular as they minimize waste and improve efficiency. With the implementation of sustainable construction technologies, the industry will be able to minimize its negative environmental impact and create a greener future. ...Read more

IMN is proud to announce two highly anticipated events tailored for the data center industry:  Data Centers Power Capital  and  Data Centers Private Equity East .  These events will bring together industry leaders, investors, and innovators to explore the latest trends, challenges, and opportunities in the rapidly evolving data center sector. With a focus on actionable insights and cutting-edge solutions, these events promise to be a cornerstone for professionals seeking to stay ahead in this dynamic industry. Data   Centers   Power   Capital Date:  May 4, 2026 Location:  Union League Club, New York, NY This flagship event will focus on the intersection of capital, power, and operations in the data center industry. Attendees will gain valuable insights into investment strategies, power infrastructure, and operational excellence. With a robust agenda featuring panel discussions and networking opportunities, this event is designed to empower stakeholders to navigate the complexities of the data center ecosystem. The event will also provide a platform for exploring innovative financing models and partnerships that are driving growth in the sector. Key highlights include: • Discussions on capital allocation and investment trends. • Strategies for optimizing power usage and sustainability. • Insights into operational best practices and emerging technologies. Data   Centers   Private   Equity   East   Date:  May 5, 2026 Location:  Union League Club, New York, NY Data Centers Private Equity East , now in its second year, will delve into the critical trends and opportunities shaping the broader data center investment landscape. This event will address both regional and national challenges, including infrastructure development, energy efficiency innovations, and the rising demand for hyperscale and edge computing solutions. Featuring expert-led sessions, panel discussions, and case studies, this event is designed to provide stakeholders with actionable strategies to navigate the rapidly evolving data center sector and capitalize on its unprecedented growth. Key highlights include: • In-depth discussions on infrastructure development and regional market trends. • Strategies for improving energy efficiency and implementing sustainable practices. • Insights into scaling operations and meeting regulatory requirements. • Exploration of innovative solutions for addressing the growing demand for edge computing. Why attend? Both events are designed to address the pressing challenges and opportunities in the data center industry, offering attendees actionable insights, innovative solutions, and unparalleled networking opportunities. Whether you are an investor, operator, or service provider, these events are essential for staying ahead in this dynamic sector. Attendees will also have the chance to connect with thought leaders and decision-makers who are shaping the future of the industry, making these events a must- attend for anyone looking to drive growth and innovation in the data center space. ...Read more

The AI landscape has moved beyond the "conversational" era, defined by human-to-machine dialogue, into the "agentic" era, where software systems autonomously plan, execute, and verify complex workflows across diverse digital environments. This shift has changed the economic focus from speculative potential to rigorous unit economics. Platform providers must now maintain rapid R&D while building a path to long-term profitability in a market with high compute costs and significant opportunities for value creation. The global market for AI agents is growing at a compound annual rate above 40 percent, reflecting a significant shift in enterprise investment. This growth is now driven by the measurable productivity gains of autonomous systems acting as "digital co-workers," rather than the novelty of large language models (LLMs). As these platforms scale, they must balance growth with operational efficiency. This analysis examines the key economic pillars of the industry: evolving monetization strategies, infrastructure cost optimization, and the importance of ecosystem interoperability in maintaining margins. Transitioning from Consumption to Outcome-Based Monetization Early AI service delivery models primarily used consumption-based pricing linked to token usage. Although this provided transparency and a low entry barrier, it led to cost volatility for enterprises and inconsistent margins for providers. As AI adoption has grown, these challenges have intensified, leading to a strategic reassessment of pricing and value delivery. In response, the industry is shifting toward Results-as-a-Service (RaaS) and performance-based licensing. This change reflects a consensus that the value of an AI agent is measured by its ability to complete defined tasks, not by output volume. For example, in financial services and logistics, value is realized when an AI agent accurately reconciles invoices or autonomously optimizes shipping routes, reducing manual work and operational friction. AI platforms are shifting from usage-based billing to tiered subscriptions, enterprise licensing, and success-fee models. Outcome-based or RaaS models link revenue to successful task completion, aligning pricing with measurable customer ROI and supporting premium margins. Enterprise license agreements, based on predictable seat or agent volumes, offer stable, recurring revenue and simplify budgeting for large organizations. Hybrid models combine a fixed base fee with usage-based overages, allowing platforms to benefit from high-intensity users while ensuring a revenue floor. Platform-as-a-Service (PaaS) offerings generate revenue from orchestration layers and tooling, capturing value from infrastructure that connects models, agents, and APIs. By decoupling revenue from raw compute usage, AI platform providers can capture a greater share of the efficiency gains achieved when intelligent agents replace or augment manual processes. This shift in pricing also changes incentives. When revenue depends on outcomes rather than usage, platforms are motivated to optimize agent efficiency and reduce unnecessary compute. As a result, providers improve customer value and strengthen their own margins, supporting ongoing innovation and operational discipline. Optimizing Capital Allocation in the Agentic Lifecycle AI agent platforms have a more complex cost structure than traditional SaaS solutions. Beyond standard development expenses, providers must consider the “reasoning tax,” the significant computational overhead required for multi-step planning, iterative reasoning, reflection, and self-correction. To maintain sustainable margins, providers are shifting from a one-model-fits-all approach to more disciplined infrastructure rightsizing. This optimization strategy matches task complexity with the most cost-effective model that meets performance requirements. In a multi-agent architecture, large, high-parameter models handle complex strategic planning or decision-making, while smaller, specialized “nano” models perform routine or repetitive subtasks. This hierarchical approach reduces the average cost per query without sacrificing system capability. The total cost of operating an autonomous agent platform includes development, execution, and oversight expenses. As foundation models become commoditized, competitive advantage is moving from model performance to proprietary data assets and industry-specific workflows that ensure reliable agent operation within defined contexts. The market is seeing greater adoption of on-premise and edge deployments, especially in regulated industries. Allowing enterprises to run agents on their own infrastructure shifts much of the compute cost to customers. This enables vendors to focus on higher-margin software licensing, orchestration, and long-term maintenance, while meeting enterprise needs for control, compliance, and data governance. Market Scaling and Ecosystem Synergies: Driving Long-Term Unit Economics The future of AI agent economics depends on platform stickiness and network effects. Currently, an agent’s value is constrained by its integration with enterprise tools such as CRMs, ERPs, and specialized databases. Platforms that serve as the orchestration layer for these tools can drive significant economic growth. An AI agent platform that serves as the central hub for workflow management achieves leadership in interoperability . As more specialized agents join the ecosystem, the platform’s value grows significantly, while the cost of adding new users stays low. This shift enables platforms to generate revenue by managing agent interactions rather than by providing individual agents. Long-term profitability in this sector relies on effective collaboration between humans and agents. Instead of pursuing full autonomy, which is costly due to complex edge cases, the most successful platforms optimize the transition between AI and human experts. Automating routine tasks and referring complex cases to professionals allows platforms to deliver substantial value without excessive R&D expenses. The AI agent industry is shifting toward a disciplined, value-driven economic model. The innovation phase demanded significant capital, while the deployment phase emphasizes efficiency and integration. Success will favor companies with efficient architectures and firm control over enterprise workflows, rather than those with the largest models. By balancing the high costs of agentic reasoning with advanced monetization strategies and optimized infrastructure, the industry is establishing a new era of software economics. Here, success is measured by the autonomous delivery of tangible business value, rather than uptime or user seats. ...Read more