Teldat Cognitive SD-WAN
The network that understands people. Intelligent AI-driven management that turns complexity into natural conversations, delivering total observability and predictive automation.
A network that speaks, understands, and advises
Teldat Cognitive SD-WAN transforms network management by delivering the most observable, understandable, and actionable network: efficient operation, a resilient network, precise security, and a user experience built on natural interaction:
- Interaction with the network through natural language via advanced LLM models.
- Unique observability across WAN, 5G, and WLAN with deep analysis of the radio medium and coverage.
- Intelligent automation of configurations, deployments, and policy adjustments.
- Predictive analytics that detects problems before they impact end users.
- MCP server-based architecture that integrates CNM, be.Safe Pro, be.Safe XDR, and third parties via API.
Market vision – AI redefines network management
Organizations face an unprecedented challenge in managing network infrastructures. The volume of data from hybrid networks grows exponentially, but the ability to turn it into actionable information does not keep pace. Companies struggle with data overload from multiple sources: WAN, LAN, WLAN, 5G, and cybersecurity, lacking tools capable of effectively correlating this complexity.
The shortage of specialized talent compounds the problem. Organizations need to upgrade infrastructures for digital transformation, AI, and IoT initiatives, but lack of qualified personnel, the time and tools to manually analyze thousands of events, identify patterns, prevent problems, and optimize critical applications.
This reactive model is costly and inefficient. Problems impact users before IT detects the root cause and applies solutions. Experience degrades, productivity drops, and reputation suffers the consequences.
Artificial Intelligence (AI) represents the necessary transformation. AI investments are growing significantly, driven by demonstrable results. AI-powered observability platforms drastically reduce alerts and significantly decrease operational incidents. The convergence of SD-WAN, observability, and Artificial Intelligence (AI) is the answer to the challenge of managing complex enterprise networks efficiently, proactively, and cost-effectively.
Key Benefits of Cognitive SD-WAN
Unique WAN, 5G, and WLAN observability
Complete observability across WAN, 5G, and WLAN: low-level telemetry, signal quality, interference, handovers, and radio diagnostics with AI-Radio on APs. AI correlates data from all domains for comprehensive proactive diagnosis.
Complete Native NAC Integration
Native connection with Forescout, Cisco ISE, ClearPass, OpenNAC, and be.Active (Teldat’s own user identification). Real-time knowledge of devices, users, policies, and authorization that enriches security and troubleshooting.
User experience agents
Agents that can be installed on endpoints to measure per-application latency, connectivity, and protocol performance. They feed AI models for optimizations focused on the end-user experience, not just infrastructure metrics.
Open MCP Server and Natural Interaction
MCP server that connects LLM models with CNM, be.Safe Pro, be.Safe XDR, and any external system via API. Natural-language interaction to configure, diagnose, and optimize the network without complex interfaces.
Understanding Cognitive SD-WAN – AI-Powered Intelligent Networks
Cognitive SD-WAN represents the evolution of software-defined networks toward intelligent, autonomous architectures. It integrates Artificial Intelligence AI and Machine Learning directly into the infrastructure, transforming how organizations manage, monitor, and optimize their connectivity environments.
MCP-based cognitive architecture
The core of this transformation lies in the integration of an MCP (Model Context Protocol) server, an intelligent bridge between LLM models such as ChatGPT, Copilot, or Gemini and the critical components of the SD-WAN infrastructure. It communicates bidirectionally with management, security, and analytics platforms, creating an ecosystem where AI accesses real-time data, interprets complex contexts, and executes coordinated actions.
Users interact with the network in natural language. Instead of navigating multiple consoles or interpreting cluttered dashboards, they ask questions such as “What caused the video-conferencing degradation yesterday?” or give instructions like “Optimize routing for financial applications during business hours”. The MCP server translates these queries, accesses the relevant APIs, correlates information, and returns contextualized answers.
Multidimensional observability
Observability in cognitive networks goes beyond traditional monitoring: it correlates data from multiple domains to understand not only what is happening, but why and what might happen. Traditional SD-WANs offer WAN visibility but lack context about underlying causes.
A cognitive network integrates WAN telemetry (latency, packet loss, jitter), NetFlow/sFlow traffic analysis, security logs, 5G telemetry (signal, handovers, interference), WLAN diagnostics (saturation, client density, roaming), NAC data on devices and users, and experience probes from endpoints.
AI processes these heterogeneous streams, identifying correlations that are impossible to detect manually: application degradation coinciding with 5G interference in specific bands, or latency spikes correlated with unauthorized changes to firewall policies.
From reactive to predictive
Cognitive networks use predictive models trained on historical data to anticipate problems before they impact users. They predict bandwidth needs based on trends and seasonality, suggest preventive changes when early degradation appears, and identify sites that will require upgrades.
This capability extends to security: detecting subtle anomalies that precede attacks, identifying deviations in user behavior, and correlating disconnected events that reveal advanced persistent threats.
Intelligent automation
Cognitive networks act autonomously under human supervision: they generate configurations for new sites, apply temporary policies during critical events, adjust SLA thresholds, deploy security measures against threats, and run automated troubleshooting to resolve common incidents without human intervention.
Cognitive SD-WAN: Teldat Products & Solutions
Teldat Cognitive SD-WAN solutions
Teldat’s Cognitive SD-WAN solution combines four decades of experience in mission-critical networks with advanced Artificial Intelligence AI, creating a unique platform that integrates SD-WAN management, security, and deep observability under a unified cognitive paradigm.
Solution architecture
At its heart is Teldat’s MCP (Model Context Protocol) server, an intelligent orchestrator between the customer’s LLM models and three technological pillars:
Cloud Net Manager (CNM): Comprehensive network management with modules for operation, monitoring, template-based bulk configuration, and SD-WAN control with a global data model. The primary source of observability, with detailed WAN, 5G, and WLAN telemetry.
be.Safe Pro: Centralized security with a next-generation firewall, microsegmentation, and application filtering. It receives events via Syslog, correlating threats in real time with network events.
be.Safe XDR: Traffic analysis through NetFlow and sFlow from routers, switches, and APs. Granular visibility into applications, users, and anomalous behavior for predictive analytics.
The MCP server exposes standardized APIs that allow LLMs such as ChatGPT, Copilot, or Gemini to access these systems in a unified way, translating natural-language intentions into coordinated technical actions.
Key competitive differentiators
Unique 5G Observability: Proprietary 5G hardware and software enable low-level telemetry that competitors cannot reach: RSRP/RSRQ signal quality, interference, handovers, coverage, and radio diagnostics. AI prevents 5G connectivity problems before they impact critical applications.
Advanced WLAN Diagnostics: Native integration with Teldat Wi-Fi and AI-Radio: visibility into saturation, interference, client density, signal quality, roaming, dynamic per-user QoS based on the radio channel, detection of unauthorized APs, and automatic compensation for coverage gaps. AI correlates WLAN data with application events for proactive diagnosis.
Open third-party integration: The MCP architecture connects with any external platform that exposes APIs, enriching observability with external sources and adapting to the customer’s existing infrastructure.
Complete NAC integration: Native compatibility with Forescout, Cisco ISE, ClearPass, OpenNAC, and be.Active (Teldat’s own user identification). Real-time knowledge of devices, users, policies, and authorization that enriches security and troubleshooting.
User experience (UX) agents: Endpoint agents that measure per-application latency, connectivity, and protocol performance. They feed AI for optimizations focused on the real end-user experience.
Integrated AI capabilities
Two modes: a default recommendation mode, in which AI analyzes data, detects anomalies, suggests optimizations, and proposes corrections for administrator approval. And a supervised action mode: automatic configurations for new sites, temporary policies, dynamic SLA adjustments, and security measures against threats, within limits defined by the customer.
Cognitive SD-WAN – Use Cases
Conversational AI troubleshooting
Automated diagnosis of complex problems through natural language, correlating multiple data sources.
Mass deployment automation
Automated provisioning of dozens of new sites through common-language instructions, reducing time and errors.
Predictive performance optimization
Anticipating capacity needs and preventing problems through AI-based predictive analytics.
Conversational AI troubleshooting
Automated diagnosis of complex problems through natural language, correlating multiple data sources.
Challenge
Organizations with distributed networks face the challenge of diagnosing complex problems that affect critical applications. When users report service degradation such as video conferencing or SaaS applications, IT teams must manually analyze multiple systems: review WAN performance metrics, check security logs, query the status of 5G or WLAN connections, and temporally correlate this data to identify the root cause. This process consumes hours or days of specialized work, during which the user experience remains degraded. The complexity increases when the problem involves subtle cross-domain interactions, such as 5G interference impacting specific SD-WAN tunnels for certain types of traffic.
Solution
With Cognitive SD-WAN, the administrator simply asks in a common language: “What caused quality problems in Microsoft Teams at the Madrid office yesterday between 2:00 and 4:00 PM?”. The MCP server accesses CNM, be.Safe XDR, and be.Safe Pro, automatically correlating SLA probes, security events, 5G/WLAN telemetry, application traffic, and NAC system logs. In seconds, it returns a complete analysis: “Detected a latency increase in the primary SD-WAN tunnel coinciding with 5G signal degradation in cell XYZ due to interference. Teams traffic did not migrate to the secondary tunnel because of a misconfigured SLA threshold. Recommendation: adjust the failover threshold from 100ms to 60ms”.
Why Teldat?
Teldat is the only solution on the market that integrates deep 5G and WLAN observability with SD-WAN and security in a unified cognitive platform. The ability to diagnose 5G/Wi-Fi radio-medium problems and correlate them with application events is exclusive to Teldat.
Mass deployment automation
Automated provisioning of dozens of new sites through common-language instructions, reducing time and errors.
Challenge
Companies in expansion need to rapidly deploy new sites while maintaining consistency of configuration, security policies, and operational standards. The traditional process requires engineers to manually create configurations for each site, considering location type, required services, applicable policies, integration with corporate systems, and compliance requirements. This involves weeks of specialized work, a high probability of human error, inconsistencies between similar sites, and difficulty scaling when deployments of dozens of locations are required. Documentation becomes complex and hard to maintain, complicating future modifications or problem troubleshooting.
Solution
The administrator states: “Deploy 50 retail offices following the standard mid-size store profile, with primary 5G connectivity, a backup line, segregated Wi-Fi for customers and guests, and retail security policies with PCI-DSS”. The MCP server accesses templates in CNM, automatically generates individualized configurations per site considering location and characteristics, provisions policies in be.Safe Pro, configures monitoring rules in be.Safe XDR, and schedules the Zero Touch Provisioning deployment. A process that would take weeks runs in minutes with guaranteed consistency.
Why Teldat?
The integration of the MCP server with Cloud Net Manager enables end-to-end automation that goes beyond SD-WAN configuration, including security, monitoring, and compliance. Templates based on Teldat’s global data model guarantee a consistency impossible to achieve with manual approaches.
Predictive performance optimization
Anticipating capacity needs and preventing problems through AI-based predictive analytics.
Challenge
Organizations need to plan network capacity proactively to avoid saturation during seasonal peaks, special events, or business growth. Traditional planning relies on manual analysis of historical trends and linear projections that are frequently inaccurate. When capacity turns out to be insufficient, the user experience degrades suddenly, impacting business-critical applications, generating massive support tickets, and requiring costly emergency upgrades with long lead times. Over-provisioning capacity generates significant unnecessary costs. Identifying which sites will require expansions, and when, requires detailed analysis of usage patterns and seasonality.
Solution
The administrator asks: “Which sites will need capacity expansion in the next six months?”. AI analyzes traffic history in be.Safe XDR, identifies growth patterns by site and application, correlates with business events, detects seasonality, and projects needs. It responds: “Barcelona, Seville, and Valencia will reach 85% utilization in April due to the CRM application launch. Recommended upgrade from 100 to 200 Mbps. Twelve retail sites will require reinforcement during the summer campaign”. This enables proactive planning months in advance, avoiding emergencies and optimizing costs.
Why Teldat?
The combination of be.Safe XDR for granular analysis of historical traffic, integration with LLM models for common-language processing, and the ability to correlate business events with technical patterns delivers predictive capability unique in the market that goes beyond simple trend extrapolation.
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