Autonomous Networks Driving Your Business Forward

Early identification of network deviations based on AI-driven comparison of real-time and expected parameters

Automated root-cause analysis, gathering the full context of an incident or detected anomaly from available logs, TMF open APIs, and domain agents

Real-time execution, validation, and enforcement of network changes triggered by business intents and agent outputs

Continuous learning from optimization outcomes to enrich AI decision-making and expand the internal knowledge base

Secure the most sensitive part — the decision-making — based on complete insight and trusted data. Enable execution with a “human-in-the-loop” option when required, or in cases of missing expert knowledge, trusted data, or a viable solution

Autonomous network supervision with proactive remediation and minimal human involvement. Provides complete traceability, observability, and documentation for every step of the awareness, analysis, decision, and execution phase

AI-generated suggestions based on supplier documentation, historical data, and service performance models, with self-learning from planned and executed cases, as well as from expert knowledge stored as text

Task planning and coordination for human interventions requiring physical access, with task completion confirmed by automated control loops. planning and coordination for human interventions requiring physical access, with the confirmation of task completion handled by automated control loops

Using an LLM for automated interpretation of new service requests to build awareness of the goal and assess the impact on configuration changes based on network-as-a-service models

Generation of plans, request pipelines, and control loops for agents and API calls

Coordination and execution of conversational confirmations (for scope and quotations) and service delivery processes across technical, operational, and partner domains

Autonomous service activation — including testing, monitoring, and billing — through standardized APIs and defined control loops

Real-time updates to inventory systems reflecting service deployment and configuration status, with a digital twin function to detect potential collisions before changes are uploaded to the network

Automatic notification of service launches via APIs, delivered in a human-readable format across internal systems and external partner environments

Converting customer vertical-specific intents into network design and technical intents

Eliminating the need for additional experts to manage private 5G networks at scale

Autonomous, intent-driven orchestration tailored even for small businesses

Learning vertical-specific needs to bridge the knowledge gap between industries and network operations

From customer engagement and IoT Connect to network operations management

rApp/xApp intelligence enhancing RAN performance to meet SME requirements

AI on the edge powering vertical-specific applications

Identification of service usage trends based on time of day, week, season, and other influencing factors

Recognition of recurring, high-impact performance shifts across virtual and physical network elements

Intellingent na multi-criteria optimization of service delivery through alternative resources to reduce cost, emissions, and latency

Evaluation and merging of underutilized assets while maintaining performance and SLA compliance

Repurposing freed resources to support new services or deeper network consolidation initiatives

Timed, policy-driven configuration changes triggered by predictable seasonal or usage-based variations.

Autonomous implementation of optimization tasks via atomic orders and automated system updates.

Continuous learning from optimization outcomes, enriching AI decision-making and the internal knowledge base