Integrated AI Platform Capabilities
*Blog by Bejoy Pankajakshan, EVP, Chief Technology and Strategy Officer, Mavenir
Key Platform Capabilities
Model Router (LLM Orchestrator): Routes inference requests to the optimal model based on policy, cost, latency, and task complexity. The Model Router operates across three model tiers: on-premises SLMs built and fine-tuned by the operator for routine workloads; open-source foundation models running on operator GPU infrastructure for general-purpose tasks; and cloud frontier models for tasks that require advanced reasoning, multimodal capability, or specialist knowledge. Routing decisions are governed by operator-defined policies per plan tier, per subscriber segment and per workload type. Frontier model access is metered through the same token charging infrastructure as on-premises inference, providing unified cost visibility and billing regardless of where the model runs. The Model Router eliminates uncontrolled cloud AI spend while preserving access to frontier capability where it genuinely adds value.
Token Optimizer: Reduces token consumption before LLM calls through context pruning, log compression, cache alignment, and metadata elimination. Operators deploying AI services at consumer scale require predictable GPU economics. The Token Optimizer converts variable per-token cloud cost into a manageable on-prem capacity model.
SLM Builder: Builds fine-tuned Small Language Models from operator data, including requirements agents, customer service models, network operations assistants, and domain-specific reasoning models. SLMs run entirely on-prem on operator GPU infrastructure, eliminating per-seat and per-token cloud licensing at scale. The SLM Builder integrates with Red Hat AI’s MLOps and model registry pipelines.
Token Charging via Mavenir Digital Enablement (MDE): Mavenir’s Data Engine (MDE) provides the token metering, charging, and billing integration layer for operator AI monetization. MDE counts input and output tokens with billing-grade accuracy, enforces plan quotas and overage rules, generates per-session and per-department itemized records, and integrates with operator BSS and mediation systems. MDE enables operators to bill AI consumption on the phone bill, exactly as data plans are billed today. Hard caps, threshold alerts, and anomaly detection protect both operators and subscribers from runaway AI spend.
Service Assurance for AI Workloads: Closed-loop service assurance monitors AI service health with full SLA awareness, distinguishing business-critical AI inference from background workloads and prioritizing resources accordingly. Automated fault detection, predictive remediation, and SLA management ensure operators can commit contractual service levels for their own operator-managed AI offerings. For enterprise customers developing their own applications on the platform, or for third-party workloads hosted in AI Grid deployments, service assurance governs platform availability and resource guarantees; application-level SLAs are the responsibility of the application owner unless the operator explicitly offers the application as a managed service.
AI Policy Optimization: Intent-based policy management spans model behavior, network QoS and compute allocation from a single control plane. Operators define high-level intent (“premium plan subscribers receive priority model routing with <200ms guaranteed latency”) and the platform translates intent into coordinated policy across the AI gateway, model router, network slice and charging system. Policy updates propagate in real time without manual reconfiguration across Platform layers.
Sovereign-First, Hybrid-Ready Architecture: The platform runs on operator-controlled infrastructure as its default and primary deployment model. Prompts, model weights, and subscriber data remain within the operator’s network boundary under normal operations. Where specific use cases require frontier model capability, the Model Router provides policy-governed, encrypted connectivity to external model APIs. Operators configure which workloads may reach external models, which subscriber tiers are eligible and what cost thresholds apply. All external model calls are metered and billed through MDE on the same token charging infrastructure. Compliance with data sovereignty, GDPR and national AI governance frameworks is maintained through configurable data residency controls, not through a closed-by-default architecture.
AI Security and Zero Trust: The platform provides Identity, Authentication, Trust, and Authorization services, establishing a Zero Trust foundation for AI applications and agents. Verifiable identities, strong authentication, delegated authority, and policy-based access control ensure that every model, agent, tool and data source operates within explicitly defined permissions. The AI Security layer adds comprehensive protection against AI-specific threats: secure access controls for tools and external APIs, built-in guardrails enforced at the model gateway, continuous monitoring of agent behavior, threat detection tuned to adversarial prompt patterns and model extraction attempts, and governance controls that maintain audit trails for every AI interaction. Together, the Zero Trust and security layers allow operators to deploy and scale agentic AI solutions with confidence, meeting the compliance and risk requirements of regulated communications environments without constraining operational agility.
Related Press Release: Mavenir Collaborates With Red Hat to Launch Integrated AI Platform to Turn Operators Into AI Service Providers
Read the First Blog in This Series: How Operators Can Turn AI Tokens Into a Revenue Business
The platform is designed to deliver measurable operator outcomes: new AI revenue streams through bundled token plans from basic consumer tiers to unlimited enterprise quotas; predictable AI economics by shifting the majority of traffic to on-premises models with hard spend caps on any frontier model usage; data sovereignty by design, with all subscriber data and model weights remaining on operator infrastructure under normal operations; contractual SLAs for operator-managed AI services backed by closed-loop service assurance, with platform-level availability guarantees extending to enterprise and AI Grid workloads; and a rapid path to revenue through pre-validated deployment playbooks, with Mavenir’s own production deployment serving as the reference architecture; and enterprise-grade security through Zero Trust identity controls and AI-specific threat protection that meet the compliance requirements of regulated communications environments.
