INTEGRATED AI PLATFORM

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-premises capacity model.

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-premises on operator GPU infrastructure. The SLM Builder integrates with Red Hat AI’s MLOps and model registry pipelines.

Mavenir’s Digital Enablement (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 itemised records, and integrates with operator BSS and mediation systems. Hard caps, threshold alerts, and anomaly detection protect both operators and subscribers from runaway AI spend.

Closed-loop service assurance monitors AI service health with full SLA awareness, distinguishing business-critical AI inference from background workloads and prioritising resources accordingly. Automated fault detection, predictive remediation, and SLA management ensure operators can commit contractual service levels for their operator-managed AI offerings.

Intent-based policy management spans model behavior, network QoS, and compute allocation from a single control plane. Operators define high-level intent, such as priority model routing with guaranteed latency for premium plan subscribers, and the platform translates that intent into coordinated policy across the AI gateway, model router, network slice, and charging system. Policy updates propagate in real time without manual reconfiguration.

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. Operators configure which workloads may reach external models, which subscriber tiers are eligible, and what cost thresholds apply. Compliance with data sovereignty, GDPR, and national AI governance frameworks is maintained through configurable data residency controls.

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. Built-in guardrails are enforced at the model gateway, with continuous monitoring of agent behavior and threat detection tuned to adversarial prompt patterns and model extraction attempts.