At the core of Otter RAN is a Telecom Foundation Model — a frontier LLM re-trained on the language of networks: alarms, counters, configurations, and the 3GPP / O-RAN specifications themselves. On top of it, agents that operate the network: NOC Copilot for incident diagnosis, Agentic SMO for Open RAN orchestration, and RAN Config Audit for the DU and RU. Every deployment feeds the model. The model sharpens every product.
Ingests alarms from Netcool, NetAct, ENM, iManager, and any SNMP source. ML correlation replaces 10,000 hand-coded rules — root cause in under 90 seconds, vendor-agnostic, no OSS rip-and-replace.
An O-RAN-aligned Service Management & Orchestration layer driven by AI agents. Manages rApp and xApp lifecycles, enforces SLAs, and optimises energy — from operator intent to RAN action, automatically.
The same foundation model, pointed at the radio's configuration — read-only. It parses vendor YANG models natively and checks live DU / RU config against operator intent, 3GPP / O-RAN specs, and vendor best practice, flagging drift and misconfigurations before they become outages. No write access; audit and flag only.
General-purpose LLMs don't speak network. Otter RAN starts from a frontier model and re-trains it on telecom — alarm streams, performance counters, configuration data, vendor documentation, and the 3GPP / O-RAN specifications. Every product is an agentic surface over that one model, so each deployment makes the whole platform smarter.
An LLM post-trained on the language of networks: alarms, counters, YANG models, CLI output, and the standards that define them. It reads a fault report the way a senior RAN engineer does — because it was trained on the same material, at scale.
Agents built on the model act inside operator-defined intents and guardrails: read-mostly by default, every action policy-checked, every decision auditable. Autonomy you can widen gradually — from recommendations, to approvals, to closed loop.
NOC Copilot, Agentic SMO, and the RAN configuration domain share one model and one data layer. An incident diagnosed in the NOC informs orchestration decisions; a config change pushed by the SMO updates the Copilot's baseline. Each surface makes the others better.
A single AI-caused incident can end an automation program. So the platform is built so that can't happen: every product starts read-only, every action is policy-checked and logged with its rationale and a rollback plan, and you decide — per domain — when an agent earns the next rung.
The platform ingests alarms, counters, and configuration with zero write access. It builds its model of your network and proves its understanding before it's allowed to suggest anything.
Ranked diagnoses and proposed actions, each with evidence, confidence, and expected impact. Your engineers stay in full control — the platform earns credibility one recommendation at a time.
Agents prepare the complete action: the change, the guardrail checks it passed, and the rollback plan. A human approves; the platform executes and verifies the outcome against its own prediction.
In domains where trust is earned — cell sleep schedules, known remediation patterns — agents act autonomously within hard guardrails, with automatic rollback the moment reality diverges from the plan.
The platform is a read-mostly, additive intelligence layer. It augments existing infrastructure rather than replacing it — designed so a proof of concept can run on your historical data in weeks, not quarters.
Most AIOps and SMO platforms started life in cross-industry IT operations and were adapted for telecom. Otter RAN starts from the other end: a foundation model trained for the RAN, with products built on top of it.
We're partnering with a small number of operators and O-RAN vendors as design partners. Bring your historical alarm and configuration data; get early access, direct influence on the roadmap, and first results on your own network — no OSS replacement, no long-term commitment.