Unryo Agentic AI Assistant

Unryo addresses the risk of data leaving the operator's controlled network in two ways: ingestion over duplication, and a model-agnostic AI engine.

MONTREAL, QC, CANADA, June 25, 2026 /EINPresswire.com/ -- As network operators add agentic, LLM-driven AI to their observability stacks, most platforms force a trade-off regulated operators cannot accept: they run as SaaS that copies network telemetry into a vendor-controlled cloud and lock operators into a single proprietary model.

Unryo, an agentic AI platform built for network operations, removes that trade-off. It deploys inside the operator's own environment, runs against whichever LLM the operator chooses, and queries telemetry in place rather than copying it out, giving operators AI-led diagnosis without losing control of their network or their stack

"Operators should not have to choose between using AI and keeping control of their network data. A private inference architecture gives them both, because the AI runs where the operator's data already lives, not the other way around." said Michel Foix, Co-founder and CTO, Unryo.

Deployment under the operator's control

Unryo deploys where the operator needs it: on-premises on Kubernetes, in a private cloud, or in a hybrid configuration. The full agentic stack, including the reasoning engine, can run inside the operator's own network boundary, following their data-residency and audit requirements rather than a vendor's hosted service.

A model-agnostic AI engine, not a single locked-in model

Recent incidents have shown that access to a single AI model can be suspended or have its terms changed without warning, for reasons entirely outside an operator's control, even for widely deployed commercial models. Most AI observability platforms tie operators to one vendor-controlled model.
Unryo's engine is model-agnostic: operators can run it against a self-hosted open-source model, an LLM they already operate, or a private, dedicated instance on a service such as AWS Bedrock or Azure OpenAI Service. The self-hosted path keeps inference entirely in-network; the private cloud path keeps it in an isolated instance rather than a shared SaaS endpoint. Either way, the operator decides where inference runs and which model performs it.

No second copy of the network's data

Most AI platforms copy telemetry into a separate store before analyzing it, adding another place sensitive data lives. Unryo ingests directly from the operator's existing sources, queries it live, and retains only what historical analysis requires. Operators get the same cross-domain visibility without the extra copy.

Beneath the AI, a live source of truth

Unryo's foundation is its topology, a live map of how every part of the network connects to every other part. When the AI agent investigates a problem, it starts from that full picture instead of searching blindly. For regulated industries like telecom, where every AI-driven conclusion has to hold up to audit, that grounding is what makes a result verifiable rather than merely plausible.

"The model is replaceable, but the topology underneath it is not. That live map is what lets our agent start with the full picture instead of guessing, and it is what makes its answers something an operator can actually verify." said Nicolas Souty, Co-founder and CEO, Unryo.

About Unryo

Unryo is a full-stack observability and agentic AI platform built for network operators. Its Topology Data Fabric automatically discovers and maps multi-layer dependencies across physical, logical, and application infrastructure, giving AI agents the context to diagnose issues and drive resolution without relying on manual correlation. Unryo is available on-premise, in the cloud, and in hybrid configurations.

Nicolas Souty
Unryo Inc.
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