Most operators have the models. Most have the data. What stops production deployment is the layer in between — the one that tells the agent what it is looking at, what it means, and what to do about it. That layer is AURA.
Active Network Intelligence (ANI) is what operators experience. AURA is what makes it possible.
Built in production. Proven at scale.
WHY AGENTIC AI STALLS IN PRODUCTION
Not model quality. Not data volume. The same four architectural gaps — every time.
Institutional knowledge has no home
Thresholds, escalation rules, domain-specific signal signatures — they live in your senior engineers, not in any system. An agent with no domain skill layer has no access to any of it.
Operational data is not in the data lake
The data lake has yesterday. Operations runs on live system pulls — CMTS telemetry, provisioning state, open tickets right now. An agent querying a data warehouse for a real-time decision is working from the wrong source.
Relationships need a graph, not a schema
A relational model tells you a subscriber has a modem. A graph tells you that modem is on a service group, that service group is on a node, and two other service groups on the same CMTS are degrading right now. Propagation and root cause are not answerable from a flat schema.
Raw data cannot leave the perimeter
Subscriber PII, network topology, live telemetry — security and compliance teams will not allow it in a cloud AI pipeline. Any architecture that requires raw data to move is a non-starter before the first meeting.
These are not data problems or model problems. They are architecture problems. And they have one solution.
"Without a Control Plane — the layer that knows what every experienced NOC engineer knows before they pick up the ticket — agentic AI is a fast, confident, well-dressed system that doesn't know what it doesn't know."
Governance tools give you a glossary. Agentic frameworks give you a workflow. Neither gives you that layer. AURA does.
THE DVSUM AURA DIFFERENCE
A subscriber calls about intermittent drops on Sunday evening. Your agent has 400 milliseconds to give the right answer. Here is what that requires.
That assembly — per query, in under a second, inside your perimeter — is the context bundle. AURA is the architecture that makes it possible.
THE ARCHITECTURE
Not a dictionary. Not a workflow engine. Not a data platform. All four — scoped, grounded, and assembled at runtime.
Metadata & Ontology
Domain knowledge and entity relationships.
Every signal located in your topology — subscriber to modem to service group to node to headend — with the semantic meaning of what each signal type means in your specific network environment.
Closes the context gap. The agent knows what the data means — in this domain, in this topology.
Domain Skills
Vertical-specific reasoning rules.
DOCSIS-native reasoning: pre-eq patterns, T3/T4 signatures, ingress noise profiles. Operator-configurable thresholds, escalation criteria, and automation boundaries. Reasons like an HFC engineer, not a generic model.
Closes the judgment gap. The agent reasons like an HFC engineer, not a generic model.
Data Collection Endpoints
Live pulls from production systems at runtime.
Real-time telemetry from your network monitoring platform, CMTS, CCAP, OLT — plus OSS/BSS across provisioning, CRM, billing, and ticketing. All processed at the edge. Nothing moves to the cloud.
Closes the data gap. Context without live data is metadata. AURA assembles both at runtime.
Closed Loop APIs
The action layer — decisions land in operational systems.
Network element actions, work orders, dispatch tickets, escalation payloads — confidence-gated against your defined automation boundary. The agent's answer does not end in a chat window.
Closes the integration gap. The agent's answer lands in an operational system, not a summary report.
SOVEREIGN BY ARCHITECTURE · PATENTED
Every operator evaluating AI at the network layer gets the same question from their security team: where does the data go? The answer with AURA is architectural, not procedural. Designed this way from the start — not retrofitted.
Your data stays in your network
Raw telemetry, subscriber records, network topology — processed at the edge inside your perimeter. Not a compliance policy. A patented architectural guarantee. The data never moves because the architecture never requires it to.
Edge Gateway
A lightweight process deploys inside your environment. LLM instructions flow in, execute directly against local data, structured results flow out. The model never sees raw subscriber data. The AI comes to the data — not the other way around.
Full Audit Control
Every data flow is inspectable. Every query is logged. Operator-configurable data retention. Designed for the operator who needs to show their security team exactly what moves and what does not.
THE BUILD-VS-BUY QUESTION
Production, not pilot
1.2M+ subscriber HFC network. 8+ source systems. Real alarms, real subscribers, real dispatch decisions.
13 weeks
From engagement to production at Liberty Latin America. Not a lab setup. A live network at scale.
8+ source systems
OSS, BSS, telemetry, CRM, ticketing — integrated and reasoning across all of them simultaneously.
AWS qualified
Evaluated at the infrastructure level. Not a badge — a technical statement.
The scaffold — LangChain, an LLM, some connectors — your team can build that. The domain cannot be assembled from open-source. The DOCSIS ontology, the reasoning rules, the confidence calibration tuned against a 1.2M subscriber live network — that took three years and real operational consequences to get right. You are not buying a platform. You are buying the three years and the production proof.
WHERE EVERY AI PROJECT STALLS
This is not a criticism. It is an architectural reality. A context bundle requires all four simultaneously — and every vendor in your current AI stack is built to own one lane.
"Governance tools give you a glossary. Agentic frameworks give you a workflow. Neither gives you that layer."
Context
Ontology, metadata, semantic meaning.
Lane stops at: design-time knowledge. The agent knows what the data means. It cannot query it.
Data
Live data collection, runtime access, source system connectors.
Lane stops at: the data layer. No domain reasoning. No action.
Orchestration
Agentic workflow, tool calling, LLM coordination.
Lane stops at: execution scaffolding. Without grounded context, every tool call is a guess.
Action Integration
Decisions landing in operational systems — workflows, network elements, ticketing.
Lane stops at: generic horizontal workflow. No domain intelligence. Telco is a use case, not a native environment.
COMMON QUESTIONS
The connectors for OSS/BSS systems — provisioning, CRM, billing, ticketing — and network telemetry sources are pre-built for telco. Your team configures, not builds. There is no blank-slate onboarding. Liberty Latin America went from engagement to production in 13 weeks with 8+ source systems and sovereign data requirements.
The automation boundary is operator-configured. You define which actions require human confirmation and which are autonomous. The confidence threshold, escalation criteria, and action types are all in your control. AURA enforces the boundary — it does not override it.
Liberty Latin America went from engagement to production in 13 weeks. The DOCSIS ontology and domain skills are pre-built — you configure them to your environment, you do not build from scratch. The deployment architecture is as designed as the platform itself.
The scaffold, yes. The domain, no. The DOCSIS ontology, the reasoning rules, the confidence calibration against a live 1.2M subscriber network — that took three years and real operational consequences to get right. Your team would spend that time building the Control Plane instead of running the network.
Pricing is based on operator size and deployment scope. The right conversation is the architecture call — we will walk through the integration against your environment and show you the investment alongside the savings model.
NEXT STEP
We will walk through the integration against your specific environment — your OSS/BSS stack, your network telemetry sources, your data perimeter requirements. No generic demo. A technical session with your team and ours.