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How Sella Delivers Scalable Credit Risk Explanations with Prometheux
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By Industry / Telecommunications

One End-to-End Infrastructure.
Autonomous Networks.

Autonomous networks run on knowledge, not more dashboards. Prometheux turns your network, OSS and BSS data and your engineers' expertise into rules that run where the data lives, so systems and agents can diagnose and decide from consistent, traceable results: same data, same rules, same result. Then reuse it to cut cost, protect revenue and grow customers. No rip-and-replace, value in weeks.

Proven in production

Why telco AI stalls

Automation Runs Scripts.
Autonomy Needs Knowledge.

Operators want networks that run closer to autonomy and AI that cuts cost and grows revenue. But network operations, customer care, marketing, finance and security each work from their own systems, automation only covers scenarios someone already scripted, and AI pilots give answers nobody can check, so they never reach production.

Common limitations of today's telco stacks
With Prometheux
Network, care, marketing, finance and security data split across vendors and teams
One model of network, services, customers and contracts that every team and agent shares
Every new use case starts with copying data into another platform
Network, OSS and BSS data processed in place, where it already lives: nothing to copy or migrate
Automation only covers scenarios someone has already scripted
Reuse the same rules across new combinations of modeled entities and dependencies
AI pilots give different answers to the same question, so nobody lets them act
Agents call deterministic rules over MCP: same input, same answer, with the derivation behind it
Answers nobody can trace, so every decision is explained by hand
Every result comes with its rules and source rows, for engineers, auditors and regulators
LLM costs grow with every query, including the ones that are really logic
Logic runs on your own compute; language models are used only where language is needed
Autonomous networks

Built for
Level 4

TM Forum's Level 4 blueprint describes autonomous networks that make cross-domain decisions based on knowledge and inference, with people supervising rather than executing. Most operators have automated the scenarios they already know and stalled at Level 3. The missing piece is a knowledge plane: one operator-owned model of the network and its rules that systems and agents can reason over, with every decision traceable.

Sense

Alarms, telemetry, inventory, topology and change records are read in place, across vendors, domains and clouds. Rules re-run on a schedule or when the data changes.

Understand

One network and service model ties physical, logical and service layers together, so rules can trace modeled dependencies at any depth, from a cooling fault to the services and customers potentially affected.

Decide

Root cause, blast radius, capacity and change-safety checks run as deterministic rules: the same input data and rule version produce consistent diagnostic results, with a traceable explanation.

Act

Agents and automation call those rules over MCP and the API. Actions run through your existing orchestration and change processes, with the evidence attached, so engineers supervise instead of executing.

What this means for you ⟶

Plan your path to Level 4

Move from scripted automation to knowledge-based decisions. One knowledge plane across vendors, owned by you. Closed loops your engineers can supervise and audit.

Use case menu

Where Telco Teams
Start

Most operators begin with autonomous network operations, then reuse the same knowledge plane for customer, fraud and business teams, so each new use case starts further ahead.

Autonomous Network Operations

Move network operations from scripted automation toward Level 4: diagnose, predict and decide across domains on one knowledge plane.

01Faster Fault Diagnosis
Which element is behind this alarm storm, and what changed on it first? Identify root causes by correlating alarms, dependencies and recent changes, with the evidence attached.Recursion →
02Predictive Maintenance on Network Assets
Which servers, racks and sites are trending towards temperature, disk or capacity limits, and when? Python steps project the trend on a schedule.Deterministic AI →
03Capacity Planning and Investment
Where is capacity running short end to end for each service, and which link or port is the constraint?Graph Analytics without a Graph Database →
04Fewer Tickets, Fewer Truck Rolls
Which open tickets trace back to the same failing element, so one fix and one field visit closes them all?Graph Traversal →
05Configuration Checked Against Standards
Which device configurations break your engineering standards? Checked on a schedule and whenever a config changes.AI Governance →
06NOC Assistants and Agents
Engineers and agents ask the same rules over MCP and get the same answer with its derivation, before any action goes through your change process.Agentic Workflows →

Customer Experience, Consumer and Business

Grow and keep consumer and business customers with offers and actions every channel can explain.

01Customer 360 Across Systems
Which billing accounts, CRM records and contracts, across brands or merged operators, belong to the same customer? Every match cites its rule.Entity Resolution →
02Orders Stuck Before Activation
Which new orders are stuck between ordering, provisioning and billing, and at which step? Checked on a schedule across the systems involved.Cross-Database Processing →
03Personalised Plans and Bundles
Which plan or bundle fits each customer's usage, devices and contract under your eligibility and pricing rules, and why?Semantic Layer →
04Churn Signals From Network and Billing
Which subscribers saw repeated service degradation and then a complaint, downgrade or billing dispute? Network quality joined to BSS data in place.Cross-Database Processing →
05Proactive Care Before the Call
When an incident is raised, which customers are affected, so care can reach them first? Triggered when the incident data changes.Agentic Workflows →
06SLA Breaches Before Penalties
Which enterprise services or slices are trending towards an SLA breach, and which contracts and penalty terms apply?Recursion →

Fraud, Risk and Compliance

Stop fraud early and answer auditors and regulators with lineage.

01SIM Swap Takeover Signals
Which SIM swaps were followed, within the window you set, by a password reset, port-out request or new payment method on the same account?Deterministic AI →
02Subscription and Identity Fraud
Which new accounts share identity documents, payment details or contact data with accounts already flagged for fraud?Graph Analytics without a Graph Database →
03IoT SIMs Outside Their Profile
Which IoT and machine SIMs use services outside their contracted profile, such as voice or roaming on a data-only plan?Deterministic AI →
04Customer Data Use Within Policy
Is each use of customer data covered by the consent and purpose your policies require? Checked by rules, with the record behind each answer.AI Governance →
05Regulatory Reporting With Lineage
Which figures go into each regulatory return, computed by your rules, with lineage from every figure back to its source rows?Data Lineage →

Business Operations

Close revenue gaps and run the back office on the same rules as the network.

01Revenue Leakage Between Network and Billing
Which active services on the network have no matching billing record, or are billed on the wrong rate plan?Cross-Database Processing →
02Interconnect and Roaming Reconciliation
Do interconnect, roaming and wholesale records agree with partner invoices, and which records explain each gap?In-Place Data Processing →
03Quoting and Serviceability Checks
Can this customer site be served at the bandwidth requested, over which path, under your product and engineering rules?Graph Analytics without a Graph Database →
04Network Energy Use per Site
Which sites use the most energy for the traffic they carry, and which equipment and configuration explain it?Cross-Database Processing →
05Enterprise Service Impact
When a link or site degrades, which enterprise customers, sites and services are affected, in which SLA tier?Graph Traversal →
Where connected data matters

The Questions Other
Tools Can't Answer

Assurance

Root Cause Under an Alarm Storm

Which single upstream element explains the largest share of the last ten minutes' alarms, and what was the earliest change on it? A recursive walk from every alarm to its ancestors, counted per ancestor.

Impact

Blast Radius With Revenue

If this node fails, which services degrade, which customers lose service, in which SLA tier, and how much recurring revenue is exposed, through which path?

Fraud

Subscription Fraud Rings

Which accounts are linked, at any depth, through shared identity documents, payment details or contact data? Connected components over the shared attributes, with every link cited.

Inventory

One Asset, Many Records

Which inventory entries across systems, or across merged operators, are the same physical asset? Every match cites its rule. See entity resolution.

Architecture

One Plane Across
Every Domain

Typical network-intelligence platforms copy data into their own store and add a digital twin on top. Prometheux sits between your network data and the people, tools and agents that act on it, and runs the knowledge where the data already lives.

Analytics

NOC, commercial and finance dashboards, graph and BI

Integrations

Back into the databases behind OSS, BSS and CRM

Agents

Network and care agents, deterministic answers

Models

Define the ontology, read answers, and power analytics, integrations and agents

Your network and business data

Alarms, inventory, topology, Kubernetes, metrics, configs, usage, billing and CRM data, connected in place. Nothing to migrate.

  • Telco Operations

    • Root cause, blast radius, capacity, churn signals, revenue leakage and fraud rings on one knowledge plane
    • Agents call deterministic rules over MCP and API, with the derivation behind every answer
    • Results written back to OSS, BSS and CRM databases, on a schedule or when data changes
  • The Knowledge Plane Engine

    • One model of network, services, customers and contracts across vendors and domains, instead of a twin per tool
    • Recursion with aggregation and graph algorithms: alarms per ancestor, revenue per path, capacity along a route, accounts linked at any depth
    • Same input, same output, with lineage to the rules and source rows
  • Cloud Agnostic

    • Run in the lab, the network estate or any cloud, under your own controls
    • Scale horizontally on clusters as network events, usage records and subscribers grow, with optional GPU acceleration
    • 1-click deployment anywhere: EKS, AKS, GKE, Databricks Clusters, Snowpark Containers
  • Telco-Data Agnostic

    • Read and write inventories, telemetry, alarms, configs, usage and billing records in any database, format and schema
    • Connect data lakes, warehouses, object storage, APIs and alert webhooks across clouds and on-premises
    • No new data store or twin to migrate into and keep in sync
Layer by layer

Same Layers.
Better Built.

Map a typical telco data or network-intelligence stack to Prometheux and the difference is where the work happens: in place, in rules you own, with an answer you can check.

Typical telco intelligence platform
Prometheux
Data: agnostic sources ingested into the platform's processing and storage
Data: network, OSS and BSS sources read and written in place, across clouds and on-premises; no new store
Model: a semantic digital twin that describes the network
Model: a network, service and customer model whose relationships and rules execute
Analytics: ML anomaly detection and algorithm-driven analytics
Analytics: deterministic rules, graph algorithms and Python steps, with lineage on each result
Questions: dependency walks of known depth
Questions: recursion with aggregation at every hop: alarms per ancestor, revenue per path, minimum capacity along a route
Use cases: a module per domain or function: assurance, churn, revenue assurance, fraud
Use cases: one plane across mobile, fixed, core, transport and IT, reused by network, commercial and finance teams
AI interface: GenAI chat over the data
AI interface: agents call deterministic rules over MCP and see how each answer was reached
Deployment: the vendor's platform
Deployment: Prometheux cloud, your cloud, your own Kubernetes, or inside Snowflake and Databricks
A step toward Level 4

Rack to Pod,
in Seconds

In a tier-1 operator's R&D lab, Prometheux connects inventory, Kubernetes state and live alerts in place and maps the hierarchy from rack to server to node to pod across clusters. When alerts fire, it finds whether failures share a rack, server or cluster, flags components still running but at risk, and projects resource trends so warnings arrive before thresholds are crossed. Engineers and agents reach the same rules over dashboards and MCP.

Owned, Not Vendor-Bound

The knowledge plane spans vendors and domains and stays with the operator, in rules your engineers can read and version.

Deterministic and Explained

Same data, same rules, same result, with the rules and source rows behind it. Traceable decisions support controlled automation through validated policies and your existing orchestration.

Built for Network Scale

Rules run as distributed, parallel jobs, with optional GPU acceleration, on compute you already have. See high-performance engine.

Portable by Design

Run in Prometheux cloud, your cloud, or on your own Kubernetes inside the lab or the network estate. See on-premises.

What this means for you ⟶

Bring one use case

Codify network knowledge once. Reuse it for diagnosis, prediction and response. Give agents rules they can be trusted to act on.

How operators start

One Use Case.
Then the Next.

01

Connect in Place

Point Prometheux at the systems behind one use case: inventory, telemetry and alerting, or usage, billing and CRM. The data is processed where it lives; nothing is copied.

02

Codify the Knowledge

Your engineers and analysts, our forward deployed engineers and the AI assistant turn topology, runbooks, policies and expertise into readable rules in days, not months.

03

Run One Use Case

Root cause, blast radius, churn signals or revenue leakage, on live data, for teams and agents alike.

04

Expand Domain by Domain

Each new domain or team reuses the rules and connections already in place, so the plane grows without rebuilding context for every tool.

FAQ

Questions,
Answered

How does this help us reach Level 4 autonomy?

Level 4 needs decisions across domains based on knowledge, not just more scripted workflows. Prometheux provides that knowledge plane: one model of your network and its rules, running in place, that systems and agents can call for deterministic answers with the evidence attached. It works alongside your orchestration, assurance and vendor tools rather than replacing them, and operators typically start with one domain, such as root cause in a lab or one region, then expand.

Is this another data store or digital twin?

No. Prometheux reads your inventories, telemetry, alerts, configuration, usage and billing data where they already live, across clouds and on-premises, and runs the rules there. The model is logical: there's nothing new to migrate or keep in sync.

Our equipment vendor says its stack does this. Why add Prometheux?

Equipment vendors' intelligence layers are strong inside their own domain. A knowledge plane for Level 4 has to span vendors, domains and clouds, and belong to the operator. Prometheux works alongside vendor tooling and reads from it.

Do we have to replace our OSS, BSS or assurance tools?

No. Keep them. Prometheux reads from them, adds the cross-domain reasoning, and writes results back to the systems your teams already use, on a schedule, when data changes or on demand.

Is this only for network teams?

Network operations is where most operators start. The same rules and connections then serve customer, revenue assurance, fraud and finance teams, on the same network, OSS and BSS data, so each new use case reuses what is already there.

Can agents act on the network?

Agents call deterministic rules over MCP or the API and get an answer with its derivation, with the same access rights as the user they act for. Actions run through your existing automation and change processes, with the evidence attached.

What does it cost at our data volumes?

It depends on your data and use cases, so we measure it on yours rather than quote a generic figure. Most operators start with one domain or use case, prove the value, then expand on the same rules and connections.

Take One Step
Toward Level 4.

Root cause, blast radius or predictive assurance on your own network data first, then customer and revenue use cases on the same knowledge plane. Our forward deployed engineers will run it with you, in place, on your infrastructure.

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