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Built for operations teams, network engineering, and automation programs 🔹

Contact us for a tailored scope (data sources, domains, governance, rollout plan).

Opti-Sphere is BubbleRAN’s intelligent, layered decision-support and optimization platform for 5G operations. It integrates with existing EMS/NMS/SMO/RIC/RAN/Core environments through available APIs, transforms operational data into traceable insights, and produces governed recommendations and change plans with an audit trail. In Opti-Sphere automation is policy-controlled as follows:

• Recommend-only: ranked recommendations, no execution
• Human-approve: approval-ready plans & audit trail
• Bounded closed-loop (optional): autonomous execution within guardrails & monitoring/rollback rules

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At a glance

What customers achieve

• Faster, consistent operations decisions with explainable evidence and confidence indicators
• Higher availability and SLA/SLE assurance via early warning, forecasting signals, and RCA guidance
• Lower-risk change management with approval-ready rollout plans, canary/staged deployment, and rollback triggers
• Operational savings at scale through governed automation and repeatable workflows
• Energy-aware operations where energy/sustainability telemetry is available (power/energy/CO₂ tagging and exports)

Reference architecture

Opti-Sphere deploys incrementally as three modular layers (L1–L3) plus an optional Digital Twin spanning all layers.

What is included

Layer 1 (L1) — Data (Sense)

Purpose: make operational data consistent, queryable, and auditable across vendors and domains.

Included

• Multi-source ingestion: KPIs/counters, logs, traces, alarms/events, configuration snapshots, topology/context, infrastructure/resource telemetry (CPU/GPU, memory, storage, network), plus energy/sustainability signals (where available)
• Normalization & schema management: entity identifiers, units, time alignment, enrichment
• Time-series optimized storage and querying: retention policies, downsampling/rollups
• Dashboards & threshold alerts for KPIs/KQIs
• Exports & governance: dataset export APIs (REST/gRPC where applicable), RBAC, lineage/audit metadata, data quality checks

Customer benefit: one trustworthy operational dataset across domains and faster triage/troubleshooting.

Layer 2 (L2) — Insights & Analytics (Infer)

Purpose: produce explainable, decision-ready signals (formulas first, models second).

Included

• Baselines & scoring per KPI/KQI (with confidence)
• Correlation & RCA candidate ranking with evidence summaries
• Trend/seasonality & anomaly indicators (statistical and ML where enabled)
• Forecasting signals (traffic, risk, degradation likelihood)
• Optimization primitives: objectives, constraints, feasibility checks, what-if scoring hooks

Customer benefit: less noise, better prioritization, faster mitigation.

Layer 3 (L3) — Agentic AI & Decision Support (Automate)

Purpose: turn intent and constraints into governed, repeatable workflows that produce auditable recommendations and change plans.

Included

• Blueprint catalog (configurable multi-agent workflows) with intent-to-task translation
• Decision narrative & structured outputs: recommended actions, expected impact, confidence, evidence links
• Tool integrations: analytics/optimization engines, Digital Twin validation, change management systems
• Governed execution modes: recommend-only, human-approve, optional bounded closed-loop
• Policy guardrails: allowed actions, evidence thresholds, approval gates, rollback hooks

Blueprint examples (tier-dependent)

• Config Planner (propose/validate configuration deltas with rollout plans)
• Observability Copilot (guided exploration of KPIs/alarms/context with traceability)
• SLA/SLE Assurance (detect degradations, quantify impact, propose mitigations under constraints)
• Anomaly Detection & RCA (early warning, evidence collection, RCA hypotheses, next steps)

Optional: Digital Twin — Validation & What‑If (Twin add-on)

Purpose: reduce operational risk by validating candidate changes before production actions.

Included

• Synced representation of topology/config/state (where integration allows)
• What-if evaluation: expected KPI/KQI deltas, risk scoring, constraint feasibility
• Controlled trials & rollout planning (canary/staged deployments)
• Optional synthetic scenarios for rare faults/stress conditions (when enabled)

Customer benefit: safer changes—test first, deploy with confidence, and rollback faster.

Example blueprint: multi-agent cell reconfiguration

This blueprint illustrates a governed workflow for proposing, validating, and refining cell configuration changes:

1. Cell Reconfiguration Planner (Reconfig) proposes candidate configuration deltas and a rollout plan.
2. Digital Twin Validator (DT) evaluates candidates with what-if analysis and safety checks.
3. Physical Network Optimization Agent (PHY) refines and enforces the configuration within constraints.
4. Flexible LLM placement supports cloud, edge, or AI factory deployments depending on latency, privacy, and compute constraints.

Checkout sample multi-agent blueprint for governed configuration planning and validation here

Interfaces & integration

Opti-Sphere integrates via northbound and southbound interfaces; exact protocols depend on your environment.

Northbound (outputs & control)

• UI for dashboards, recommendations, and workflow approvals
• APIs for queries, reports, and exporting structured recommendations/action plans (REST/gRPC; webhooks/SDK where applicable)
• Optional integration with ticketing/change-management systems for approvals and audit

Southbound (data in / actions out)

• Data ingestion via streaming or batch: Kafka/MQTT, Prometheus/OpenTelemetry, REST/gRPC, syslog/SFTP, NETCONF/YANG, O‑RAN O1 (when applicable)
• Action outputs (optional, governed): configuration deltas, policy updates, workflow triggers, or change requests via EMS/NMS/SMO/RIC/vendor APIs

Deployment and Operational Modes

Opti-sphere could be either hosted on-permises or developer as a service with API models. It can operate in three modes as defined in the table below.

Packaging & tiers

Ready to start?

Recommended rollout

1. Start with Sense (L1) to unify telemetry and build trust in the data.
2. Add Infer (L2) for explainable signals: baselines, anomalies, forecasts, and feasibility checks.
3. Enable Automate (L3) for governed workflows and approval-ready change plans.
4. Add Twin where high accuracy, risk reduction and what‑if validation are critical.

# Illustrative workflow (adapt to your environment and chosen tier)
bash$ brc connect sources --kpis --events --cm --topology
bash$ brc enable tier sense
bash$ brc enable tier infer        # optional
bash$ brc enable tier automate        # optional
bash$ brc install aifabric blueprint.yaml        # optional

Need more information ?

Check our frequently asked questions about Opti-Sphere here below and get quick replies.

To answer your unique deployment and projects needs, we can plan a live demo, help you forward with a requirements questionnaire, and connect you with our partner ecosystem (universities, system integrators, cloud providers).

📧 contact@bubbleran.com