Autonomous Decision Intelligence for the 100X Executive See the signals that matter. Act before service and customer impact compounds

Autonomous decision intelligence for telecom operators. Lighthouse continuously monitors network, customer and field signals, diagnoses why KPIs move, and recommends the next best action with clear ownership.
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  • Multi-vendor operating context
  • AI-powered diagnostics
  • Conversational investigation
LLIGHTHOUSE
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JSTelecom Operations
ILLUSTRATIVE PRODUCT VIEW

Network Health at Risk

Elevated network issues impacting customer experience

High priority
Sites with issues326↑ 48% vs. prior week
Affected subscribers1.2MConcentrated in priority cohorts
Revenue at risk$2.8MIllustrative monthly exposure
Churn risk3.6%Elevated within affected cells
AI-POWERED ROOT CAUSE

High packet loss on priority 4G sites

Likely cause
Backhaul congestion
Affected estate
Recurring site cohort
Customer impact
Latency, drops, complaints
CUSTOMER IMPACT

Who is being affected?

Impacted subscribers1.2M
Complaint signalElevated
Churn risk3.6%
NEXT BEST ACTION

Prioritize the highest-impact sites

  1. Optimize backhaul capacity
  2. Route priority work orders
  3. Notify care for affected cohorts
Owner: Network Operations

Why telecom needs better decision intelligence The telemetry already exists. The decision layer does not.

Carrier operations generate more signals than teams can interpret in time. Lighthouse connects network, service, customer and field context so teams can move from noise to root cause to action.
Business challenge How Lighthouse helps

Alarm noise hides the few signals that matter

Multi-vendor estates generate high event volumes, making evidence-based prioritization difficult.

Smart anomaly detection and evidence-linked diagnosis

Baselines KPIs, separates meaningful deviations from noise and keeps the supporting evidence attached to the diagnosis.

Outages are managed after customer impact begins

Degradation signals may appear earlier, but teams still depend on reactive incident workflows.

Predictive degradation intelligence

Connects early warning signals to likely failure patterns so teams can intervene before service impact escalates.

Churn is visible after the customer has already left

Commercial and network-driven causes remain blended, weakening retention decisions.

Customer-impact and churn attribution intelligence

Links network experience to affected subscriber cohorts and separates network-driven churn from other causes.

Capacity and field decisions operate with incomplete context

Planning, augmentation and dispatch can remain manual, fragmented and slow to reflect changing demand.

Capacity and field decision intelligence

Combines demand, congestion, utilization and operational signals to focus investment and field action where it matters most.

Leaders who own the outcome

Built for

  • Network Operations

    Regional Ops VP NOC Manager Service Assurance Manager

  • Customer & Commercial

    Regional CX / Commercial Manager Head of Retention

  • Planning & Field

    Capacity Planner Field Operations Manager

Built around carrier operating realities, not generic analytics.

Telecom domain expertise

Lighthouse for Telecom brings decision intelligence into the contexts Incedo works across, including network operations, customer experience, OSS modernization, capacity planning, field workflows, and AI & data engineering.

  • Network operations
  • Customer experience
  • OSS / BSS context
  • Planning & field

Priority Telecom use cases Built around the decisions that protect network performance and customer value

Explore priority decisions across network health, customer experience, competitive retention and field operations.

Network Health Intelligence

  • Network operations

Move from blended reporting and reactive incident handling to earlier detection, root-cause diagnosis and targeted action.

Performance Benchmarking by Vendor & Equipment

Benchmarking

Compare network quality across vendors, regions and equipment cohorts to expose where reliability and performance lag.

Performance percentileFailure rate

Automated Capacity Planning & Forecasting

Planning

Prioritize augmentation using demand, congestion and utilization signals instead of spreadsheet-driven assumptions.

Forecast accuracyCapacity utilization

Predictive & Preventive Outage Management

Service continuity

Identify degradation patterns earlier and create an intervention window before service impact escalates.

P1s avoidedMTTR

Capacity Failure Prediction

Early warning

Identify sites and equipment approaching a capacity or reliability threshold before they become urgent incidents.

Failure riskLead time

Proactive Problem Management

Problem management

Find recurring incident patterns, isolate systemic causes and focus teams on preventing repeat failures.

Repeat incidentsRoot-cause closure

Customer Experience & Churn Intelligence

  • Customer value

Connect network experience, subscriber behavior and commercial context before the save window closes.

Churn Early Warning System

Prediction
Rank subscribers at risk using network quality, experience and behavioral signals while there is still time to intervene.
At-risk cohortSave rate

Churn Attribution: Network vs. Non-Network

Attribution

Separate network-fixable churn from pricing, competitive and lifecycle causes to direct retention investment more effectively.

Cause splitMisallocation avoided

Competitive Retention Intelligence

  • Retention

Make competitive movement visible at the region, segment and subscriber level, then focus winback where the economics make sense.

Port-In / Port-Out Competitive Flow

Competitive flow
See where subscriber movement is accelerating, which segments are moving and where competitive pressure is concentrated.

NPS Gap vs. Competitor Analysis

Experience gap

Identify where experience or service perceptions lag competitors and connect the gap to operational drivers.

Subscriber Winback Intelligence

Winback
Prioritize lost or at-risk subscribers for targeted winback using value, cause and likelihood to return.

Field & Workforce Intelligence

  • Field operations

Bring network diagnosis and field readiness into the same decision context.

Van Stock & Parts Management

Field readiness

Improve first-time resolution with smarter parts allocation, dispatch visibility and better linkage between root cause and field action.

First-time fixRepeat truck rolls

What these use cases are designed to improve

Four operating outcomes that connect the use-case portfolio to the telecom executive agenda.

Service continuity

Find and address degradation before it becomes a major outage.

Customer retention

Connect experience issues to the subscribers and cohorts at risk.

Capex efficiency

Direct augmentation toward the sites and technologies demand actually requires.

Field productivity

Reduce repeat work by connecting diagnosis, parts and dispatch decisions.

How Lighthouse works From signal to resolution. One connected decision loop

Lighthouse combines multi-vendor context, continuous monitoring, AI-powered diagnostics, conversational follow-up and guided action so teams can move from issue to outcome without restarting the investigation.

Standardize

Unify multi-vendor signals and align key metrics.

Detect

Surface meaningful anomalies and customer-impact signals.

Diagnose & Predict

Test hypotheses, isolate causes and forecast what comes next.

Explore

Ask follow-up questions in natural language without losing context.

Recommend

Prioritize actions by severity, impact and operating context.

Track

Measure outcomes and feed learning into the next decision cycle.

Incedo LighthouseIllustrative telecom decision workspace

Network Health at Risk

Elevated degradation pattern with customer impact

High priority
Network signalDegradation rising
Affected cohortIdentified
Business impactPrioritized
ConfidenceValidated
Root-cause analysisBackhaul congestion is the leading hypothesis

Evidence converges across recurring site and service patterns.

Customer impactAffected subscriber cohorts isolated

Network experience is linked to the customers using the impacted cells.

Recommended actionPrioritize highest-impact sites

Route the intervention to network operations and track the outcome.

Why Lighthouse for Telecom A decision layer that already knows your network

Built to connect telecom context, root-cause intelligence, conversational investigation and accountable action above the operating stack you already run.

Starts with the way a telecom network actually operates.

Telecom context
Connects network, service, subscriber and field entities into one operating context, with telecom KPI relationships and problem structures built into the decision layer.
  • RAN & Core
  • OSS / BSS
  • Subscriber
  • Field
  • AI-powered KPI diagnostics

    Combines anomaly detection, hypothesis-driven RCA and predictive models to explain what changed and why.

  • Context-aware follow-up

    Lets teams keep asking the next question without losing the evidence, KPI or operational context already established.

  • Ranked recommendations, not anomaly lists

    Moves from diagnosis to prioritized action with severity, business context and clear ownership visible to the operator.

Detect→Diagnose→Decide
  • Works with the existing stack

    Connects to multi-vendor NMS, OSS/BSS, care, billing, tickets and data environments without forcing rip-and-replace.

  • Explainable and governed

    Keeps cited evidence, review controls and auditability attached to high-stakes diagnosis and action.

Trust, governance and traceability

Telecom networks are critical infrastructure. Lighthouse is designed to keep human oversight and evidence visible as teams move toward more autonomous decisioning.

  • Source traceability and cited evidence
  • Role-based access and controlled data use
  • Human review for genuine judgment calls
  • Decision, model and action audit history

Business value Better telecom decisions show up in service, customer and capital outcomes

The goal is not another analytics layer. It is earlier intervention, faster root-cause resolution and more focused operating action.
  • Higher network reliability

    Fewer avoidable incidents, faster restoration and more proactive problem resolution.

  • Stronger customer retention

    Connect service degradation to the subscribers at risk and intervene while the save window is still open.

  • Smarter capital allocation

    Prioritize capacity expansion using demand, congestion and utilization evidence.

  • Greater field productivity

    Improve first-time resolution and reduce repeat truck rolls by connecting diagnosis to execution.

Potential impact ranges

Indicative ranges from the current telecom business case and comparable industry benchmarks. Actual results depend on the operator baseline, scope and deployment.

20 to 40%fewer outages
~25%lower MTTR
1 to 2 ptspotential churn reduction
10 to 18%capex efficiency opportunity

Use these ranges as directional benchmarks only. Pilot success criteria should be set against the operator's own baseline.

Program approach Prove a priority use case in 8 weeks

Start with one use case, one region and clear success criteria. Lighthouse is configured against the operator’s data and measured against an agreed baseline in a focused two-month pilot.
Weeks 0–1

Align & Connect

Confirm the use case, success criteria, security path and required data access.

Weeks 2–3

Bind Telecom Context

Map the ontology, confirm KPI definitions and normalize the feeds needed for the pilot.

Weeks 4–6

Configure & Validate

Configure the use case on existing Lighthouse engines, calibrate diagnostics and benchmark against the baseline.

Weeks 7–8

Run & Measure

Operate the pilot in supervised mode, measure agreed outcomes and define the production scale path.

  • Pilot exit

    Measured outcome, validated operating fit and a clear recommendation for production scale.

Buyer questions What telecom leaders want to know

Common questions from network, customer and operations leaders evaluating a decision-intelligence layer.
How is Lighthouse different from an NMS, OSS or service assurance platform?
Lighthouse is positioned as a decision-intelligence layer above the existing operating stack. It connects network, service, customer and field context, then focuses on problem discovery, root-cause diagnosis, conversational follow-up and recommended action rather than replacing the underlying systems of record.
Does Lighthouse support multi-vendor network environments?
Yes. The telecom proposition is designed around multi-vendor RAN, transport and core environments, with a normalized operating context that allows KPI and reliability comparisons across domains and vendors.
What data does Lighthouse need?
The exact data depends on the use case. Typical inputs include multi-vendor NMS feeds, topology and inventory, subscriber and billing context, care data, tickets and historical RCA. A scoped pilot should define only the feeds required for the selected problem.
How does Lighthouse explain its diagnosis?
The design keeps cited evidence, KPI relationships and the reasoning path visible so teams can challenge a conclusion, review the supporting data and route genuine judgment calls to an operator.
Can business users ask follow-up questions without an analyst?
That is a core part of the product direction. The conversational layer keeps the established network and business context so users can ask follow-up questions about RCA, insights and actions without restarting the analysis.
How should an operator start?
Start with one priority use case and one region. The revised delivery model targets an 8-week pilot covering alignment and access, telecom-context binding, configuration and validation, then a supervised pilot run measured against agreed success criteria.

See Lighthouse for Telecom in action Start with the decision area costing you the most today

Choose a network, customer or field problem and see how Lighthouse can move from signal to diagnosis to action in one connected decision loop.

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