AI for Telecommunications

Our point of view

Anomaly correlation and autonomous remediation for network operations.

We start where the operating constraint is, connect the systems that hold the truth, and only then choose the model. Governance is designed with the workflow, not bolted on.

Priority AI opportunities

Fraud and anomaly detection
Customer-service agents
AI-assisted software delivery

Use cases

Telecommunications use cases in production scope.

Business function

AI technology

Business outcome

Implementation horizon

3 use cases

Predictive MLRisk & compliance3-6 months

Fraud and anomaly detection

Rules engines lag behind evolving criminal behaviour.

AI approach: Graph features plus streaming models with analyst feedback loops.
Required data: Transactions, device signals, counterparty graph.
Systems involved: Core banking, case management
Expected impact: Risk — Fewer false positives at equal or better detection.
Time to value: 3-6 months

Impact ranges are illustrative until validated with your data.

Discuss this use case

Agentic AICustomer experience3-6 months

Customer-service agents

Contact centres handle repetitive, system-bound requests.

AI approach: Agents that read entitlements and execute transactions with approval limits.
Required data: Interaction history, order and account data.
Systems involved: CRM, billing, order management
Expected impact: Cost — Higher containment with clear escalation paths.
Time to value: 3-6 months

Impact ranges are illustrative until validated with your data.

Discuss this use case

Generative AIIT & software engineering0-3 months

AI-assisted software delivery

Delivery slows under legacy code and documentation debt.

AI approach: Code understanding, test generation and migration assistants under review gates.
Required data: Repositories, tickets, test suites.
Systems involved: SCM, CI/CD, ITSM
Expected impact: Speed — Faster delivery cycles with quality gates enforced.
Time to value: 0-3 months

Impact ranges are illustrative until validated with your data.

Discuss this use case

Reference architecture

How the intelligence connects.

OSS/BSS→Telemetry→Correlation engine→Remediation agents

Business impact indicators

Alarm noise-55% Illustrative

Mean time to repair-21% Illustrative

Care deflection+15% Illustrative

Accelerators

Relevant accelerators.

Accelerator

Enterprise Agent Studio

Design, evaluate and operate governed agents against real enterprise tools.

Solves: Agent prototypes stall because permissions, testing and observability are missing.
Architecture: Tool registry, policy engine, evaluation harness, trace store.
Integrations: ERP, CRM, ITSM, Identity provider
Implementation: 6–10 weeks to first supervised agent in production.
Governance controls: Scoped permissions · Approval thresholds · Full traces

Request a demonstration

Accelerator

AI Knowledge Hub

Entitlement-aware retrieval across every enterprise knowledge source.

Solves: Copilots answer without provenance or respect for access rights.
Architecture: Connectors, hybrid index, re-ranking, citation service.
Integrations: SharePoint, Confluence, Object storage, Ticketing
Implementation: 4–8 weeks to a governed knowledge surface.
Governance controls: Entitlement propagation · Citation enforcement · PII redaction

Request a demonstration

Accelerator

Document Intelligence Engine

Turn high-volume documents into validated structured data.

Solves: Manual keying and review dominate document-heavy processes.
Architecture: Layout parsing, extraction models, confidence routing, review UI.
Integrations: ECM, Workflow, ERP
Implementation: 4–6 weeks per document family.
Governance controls: Confidence thresholds · Human review queues · Accuracy monitoring

Request a demonstration

Accelerator