Telecom

Telecom has high call volume, complex billing, and regulatory obligations. AI can take on the first line of each.

Support queues filled with billing questions that have straightforward answers. NOC engineers wading through hundreds of correlated alerts to find the three that matter. Churn happening from accounts that showed warning signs for months. Plan recommendations that are the same for everyone in a demographic bucket.

We build AI systems for telecom companies that handle the triage, analysis, and routing layer, integrated with your BSS/OSS stack and built with CPNI compliance requirements in scope from the start.

How we work

Tell us about your customer operations or network workflow.

What we build.

Five specific AI workflows for telecom operations.

Support ticket triage and auto-routing

AI classifies inbound support tickets by issue type (billing dispute, technical fault, service cancellation, account change) and routes to the correct queue with a structured summary. Reduces misrouting and first-response time.

Churn risk scoring from usage data

Analyze customer usage patterns, complaint history, contract status, and competitor event data to score churn risk at the subscriber level. Proactive retention outreach targets real risk, not demographic proxies.

Network alert summarization and escalation

AI reads high-volume network monitoring alerts (from NOC tools like SolarWinds, Nagios, or Splunk) summarizes correlated events into incident briefs, and routes escalations with context so NOC engineers work smarter, not faster.

Plan recommendation engine

AI analyzes a customer's current usage against your plan catalog and generates a personalized plan recommendation (for upsell, rightsizing, or bundle offers) surfaced at the right interaction moment in your CRM or service portal.

Regulatory compliance document processing

Process CPNI (Customer Proprietary Network Information) notice requirements, CALEA compliance documentation, and state PUC filing requirements, extracting structured data and generating compliance reports at the required regulatory cadence.

Who we build for.

Regional carriers and CLECs with large subscriber support operations
ISPs and broadband providers with high billing inquiry volume
MVNOs building AI-enabled customer experience differentiation
Telecom technology companies adding AI to BSS/OSS platforms
Cable operators managing bundled service complexity

Who this isn't for.

Carriers expecting AI to replace NOC engineers
Network alert summarization and escalation routing accelerates NOC operations. It does not replace network engineers. Incident diagnosis, root cause analysis, and remediation require experienced telecom engineers.
Companies without BSS data access for churn modeling
Churn prediction requires subscriber-level data from your BSS: usage records, complaint history, payment history. If that data is not available in a queryable form, churn modeling cannot be built.
Very small carriers with fewer than 10,000 subscribers
At sub-10,000 subscriber scale, the ROI on AI customer service automation is limited relative to the investment required. We will tell you honestly on the discovery call if your scale does not justify it.

Frequently asked questions.

How do you handle CPNI compliance?: CPNI (Customer Proprietary Network Information) is protected under 47 CFR Part 64. AI systems that access or process CPNI (for plan recommendations, churn scoring, or account lookups) must follow CPNI access rules. We configure AI systems to enforce the same CPNI access controls as your existing systems, log all data access for audit purposes, and avoid using CPNI data for purposes not authorized by the customer.
Can this integrate with our BSS and OSS platforms?: We have integrated AI systems with Amdocs, CSG, Netcracker, and several proprietary BSS/OSS platforms via REST APIs, database connections, and event stream integrations. Integration complexity varies significantly by BSS/OSS platform version and API maturity. We evaluate integration feasibility specifically on the discovery call, bring your BSS/OSS vendor and version to the call.
How do you handle network data without compromising security?: Network operations AI reads from your monitoring and analytics layer, not from live network equipment. We connect to your SIEM, log aggregation platform, or network analytics system via read-only API access. The AI system never issues commands to network equipment. All network data access is logged and auditable.
How do you handle multi-language support for diverse customer bases?: Modern LLMs handle Spanish, Portuguese, French, and Mandarin well enough for customer service use cases without separate language models. For tier-1 customer support with high multilingual volume, we test language quality against a sample of real customer inquiries in each target language before deployment. We flag any languages where quality is insufficient for production use.

Ready to reduce support volume and churn?

Tell us your subscriber count and the customer operations or network operations challenge driving the most cost. We will reply within one business day with a rough scope and price range.

Telecom has high call volume, complex billing, and regulatory obligations. AI can take on the first line of each.

We build AI systems for telecom companies that handle the triage, analysis, and routing layer, integrated with your BSS/OSS stack and built with CPNI compliance requirements in scope from the start.

What we build.

Five specific AI workflows for telecom operations.

Support ticket triage and auto-routing

Churn risk scoring from usage data

Network alert summarization and escalation

Plan recommendation engine

Regulatory compliance document processing

Who we build for.

Regional carriers and CLECs with large subscriber support operations

ISPs and broadband providers with high billing inquiry volume

MVNOs building AI-enabled customer experience differentiation

Telecom technology companies adding AI to BSS/OSS platforms

Cable operators managing bundled service complexity

Who this isn't for.

Carriers expecting AI to replace NOC engineers

Network alert summarization and escalation routing accelerates NOC operations. It does not replace network engineers. Incident diagnosis, root cause analysis, and remediation require experienced telecom engineers.

Companies without BSS data access for churn modeling

Churn prediction requires subscriber-level data from your BSS: usage records, complaint history, payment history. If that data is not available in a queryable form, churn modeling cannot be built.

Very small carriers with fewer than 10,000 subscribers

At sub-10,000 subscriber scale, the ROI on AI customer service automation is limited relative to the investment required. We will tell you honestly on the discovery call if your scale does not justify it.

Frequently asked questions.

How do you handle CPNI compliance?

CPNI (Customer Proprietary Network Information) is protected under 47 CFR Part 64. AI systems that access or process CPNI (for plan recommendations, churn scoring, or account lookups) must follow CPNI access rules. We configure AI systems to enforce the same CPNI access controls as your existing systems, log all data access for audit purposes, and avoid using CPNI data for purposes not authorized by the customer.

Can this integrate with our BSS and OSS platforms?

We have integrated AI systems with Amdocs, CSG, Netcracker, and several proprietary BSS/OSS platforms via REST APIs, database connections, and event stream integrations. Integration complexity varies significantly by BSS/OSS platform version and API maturity. We evaluate integration feasibility specifically on the discovery call, bring your BSS/OSS vendor and version to the call.

How do you handle network data without compromising security?

Network operations AI reads from your monitoring and analytics layer, not from live network equipment. We connect to your SIEM, log aggregation platform, or network analytics system via read-only API access. The AI system never issues commands to network equipment. All network data access is logged and auditable.

How do you handle multi-language support for diverse customer bases?

Modern LLMs handle Spanish, Portuguese, French, and Mandarin well enough for customer service use cases without separate language models. For tier-1 customer support with high multilingual volume, we test language quality against a sample of real customer inquiries in each target language before deployment. We flag any languages where quality is insufficient for production use.

Ready to reduce support volume and churn?

Tell us your subscriber count and the customer operations or network operations challenge driving the most cost. We will reply within one business day with a rough scope and price range.