Evidence-based operational RCA
AI Root Cause Analysis for Telecom Operations
Root cause analysis in telecom is slow when evidence is distributed across CDR exports, SBC logs, carrier reports, dashboards and spreadsheets. Hesanor brings the operational cut into one investigation flow, compares it with a baseline and helps teams rank the routes, carriers, customers, sources and failure causes that changed most.
Updated 2026-08-19
Questions this guide answers
AI Root Cause Analysis for Telecom Operations
- When did the deviation begin and which metrics changed together?
- Which route, carrier or customer contributed most to the delta?
- Which statuses, SIP outcomes or hangup causes explain the failures?
- How confident is the conclusion and what evidence is still missing?
Signals and metrics
Signals and metrics
Practical guides for teams connecting omnichannel interactions, telecom evidence and AI-assisted decisions.
ASR and NER
Measure answer efficiency and network-effective delivery against a comparable period.
Failure rate
Separate answered and failed traffic and rank the failure causes that increased.
MOS and PDD
Correlate quality degradation and setup delay with the same routes and customers.
Traffic mix
Detect whether volume shifts, customer composition or source behavior changed the result.
Route and carrier
Compare operational scores, stability, quality, failure and business exposure.
Call evidence
Drill from aggregated symptoms into call-level records and declared data scope.
Operational workflow
Operational workflow
Define the symptom
Select the metric, period, tenant and operational scope affected.
Build the baseline
Use the previous equivalent period or another relevant comparison window.
Rank contributors
Measure which routes, carriers, customers, sources and causes explain the delta.
Validate evidence
Confirm the hypothesis with call-level data, quality and failure context.
Prepare the response
Generate a technical summary, executive explanation and governed next action.
Why this approach is different
Why this approach is different
Comparative by design
RCA starts with a declared period A and period B instead of relying on visual intuition.
Evidence before language
AI summaries are grounded in the active filters, metrics and available records.
Guided, not autonomous
Root cause playbooks recommend next tests and actions while operators retain approval.
FAQ
Frequently asked questions
What is AI-assisted root cause analysis?
It uses statistical comparison, operational context and generative explanation to narrow hypotheses and present supporting evidence. Human operators validate the conclusion and approve actions.
Can Hesanor explain an ASR drop?
Hesanor can compare periods and rank routes, carriers, customers, sources, statuses and hangup causes associated with the ASR change.
Does RCA require historical data?
A useful baseline requires enough comparable history. When history is insufficient, Hesanor declares the limitation instead of presenting false certainty.
Can the result be shared with leadership?
Yes. Operational evidence can be transformed into technical and executive summaries while preserving scope and limitations.
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Continue exploring
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