Skip to content
DataCaffe.ai
Telecom
Network quality analytics
Back to Telecom
Production-Ready AI Solution
Telecom · Enterprise Solution

Network quality analytics

Subscriber-experience QoE and drop-cause analytics.

Deploy this use caseExplore Telecom Hub
Powered by DataCaffe Core AI
CommandIQ product logo — DataCaffe.aiCommandIQBreww product logo — DataCaffe.aiBreww
LIVE AI ENGINE
Real-Time Execution Pipeline

Network quality analytics · Live Event Stream

STREAMING
STAGE 01INGEST
Enterprise Data
Lakehouse & APIs
STAGE 02ACTIVE
Breww AI Kernel
Multi-Agent Engine
STAGE 03ACTION
Automated Action
Low Latency SLA
EVENT MONITOR · TELECOM
ONLINE

[INGEST] Multi-source telemetry batch synced (2,480 items)

p99: 12.4ms
Throughput: 420 ev/s
ZERO-DRIFT SLA
The problem

What we hear

NOC dashboards show network health, not subscriber experience.

Our approach

How we solve it

Subscriber-level QoE models over probe, RAN, and BSS data with drop-cause attribution.

Outcomes

Measured impact

  • Faster subscriber-impacting issue resolution
  • Better retention on high-value cells
  • Cleaner capex prioritization

Other Telecom use cases

See all
Churn prediction

Retain subscribers with well-timed offers.

Plan recommendation

Match the right plan to each subscriber.

Tower / capacity planning

Forecast capacity investment where it pays off.

Revenue assurance

Detect and recover revenue leakage.

Subscriber 360

Unified view across billing, care, and network.

SIM-swap fraud

Detect account takeover attempts in real time.

Talk to us

Bring network quality analytics into production.

Share a couple of lines about your setup — a senior partner will get back within a business day.

Discuss this use case

Tell us about your organization and what problem you are solving.

Securely saved to database · No spam guarantee
Network quality analytics — Telecom · DataCaffe.ai