Build a CDR Usage Analytics Dashboard in Python

Raw Call Detail Records are useful, but they are not exactly fun to read.

If you are building with voice, messaging, or any communications workflow, CDR-style data can answer practical questions:

• How many calls happened in a date range?


• What did usage cost?


• Which hours are busiest?


• Which routes are most active?


• Are short calls or failed calls increasing?

I put together a small Python example that turns Telnyx CDR data into a Flask analytics API, then uses Telnyx AI Inference to generate a short operational readout.

Code: https://github.com/team-telnyx/telnyx-code-examples/tree/main/cdr-usage-analytics-dashboard-python

What it exposes

The example includes these routes:

GET /cdrs

GET /analytics/summary

GET /analytics/peak-hours

GET /analytics/top-routes

GET /analytics/daily

GET /analytics/ai-insights

GET /health

The analytics routes do the normal dashboard work in Python:

• total call count


• total, average, median, and p95 duration


• total, average, and max cost


• grouped counts by direction, call type, and status


• peak-hour distribution


• top route breakdowns


• daily call and cost totals

Then /analytics/ai-insights takes a compact summary of the metrics and sends it to Telnyx AI Inference through the chat completions API.

Why not ask the model to calculate everything?

Because the model should not be the calculator here.

For this kind of app, I like the split:

1. Code calculates the metrics that need to be exact.


2. The LLM explains the trend and suggests what to investigate next.

That way, your totals and costs stay deterministic, but your dashboard can still give users a helpful plain-English summary.

Run it

Clone the examples repo:

git clone https://github.com/team-telnyx/telnyx-code-examples.git

cd telnyx-code-examples/cdr-usage-analytics-dashboard-python

Create your .env file:

cp .env.example .env

Add your Telnyx API key:

TELNYX_API_KEY=your_telnyx_api_key

AI_MODEL=moonshotai/Kimi-K2.6

HOST=127.0.0.1

Install and run:

pip install -r requirements.txt

python app.py

Test it:

curl http://localhost:5000/health

Get a summary:

curl "http://localhost:5000/analytics/summary?start_date=2026-07-01&end_date=2026-07-08" | python3 -m json.tool

Ask for AI insights:

curl http://localhost:5000/analytics/ai-insights | python3 -m json.tool

Where this could go

This is intentionally small, but the pattern is useful:

• add a UI for support or ops teams


• store daily snapshots in a database


• send weekly summaries to Slack


• alert when costs spike


• detect unusual short-call patterns


• compare usage by campaign, customer, or region

The repo is also agent-readable, so you can point a coding agent at the example and ask it to extend the dashboard, add charts, wire in auth, or adapt the metrics to your own workflow.

Resources

• Code: https://github.com/team-telnyx/telnyx-code-examples/tree/main/cdr-usage-analytics-dashboard-python
  


• Telnyx AI skills and toolkits: https://github.com/team-telnyx/ai
  


• Telnyx AI Inference docs: https://developers.telnyx.com/docs/inference
  


• Chat Completions API: https://developers.telnyx.com/api/inference/chat-completions
  


• Telnyx Portal: https://portal.telnyx.com/