I put together a small Flask example that redacts PII from call transcripts and call recordings using Telnyx AI.
Code:
https://github.com/team-telnyx/telnyx-code-examples/tree/main/call-recording-redactor-python
The app has two workflows:
The prompt asks the model to replace common PII with placeholders:
[NAME]
[CREDIT_CARD]
[SSN]
[PHONE]
[EMAIL]
[ADDRESS]
[DOB]
[ACCOUNT_NUMBER]
The useful part is that the response is structured JSON, not just rewritten text.
{
"redacted_transcript": "Hi, this is [NAME] calling. My card number is [CREDIT_CARD].",
"redactions": [
{
"type": "name",
"original": "John Smith",
"redacted": "[NAME]",
"count": 1
}
],
"items_redacted": 2,
"pii_types_found": ["name", "credit_card"]
}
That means your app can show the sanitized transcript while still keeping an audit-friendly redaction map.
git clone https://github.com/team-telnyx/telnyx-code-examples.git
cd telnyx-code-examples/call-recording-redactor-python
cp .env.example .env
pip install -r requirements.txt
python app.py
Configure .env
:
TELNYX_API_KEY=your_telnyx_api_key
AI_MODEL=meta-llama/Llama-3.3-70B-Instruct
HOST=127.0.0.1
curl -X POST http://localhost:5000/redact \
-H "Content-Type: application/json" \
-d '{
"transcript": "Hi, this is John Smith calling. My card number is 4532-1234-5678-9012 and my SSN is 123-45-6789."
}'
This calls Telnyx AI Inference through:
POST /v2/ai/chat/completions
curl -X POST http://localhost:5000/redact/audio \
-F "file=@recording.wav"
For audio, the app first calls:
POST /v2/ai/audio/transcriptions
using:
distil-whisper/distil-large-v2
Then it sends the transcript through the same PII redaction flow.
POST /redact
POST /redact/audio
GET /redactions
GET /redactions/<id>
GET /health
This is a starter app, so it stores redaction jobs in memory.
For production, I would add:
The main pattern is reusable: turn communications data into structured, safer application data before it moves deeper into your systems.
Resources: