Extract Structured JSON from Messy Text with Telnyx AI Inference

#ai #inference #telnyx #json

Messy text is everywhere: support tickets, lead forms, emails, contracts, incident reports, call notes, Slack messages. The annoying part is that the useful data is usually in there somewhere, but not in a shape your app can trust.

I built a small Python example that uses Telnyx AI Inference to turn unstructured text into structured JSON.

Repo: https://github.com/team-telnyx/telnyx-code-examples/tree/main/extract-structured-json-with-ai-python

What it does

The app exposes a simple Flask endpoint where you send messy text plus the fields you want back.

For example, you can send something like a customer support note and ask for:

customer name
issue type
urgency
product
next action

The model returns a structured JSON object that your app can validate, store, route, or pass into another workflow.

Why this pattern is useful

A lot of AI demos stop at “summarize this text.” That is useful, but many real apps need something stricter:

route a ticket
classify an incident
extract lead details
normalize intake forms
prepare data for a CRM
trigger automations based on extracted fields

Structured JSON makes the LLM output easier to use in actual software.

How it works

The example uses Telnyx AI Inference through an OpenAI-compatible client pattern.

At a high level:

Define the schema you want back
Send messy text to the model
Ask the model to return JSON
Validate the result before using it

That last part matters. Even when you ask an LLM for JSON, your app should still treat model output like external input and validate it.

Try it

Clone the repo:

git clone https://github.com/team-telnyx/telnyx-code-examples.git cd telnyx-code-examples/extract-structured-json-with-ai-python

Install dependencies and run the app:

pip install -r requirements.txt cp .env.example .env python app.py
Then call the endpoint with your own text.

Why I like this example

It is small enough to understand quickly, but it maps to a very real app pattern: taking messy human language and turning it into data your system can actually act on.
Also worth noting: the Telnyx code examples repo is structured to be agent-readable, so coding agents can inspect the examples, understand the API patterns, and help you extend them into fuller apps.