The Slowest Intern in the World
Let’s talk about Bartholomew. He was our first “data entry specialist.” His job was simple: read customer support emails from our generic inbox, figure out what the person wanted, and copy-paste the key details into our project management tool.
Name. Email address. A one-sentence summary. Urgency level. Simple.
Bartholomew was… meticulous. So meticulous, in fact, that he could process about one email every three minutes. On a good day. On a bad day, he’d get distracted by a particularly interesting bird outside the window and forget which customer was which. We were paying him $18 an hour to be a human copy-paste machine, and he was creating more chaos than clarity.
We fired Bartholomew. Not really, because he never existed. But we’ve all employed a “Bartholomew.” It might be ourselves, late on a Friday, manually moving data. It might be a team member whose talent is wasted on robotic tasks. It’s the slow, error-prone, soul-crushing part of the business that we just accept.
Today, we’re not just firing Bartholomew. We’re replacing him with a machine that does his job in less than a tenth of a second, with perfect accuracy, for fractions of a penny. Welcome to the academy.
Why This Matters
Look, AI that writes poetry is cute. AI that generates photorealistic cats in space is fun. But AI that turns a messy, unstructured paragraph into clean, structured, machine-readable data is where the money is.
This workflow is the absolute bedrock of intelligent automation. It’s the “intake valve” for your entire automated business. Before you can respond to an email, update a CRM, or assign a task, you first need to *understand* what the request is. This is that step.
This isn’t about saving a few minutes. This is about building systems that can handle 10, 100, or 10,000 inbound requests per hour without a single human in the loop. It’s about turning your chaotic inbox into a perfectly organized, real-time database of customer needs. You can’t scale a business on manual data entry. You can with this.
What This Tool / Workflow Actually Is
We’re going to use an AI provider called Groq (pronounced “grok,” like the word that means to understand deeply).
What Groq Is
Groq is not just another LLM company like OpenAI. They build their own custom chips—Language Processing Units (LPUs)—designed to run AI models at absolutely insane speeds. Think of it this way: if ChatGPT is a brilliant, thoughtful philosopher who carefully considers your question, Groq is the world’s fastest librarian. It might not invent a new field of metaphysics for you, but if you ask it to find, categorize, and structure information, it will do it faster than you can blink.
What This Workflow Is
We’re using a feature often called “Function Calling” or “Tool Use.” We are giving the AI two things:
- A piece of messy, unstructured text (like a customer email).
- A strict, empty form (a JSON schema) that we want it to fill out.
The AI’s job is to read the text and intelligently populate our form. It’s a translation layer from human language to computer language.
What This Is NOT
This is not a general-purpose chatbot. We are not asking for creative ideas. We are using a lightning-fast model for a very specific, structured task. Using Groq for a long, creative writing assignment is like using a Formula 1 car to haul gravel. Wrong tool for the job. We’re here for speed and structure.
Prerequisites
I know some of you are allergic to code. I get it. But if you can copy and paste, you can do this. I promise.
- A Groq API Key: Go to GroqCloud, sign up for a free account, and create an API key. Copy it somewhere safe. Their free tier is very generous.
- Python 3 installed: This is our language for gluing things together. If you don’t have it, a quick Google search for “install Python on [your OS]” will get you there.
- The Groq Python library: We just need to install it. Open your terminal or command prompt and type this one command:
pip install groq pydanticThat’s it. You’re ready. No scary frameworks, no complex setup. Let’s build.
Step-by-Step Tutorial
We’re going to build a Python script that can read any customer support email and pull out the important details. No more Bartholomew.
Step 1: Set Up Your Python File
Create a new file called
data_extractor.py. This is where our code will live. The first thing we do is import the necessary tools and set up our API key.Why: This tells Python what libraries we need and gives it the credentials to talk to Groq’s servers.
import os from groq import Groq from pydantic import BaseModel, Field from typing import Literal # IMPORTANT: Replace this with your actual Groq API key # Or better yet, use an environment variable API_KEY = "gsk_YourApiKeyGoesHere" client = Groq(api_key=API_KEY)CRITICAL: Replace
gsk_YourApiKeyGoesHerewith the key you copied from the Groq console. Don’t share this key with anyone.Step 2: Define Your “Form” (The Data Structure)
Now, we define the empty form we want the AI to fill out. We use a library called Pydantic, which makes this incredibly easy and readable. It’s like creating a blueprint for our data.
Why: A strict structure ensures we always get the data back in the exact same format. This is crucial for automation. No surprises.
class SupportTicket(BaseModel): """Information extracted from a customer support email.""" customer_name: str = Field(..., description="The first and last name of the customer.") customer_email: str = Field(..., description="The email address of the customer.") urgency: Literal["low", "medium", "high"] = Field(..., description="The urgency of the ticket, categorized as low, medium, or high.") summary: str = Field(..., description="A one-sentence summary of the customer's issue.")Look at how clear that is! We’re telling the AI exactly what we want. We even constrained `urgency` to only three possible values. This is how you build reliable systems.
Step 3: Prepare the Unstructured Text
This is our input. The messy, human-written email we want to process.
Why: This is the real-world data our automation will encounter.
email_text = """ Hi there, My name is Jessica Miller and my login stopped working this morning. I can't access my dashboard and this is blocking my entire team. We have a major deadline today, so this is extremely urgent!! My email is jess.miller@examplecorp.com. Thanks, Jess """Step 4: Make the API Call to Groq
This is the magic. We send the text and our empty form blueprint to Groq and ask it to fill it in.
Why: This is the core of the automation, where the AI performs the structured extraction task at incredible speed.
def extract_ticket_data(text: str) -> SupportTicket: print("--- Sending request to Groq ---") chat_completion = client.chat.completions.create( messages=[ { "role": "system", "content": "You are an expert at extracting information from text and outputting it in a structured JSON format." }, { "role": "user", "content": f"Please extract the support ticket information from the following email: \ \ {text}" } ], model="llama3-70b-8192", tool_choice="auto", tools=[ { "type": "function", "function": { "name": "extract_support_ticket", "description": "Extracts customer support ticket details from an email.", "parameters": SupportTicket.model_json_schema() } } ] ) # Extracting the arguments from the tool call tool_call = chat_completion.choices[0].message.tool_calls[0] arguments = tool_call.function.arguments return argumentsThis block might look intimidating, but it’s mostly boilerplate. The key parts are `model` (we’re using Llama 3 70B), `messages` (our prompt), and `tools` (where we pass our Pydantic `SupportTicket` blueprint).
Complete Automation Example
Now let’s put it all together in our
data_extractor.pyfile and run it. This one script replaces Bartholomew forever.import os import json from groq import Groq from pydantic import BaseModel, Field from typing import Literal # --- Step 1: Setup --- # IMPORTANT: Best practice is to use environment variables for API keys # For this example, we'll hardcode it. Replace with your key. API_KEY = "gsk_YourApiKeyGoesHere" client = Groq(api_key=API_KEY) # --- Step 2: Define Your Data Structure --- class SupportTicket(BaseModel): """Information extracted from a customer support email.""" customer_name: str = Field(..., description="The first and last name of the customer.") customer_email: str = Field(..., description="The email address of the customer.") urgency: Literal["low", "medium", "high"] = Field(..., description="The urgency of the ticket, categorized as low, medium, or high.") summary: str = Field(..., description="A one-sentence summary of the customer's issue.") # --- Step 3: Prepare Your Input Text --- email_text = """ Hi there, My name is Jessica Miller and my login stopped working this morning. I can't access my dashboard and this is blocking my entire team. We have a major deadline today, so this is extremely urgent!! My email is jess.miller@examplecorp.com. Thanks, Jess """ # --- Step 4: Define the Extraction Function --- def extract_ticket_data(text: str): try: print("--- Sending request to Groq ---") chat_completion = client.chat.completions.create( messages=[ { "role": "system", "content": "You are an expert at extracting information from text and outputting it in a structured JSON format based on the provided schema." }, { "role": "user", "content": f"Please extract the support ticket information from the following email: \ \ {text}" } ], model="llama3-70b-8192", tool_choice="auto", tools=[ { "type": "function", "function": { "name": "extract_support_ticket", "description": "Extracts customer support ticket details from an email.", "parameters": SupportTicket.model_json_schema() } } ], temperature=0.0 ) tool_call = chat_completion.choices[0].message.tool_calls[0] arguments_str = tool_call.function.arguments # Convert the JSON string into a Python dictionary arguments_dict = json.loads(arguments_str) # Validate and create the Pydantic model ticket = SupportTicket(**arguments_dict) return ticket except Exception as e: print(f"An error occurred: {e}") return None # --- Run the automation --- if __name__ == "__main__": extracted_data = extract_ticket_data(email_text) if extracted_data: print("\ --- Extraction Successful ---") print(json.dumps(extracted_data.dict(), indent=2))Save that file, open your terminal in the same directory, and run:
python data_extractor.pyIn less than a second, you will see this beautiful, clean, structured output:
-- Extraction Successful --- { "customer_name": "Jessica Miller", "customer_email": "jess.miller@examplecorp.com", "urgency": "high", "summary": "Customer's login stopped working, blocking her team on a deadline day." }That JSON object is gold. You can now send it to any other system in your business. Create a Trello card. Add a row to a Google Sheet. Update a Salesforce record. The possibilities are endless, and it all happened instantly.
Real Business Use Cases
This exact same pattern can be used everywhere:
- E-commerce Store: Parse return-request emails to extract
order_number,product_name, andreason_for_returnto automatically initiate the returns process. - Real Estate Agency: Scrape text from property listing websites and instantly extract
address,price,bedrooms,bathrooms, andsquare_footageinto a database. - Recruiting Firm: Feed in a resume (as text) and pull out
contact_info,years_of_experience,key_skills, andwork_historyto pre-screen candidates in seconds. - Financial Analyst: Process news articles or earnings call transcripts to extract mentions of specific companies, financial figures, and sentiment (
positive,negative,neutral). - SaaS Company: Analyze user feedback from a form to categorize it as a
bug_report,feature_request, orbilling_questionand extract the relevant details for each.
Common Mistakes & Gotchas
- Overly Complex Schemas: Don’t try to extract 50 nested fields from a single paragraph. The AI will get confused. Start simple, with 3-5 key fields, and build from there.
- Vague Field Descriptions: The `description` in your Pydantic model is a prompt for the AI. Be specific. Instead of `”summary”`, write `”A concise, one-sentence summary of the user’s core problem.”`. This dramatically improves accuracy.
- Using the Wrong Model for the Job: Groq’s speed is its main advantage. If you need deep, multi-step reasoning, another model might be better. For high-volume, structured data tasks, Groq is king.
- Not Handling Failures: What if an email is just spam? The model might not be able to extract anything. Your code should be able to handle an empty or error response without crashing the whole system. Notice the `try…except` block in my final code? That’s your safety net.
How This Fits Into a Bigger Automation System
This workflow is rarely the end of the line; it’s the beginning. It’s the gatekeeper that cleans and organizes all incoming information.
- With a CRM: The extracted JSON is used to create a new lead in HubSpot. The `summary` becomes the first note on their contact record.
- With Email Automation: After extracting the `customer_name` and `summary`, you can immediately fire off an email using a tool like Resend: “Hi Jessica, we’ve received your ticket about your login issue and our team is on it.” Instant reassurance for the customer.
- With Voice Agents: A customer leaves a voicemail. The audio is transcribed to text, and then this Groq workflow processes the transcript to create a structured support ticket, just like it did with the email.
- With Multi-Agent Workflows: This is Agent #1 (The “Clerk”). It receives a request, structures it, and then passes the clean JSON to Agent #2 (The “Router”), whose only job is to look at the `urgency` field and decide which human team to notify.
Think of it this way: we’ve just built the eyes and ears of our business robot. Now it can understand the world. What it *does* with that understanding is what we’ll build next.
What to Learn Next
Okay, you did it. You replaced a slow, manual process with a lightning-fast, intelligent one. You can now turn any text into clean, usable data. That’s a superpower.
But having structured data is only half the battle. Now we need to *act* on it intelligently.
In the next lesson in this course, we’re going to build the “Router.” We’ll take the JSON output from today’s workflow and build a system that automatically decides what to do next. Is it a high-urgency bug report? Page the on-call engineer. Is it a sales inquiry? Send it to the sales team’s Slack channel. Is it a simple password reset? Send an automated email with a reset link.
We’re moving from perception to decision-making. That’s when your automated business truly comes alive. See you in the next lesson.
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“suggested_category”: “AI Automation Courses - E-commerce Store: Parse return-request emails to extract