Automate Research with the Perplexity AI API

The 3 AM Rabbit Hole

It’s 3 AM. Sarah, a startup founder, is staring at her laptop, surrounded by a fortress of cold coffee mugs. She has a critical investor meeting in eight hours. The goal was simple: create a one-page summary for each of her top 10 competitors, covering their recent funding, key product launches, and customer complaints.

Simple, right? That was six hours ago. She’s now 47 browser tabs deep into a Wikipedia rabbit hole about the history of enterprise software, her original goal a distant memory. The manual work is soul-crushing. Copy, paste, summarize, repeat. Every new search is a potential distraction. Her brain feels like a dial-up modem trying to download a 4K movie.

This is the work we tell ourselves is “necessary.” It’s the grunt work that precedes strategy. But what if you could fire yourself from this job? What if you had an intern who could read the entire internet, answer specific questions with cited sources, and deliver a perfect summary in seconds? That’s what we’re building today.

Why This Matters

In business, the person with the best information, delivered the fastest, usually wins. Whether you’re in sales, marketing, venture capital, or running your own shop, you spend hours on research:

“What are my competitor’s latest pricing changes?”
“What are the biggest pain points for CIOs in the healthcare industry?”
“Find me three recent case studies on AI in logistics.”

This work is expensive. You either do it yourself (your time is the most expensive thing you have), or you hire someone to do it. A junior analyst or a VA might charge $25-$50 an hour for this. An AI can do it for pennies, 24/7, without getting bored and wandering off to watch cat videos.

This automation replaces the slow, manual, error-prone human process of opening tabs, searching, synthesizing, and summarizing. It’s not just about saving time; it’s about creating a system for on-demand intelligence that can scale.

What This Tool / Workflow Actually Is

We are using the Perplexity AI API. Let’s be brutally clear about what this is.

Perplexity is an answer engine, not just a search engine. You ask it a question, and it synthesizes information from across the web to give you a direct answer, complete with sources. Think of it as the research brain of a brilliant analyst.

Its API (Application Programming Interface) is a doorway that lets our code talk directly to that brain. We can send it a question, and it sends us back a clean, structured answer in a format computers love (JSON).

What it does:

Answers factual questions with high accuracy.
Provides citations and sources for its answers.
Can focus its search on specific domains like academic papers, YouTube, or Reddit.

What it does NOT do:

It’s not a creative writer. Don’t ask it to write a poem or a marketing slogan. It’s a fact machine.
It’s not a general-purpose chatbot like ChatGPT. Its strength is grounded, source-based answers.
It won’t manage your calendar or book your flights (yet).

Prerequisites

I know the word “API” can make non-technical folks nervous. Don’t be. If you can order a pizza online, you can use an API. You’re just filling out a form and clicking ‘send’.

A Perplexity Account: Go to perplexity.ai and sign up. It’s free.
An API Key: Once logged in, go to your settings, find the API section, and generate a key. An API key is just a long password that proves it’s you. Keep it secret, keep it safe.
A way to send a command: We’ll use something called curl, which is built into virtually every Mac, Linux, and modern Windows computer’s command line or terminal. It’s a tool for sending web requests, like a super-basic web browser with no screen.

That’s it. No coding experience needed. We’re just sending a carefully written message and getting one back.

Step-by-Step Tutorial

Let’s hire our AI intern. We’re going to ask it a simple question and get an answer back.

Step 1: Open Your Terminal

On a Mac, search for “Terminal”. On Windows, search for “Command Prompt” or “PowerShell”. Open it up. You’ll see a black or white window with a blinking cursor. This is mission control. Don’t be intimidated.

Step 2: Prepare Your API Key

Copy your API key from the Perplexity settings page. We’re going to use it in the next step. For this example, I’ll use a fake key: pplx-xxxxxxxxxxxxxxxxxxxx. You must replace this with your real key.

Step 3: Craft The API Call

This is the message we’re sending to the Perplexity brain. It looks scary, but it’s just a set of instructions. Copy the entire block below.

curl -X POST https://api.perplexity.ai/chat/completions \\
     -H "Authorization: Bearer YOUR_API_KEY_HERE" \\
     -H "Content-Type: application/json" \\
     -d '{
          "model": "pplx-70b-online",
          "messages": [
              {
                  "role": "system",
                  "content": "Be precise and factual. Answer the user question based on your search results."
              },
              {
                  "role": "user",
                  "content": "What were the key announcements from Databricks in their latest earnings call?"
              }
          ]
      }'

Step 4: Understand the Command (The ‘Why’)

Let’s break that down like we’re explaining it to a 5-year-old.

curl: “Hey computer, use the web-request tool.”
-X POST: “We are SENDING information, not just asking for a webpage.”
https://api.perplexity.ai/chat/completions: This is the address of the AI brain we’re talking to.
-H "Authorization: Bearer YOUR_API_KEY_HERE": This is our ID badge. The -H means “Header,” which is like the ‘To’ and ‘From’ on an envelope. We’re proving we have permission to ask. REPLACE `YOUR_API_KEY_HERE` WITH YOUR REAL KEY.
-H "Content-Type: application/json": We’re telling the server we’re sending instructions in a format called JSON.
-d '{...}': This is the actual letter we’re sending. The -d means “Data”.
- "model": "pplx-70b-online": We’re telling it which brain to use. This one is powerful and connected to the internet.
- "messages": [...]: This is the conversation. We give it a “system” message to set the rules (“be factual”) and a “user” message with our actual question.

Step 5: Send it and See the Result

Paste the command (with your real API key) into your terminal and hit Enter. After a few seconds, Perplexity will send back a detailed JSON response containing the answer to your question, likely summarizing Databricks’ recent performance, product updates, and forward-looking statements, complete with sources. You just did in 10 seconds what would take a human 15 minutes of frantic searching.

Complete Automation Example

Okay, one question is cool. But what about Sarah’s problem of researching 10 competitors? Let’s build a tiny automation factory.

This simple script will take a list of companies and ask the same question for each one. You don’t need to be a developer to use it. Just copy, paste, and edit the list of companies.

The Goal: Generate a research brief for 3 different tech companies.

#!/bin/bash

# 1. Your API key (keep it secret!)
API_KEY="pplx-xxxxxxxxxxxxxxxxxxxxxx"

# 2. List of companies to research
COMPANIES=("Snowflake" "Databricks" "MongoDB")

# 3. The question we want to ask for each company
QUESTION_TEMPLATE="What are the top 3 customer complaints about COMPANY_NAME's products, according to recent articles and forum discussions?"

# 4. Loop through each company and ask the question
for company in "${COMPANIES[@]}"
do
    echo "========================================"
    echo "🔍 Researching: $company"
    echo "========================================"

    # Replace the placeholder with the actual company name
    QUESTION=${QUESTION_TEMPLATE//COMPANY_NAME/$company}

    # The same curl command from before, but now automated
    curl -s https://api.perplexity.ai/chat/completions \\
         -H "Authorization: Bearer $API_KEY" \\
         -H "Content-Type: application/json" \\
         -d "{\\
              \\"model\\": \\"pplx-70b-online\\",\\
              \\"messages\\": [\\
                  {\\"role\\": \\"system\\", \\"content\\": \\"You are a business analyst. Provide a concise, factual summary with sources.\\"},\\
                  {\\"role\\": \\"user\\", \\"content\\": \\"$QUESTION\\"}\\
              ]\\
          }" | grep -o '"content": "[^"]*"' | head -n 2 | tail -n 1
    
    echo "\
"
    sleep 2 # Be nice to the API, don't send requests too fast
done

How to run this:

Open a plain text editor (like Notepad on Windows or TextEdit on Mac).
Copy and paste the code above.
Replace `pplx-xxxxxxxxxxxxxxxxxxxxxx` with your real Perplexity API key.
Change the list of `COMPANIES` to whatever you want to research.
Save the file as `research.sh`.
In your terminal, navigate to the folder where you saved the file.
Run this command: bash research.sh

The script will now, one by one, research each company and print a clean summary of customer complaints right in your terminal. Sarah’s six-hour nightmare just became a 30-second script.

Real Business Use Cases (MINIMUM 5)

VC Analyst: Instead of manually googling a startup for due diligence, they feed the company name into this script. The question template asks for funding history, founding team background, and recent product launches. What took an hour per company now takes 10 seconds.
Marketing Agency: Before a client pitch, they run a script that researches the prospect’s top 3 competitors. The question is, “Summarize the main marketing message and recent ad campaigns for [Competitor Name].” They walk into the meeting armed with a complete market landscape.
E-commerce Store Owner: Researching new products. The list isn’t companies, but product categories like “air purifiers” or “smart dog collars.” The question is, “What are the most innovative features and biggest user complaints in the [Product Category] market right now?” This drives product development.
Sales Development Rep (SDR): Before making a cold call, the SDR’s CRM automatically triggers this script with the prospect’s company name. The question: “Summarize [Company Name]’s key business priorities and challenges mentioned in their last quarterly report.” The SDR opens the call with hyper-relevant insights, not a generic script.
Content Creator / Journalist: Fact-checking a story. They feed a list of claims or names into the script. The question: “Find three independent sources that confirm or deny the claim that [Claim].” It acts as an instant, tireless fact-checking assistant.

Common Mistakes & Gotchas

Vague Questions: Asking “Tell me about Apple” is useless. Asking “What was the component cost breakdown for the iPhone 15 Pro Max at launch?” is powerful. Be specific. The quality of your answer depends entirely on the quality of your question.
Ignoring the `focus` Parameter: The API allows you to focus the search. You can tell it to only search `academic` papers, `youtube`, `reddit`, or `wolfram_alpha`. If you’re researching scientific topics, focusing on academic papers will give you dramatically better results.
Forgetting About Rate Limits: Don’t send 1,000 requests per second. You’ll get blocked. That `sleep 2` in my script is important. Be a good citizen of the internet. Read the API documentation on rate limits.
Using it for the Wrong Job: This is a research tool, not a creative writing tool. If you want a blog post, use a model like GPT-4. If you want a factual answer with sources, use Perplexity. Use the right tool for the job.

How This Fits Into a Bigger Automation System

This automated researcher is a powerful component, but it’s not the entire factory. It becomes truly transformative when you connect it to other systems.

CRM Integration: Imagine a new lead is added to HubSpot or Salesforce. A webhook triggers our Perplexity script. The research summary is automatically added as a note to the lead’s profile. Your sales team has instant intelligence without lifting a finger.
Email Automation: The output from our researcher can be fed directly into an email generation AI. Step 1: Research the prospect (Perplexity). Step 2: Draft a personalized email based on that research (GPT-4). Step 3: Send the email. This is a fully autonomous prospecting machine.
Connecting to Voice Agents: A customer calls your support line. They mention a competitor. The voice agent, in the background, triggers our Perplexity bot to get a real-time summary of that competitor, allowing the agent to provide a much more informed comparison.
Multi-Agent Workflows: This is where it gets really fun. Our research bot is one ‘agent’. You could have another ‘analysis’ agent that reads the research and a ‘writer’ agent that turns the analysis into a report. They pass the task from one to another, just like an assembly line.

This isn’t just a script; it’s a building block. It’s a robotic brain you can plug into any workflow that needs access to the world’s information.

What to Learn Next

You’ve just hired your first AI intern. It’s tireless, brilliant, and costs less than a cup of coffee. You taught it how to perform a specific task—researching companies—and it will do that job perfectly forever.

But what if you could promote this intern? What if you could give it colleagues? In our next lesson, we’re going to do just that. We’re moving from a single bot to a multi-agent system. We’ll build a team of AI agents that can collaborate on a complex task, like creating an entire market analysis report, from research to writing to final formatting.

You’ve built the researcher. Now, it’s time to build the rest of the firm.

Stay sharp,

– Professor Ajay

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