How to Build a Spot AI System to Find High-Intent Leads

What is a Spot AI, Really?

Manually sifting through Reddit threads, Twitter mentions, and forum posts for potential leads is a soul-crushing waste of time. You know opportunities are buried in there, but finding them is like panning for gold with a fork. This guide shows you how to build a simple but effective spot ai system that automates this entire process, letting you focus on closing deals, not scrolling endlessly.

First, let’s be clear. A “spot ai” isn’t a single magical product. It’s a pipeline. It’s an assembly of a few different tools that, when connected correctly, act like a smart agent working for you 24/7. This is a classic AI operator playbook pattern: you connect systems to create a capability that doesn’t exist off the shelf. The basic flow looks like this: Ingest -> Filter -> Classify -> Extract -> Alert. You feed it a stream of raw text, and it alerts you with structured, qualified lead data. Simple as that.

The Core Stack: Your No-Code Options

You don’t need to be a developer to build this. You just need to know which blocks to connect. Here’s the stack I use for this kind of workflow.

• Data Source: You need a way to get the raw text. This could be a social media monitoring tool or, more likely, a scraper. I often use PhantomBuster for this. It’s reliable for pulling data from places like LinkedIn, Reddit, or Twitter. It’s not the cheapest, but it works without constant babysitting.
• Orchestrator: This is the glue that holds everything together. You need a tool to manage the workflow logic. My pick is the Make platform (formerly Integromat). While many people start with Zapier automations, I find its strictly linear flow too restrictive for this kind of branching logic. Make’s visual, flowchart-style interface is far better for building and debugging multi-step AI agents.
• The “Brain”: This is where the AI does its work. We’ll use the OpenAI API, specifically with the GPT-4o model. It’s fast, incredibly cheap for this kind of text processing, and its reasoning capabilities are more than enough for classifying lead intent.
• Destination: Where do the qualified leads go? A simple Google Sheet is perfect for logging everything, and a Slack notification ensures you see hot leads immediately.

This stack is modular. You could swap PhantomBuster for Apify, or Make.com for Pipedream. The principles remain the same.

What Most Guides Get Wrong About Prompting for Classification

Here’s where most people fail. They find a tutorial that tells them to write a single, simple prompt like: “Analyze this post and tell me if it’s a sales lead. Answer YES or NO.”

This is a terrible idea.

This approach generates an ocean of false positives. A post mentioning your competitor isn’t a direct sales lead, but it might be a high-intent comparison shopper you absolutely want to talk to. A simple YES/NO prompt misses all that nuance. My main gripe with this method is that it creates so much noise you end up spending more time verifying the AI’s bad output than you would have spent doing the work manually. It defeats the whole purpose.

The correct way is a two-step prompt chain. First, you classify the *intent* of the post. Then, and only then, you extract data from the posts that match a high-value intent.

Step 1: The Classifier Prompt. The goal is to categorize the post. Your prompt should force the AI to choose from a predefined list. This makes the output structured and reliable.

Step 2: The Extractor Prompt. This prompt only runs if the first prompt identifies a valuable intent (like “Buying Intent” or “Comparison”). Its job is to pull out specific information into a clean, machine-readable format like JSON.

This two-step process dramatically increases accuracy and makes the entire system predictable.

Is the Free Tier Actually Usable for This?

Can you build a production-ready spot ai for free? No. But you can build and test the entire thing without paying a dime. Here’s the breakdown.

Make.com has a free plan that includes 1,000 operations per month. A single run of our lead-spotting workflow might consume 5-10 operations depending on the complexity. This means you can process about 100-200 mentions a month for free. It’s perfect for dialing in your logic and prompts. Once you’re ready for production, the Core plan is only $9/month, which is honestly a steal for what it does. I think that’s a perfectly fair price.

The OpenAI API is also incredibly cheap. GPT-4o costs about $5 per million input tokens. A single classification and extraction task might use 4,000 tokens total. You could process over 250,000 mentions for that same $5. For most small businesses, your monthly API bill for this will be less than a cup of coffee.

The real cost is the data source. A tool like PhantomBuster starts at $69/month. To me, that feels a bit steep if you only need one or two scrapers running, but you’re paying for reliability and not having to manage your own proxies and infrastructure. You could find cheaper scraping options, but they often require more technical setup.

So, your all-in cost for a production system that can process thousands of mentions per month is around $80/month. A single deal closed from this system pays for it for years.

A Step-by-Step Build in Make.com

Let’s get practical. Here is the exact flow you’d build in Make.com. The visual interface makes this much more intuitive than it sounds.

1. The Trigger: Start with a Webhook module. You’ll configure your scraper (PhantomBuster) to send its results to this webhook’s unique URL every time it finishes a run. The data will contain the text of the posts it found.
2. The First Filter: Immediately add a Router module after the webhook. Create a filter to discard junk before you waste API credits. For example, add a condition to only proceed if the post text is longer than 20 words and doesn’t contain spammy keywords.
3. OpenAI Module (Classifier): Add the “Create a Completion” module for OpenAI. Connect it to your account and select the GPT-4o model. In the prompt field, you’ll put your classification prompt. It should look something like this:

   You are an expert lead qualification assistant. Your job is to analyze a social media post and classify the user's primary intent.

   Classify the post into ONE of the following categories: [Buying Intent], [Comparison], [Technical Question], [Complaint], [Off-Topic]

   Post: "{{1.text}}"

   Your response MUST be ONLY the category name and nothing else.

4. The Logic Branch (Router): Add another Router. This will create different paths for your workflow. Set up a filter on the first path that only allows the scenario to continue if the output from the previous OpenAI module contains “Buying Intent” OR “Comparison”.
5. OpenAI Module (Extractor): On that high-intent path, add a second OpenAI module. This one will run your extraction prompt. This is where you turn unstructured text into data.

   You are a data extraction specialist. From the user post provided, extract the user's name, their specific problem, and any tools or competitors they mention.

   Format your response as a valid JSON object with the keys "name", "problem", and "mentions". If a field is not present in the post, use "null" as the value.

   Post: "{{1.text}}"

   JSON Output:

6. Log to Google Sheets: Add a “Add a Row” module for Google Sheets. Map the data from the JSON output of the extractor module to the columns in your sheet. Now you have a permanent log of every qualified lead.
7. Alert in Slack: Finally, add a “Create a Message” module for Slack. Craft a message that includes the extracted data and a direct link to the original post. This sends a real-time notification to your sales channel. My favorite part about Make.com is the visual run history; you can see exactly which module got what data and where a process failed, which makes fixing things ten times easier than staring at log files.

How to Debug When This Breaks

Your spot ai will eventually break. It’s not a matter of if, but when. The good news is that it’s almost always one of three things, and they’re all fixable.

First, your scraper might start sending garbage data. A site changes its layout, and your scraper starts pulling menu text instead of posts. The fix is upstream. Add a more aggressive filter in that first Make.com router to validate the incoming data. Check for text length or the presence of certain keywords to ensure it looks like a real post before you even send it to the AI.

Second, the LLM will give you weird or inconsistent output. This is a prompt problem. If it’s not classifying correctly, make your instructions more explicit. If it’s not formatting the JSON correctly, give it a one-shot example in the prompt to show it exactly what good output looks like. Constraining the AI’s output format is the single most important skill in building reliable automations (and it’s amazing how many people just write a lazy question and hope for the best).

Third, the Make.com scenario will just stop with an error. This is the easiest one to fix. Open the run history. It will highlight the exact module that failed and provide an error message. Usually, it’s something simple like an expired API key, a change in the data structure from a previous step, or a temporary API outage. The error log tells you exactly where to look.

For more on this exact angle, deeper coverage of AI agent platforms.

Building this system requires some initial setup, but the process of creating a spot ai forces you to think systematically about your lead flow. If you’d rather skip the build and deploy a working version in an afternoon, we’ve packaged this entire workflow as a ready-to-deploy AI automation blueprint. You can find it at deepusecase.com/vault.