← Back to all free guides
A free guide by Mariah Brunner ↗ INSTAGRAM ↗ TIKTOK

Build The
Proprietary-Data
Moat

Larry Ellison’s argument went viral: the model isn’t the AI edge anymore. Data nobody else has is. Here’s how to apply it to your own business.

On Oracle’s December 2025 earnings call, Larry Ellison said the big AI models are "all trained on the same public data from the internet. So they’re basically the same." His point was that the next big opportunity is using private data securely. (IBTimes on Ellison’s comments)

Is it self-serving? A little. Oracle sells the systems that store private data. That doesn’t make him wrong, and it applies to a one-person business too. If Claude, ChatGPT and Gemini all learned from the same internet, what makes your AI smarter than your competitor’s is the data only you have.

I build that moat three ways. Pull data the models don’t have. Save the data you’re already throwing away. Pick the AI that can already see the data you need.

Pull Data The Models Don’t Have

AI models learn from a snapshot of the internet. They don’t know what people in your niche said this week unless you go get it.

01

The tools

  • Apify has ready-made scrapers for sites like Reddit, TikTok, Instagram and review sites.
  • Firecrawl turns any web page into clean Markdown you can drop into a Claude Project.
  • Bright Data is the bigger, pricier option for large-scale collection.

GummySearch, the old no-code Reddit tool, stopped taking new customers in November 2025 after it couldn’t get a Reddit data license, so skip it. Whatever you use, stick to public pages and follow each site’s terms.

02

Where to look

  • Competitor reviews on G2, Trustpilot, Capterra, the App Store or Yelp.
  • Conversations on X and Reddit in your niche.
  • Comment sections under the top podcasts and YouTube videos in your space.
  • Communities you’re already in, like Discord, Skool or Circle.

Once you have 100 or more raw comments, reviews or posts, this prompt turns them into a research doc and a voice doc you can reuse in every future chat.

Customer Language Synthesizer
You are a customer research analyst. I collected real customer language from places where my target audience talks about my niche and my competitors. The raw data is below. My business: [one-line description] My niche: [be specific] Competitors in the data: [list] Where the data came from: [e.g. Reddit threads, competitor reviews, YouTube comments] Raw data (100+ items work best): --- [paste] --- Do all of this: 1. Top 5 jobs customers are trying to get done. For each, one verbatim quote that proves it. 2. Top 5 pains. For each, one verbatim quote that proves it. 3. The 20 phrases customers use most often, as a swipe file for my copy. 4. The 3 biggest gaps competitors leave open (what people want and aren't getting). 5. A one-page ideal customer profile: who they are, what they value, the jobs, the pains, and what success looks like to them. 6. A brand-voice-doc.md I can load into a Claude Project: 3 voice attributes, 5 things to say, 5 things never to say, 10 example headlines, 10 example product-description openers. 7. End with a straight verdict: based on this data alone, is this a painkiller problem or a vitamin problem, and how confident are you? Rules: quote the data word for word and never invent a quote. If a pattern only shows up once or twice, say it's weak. No corporate hedging.
Save Your Own Data

This is the data you create every day and probably throw away. Nobody else can get it, which makes it the strongest moat you have.

03

What to save

  • Customer DMs and comments. Download your Instagram and TikTok data monthly, or send Manychat conversations to a Google Sheet.
  • Sales calls and meetings. Record with Otter, Fathom or Granola. Keep the transcripts, since that’s what AI reads.
  • Your best emails. The ones that converted or got real replies become your tone library.
  • Reviews and testimonials. Yotpo or Stamped if you’re on Shopify, Trustpilot if you’re not.
  • Voice memos. Your walking-around thinking, transcribed.
  • Past launches. Every brief and campaign, saved next to its results.

Put it all in one folder you also load into a Claude Project or a Gemini Notebook. Data sitting in storage does nothing. Data loaded into your AI makes every prompt start from you.

04

The Sunday ritual

Every Sunday, paste the week’s new DMs, calls and reviews into Claude and add the summary to your knowledge base. Your AI knows more about your business every week.

Weekly Knowledge Builder
You are my business analyst. Every week I give you the new raw material from my business, and you turn it into what my AI setup should remember. This week's raw material: - Customer DMs and comments: [paste] - Call or meeting transcripts: [paste or attach] - New reviews or testimonials: [paste] - Voice-memo notes: [paste] My knowledge base so far (last week's summary): [paste, or "none yet"] Steps: 1. Pull the top 5 themes from this week. For each, give 1 or 2 verbatim quotes and where they came from. 2. Flag anything new: a question customers never asked before, a new objection, a competitor mention, a phrase that keeps repeating. 3. Compare against my knowledge base. What changed, what got stronger, what got weaker? 4. Give me 3 specific actions for next week (a post, an offer tweak, an FAQ to add, a follow-up to send). 5. Write a "Week of [date]" entry, under 300 words, in markdown, that I can paste straight into my knowledge-base file. Rules: quote only what's in the material. Don't pad thin weeks. If there's nothing new, say so in one line.
Pick The AI That Sees Your Data

Most people pick an AI by brand. I pick by which data it can reach.

The same question can get very different answers depending on which data the model can see, so match the model to the job.

My Starter Stack

Start with the free tiers and only pay once a tool earns it. The data you save keeps paying off for as long as you’re in business.