Data Extractio

Extract PDF tables to Excel. Pay per document, not per page.

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April 23, 2026 I built a product for 6 months. My only user is me. Pivot time.

Hey Indie Hackers 👋

I'm Martin, a Data Engineer who just learned a very expensive lesson.

The current situation

  • ✅ Working product (PDF table extraction)

  • ✅ Deployed to production

  • ✅ Product Hunt launch done (April 14)

  • ❌ Real users: 0

  • ❌ Revenue: $0

  • ❌ Just me talking to myself in PostHog

Classic first-time founder mistake: Built for 6 months in silence. Zero audience building.

What is Data Extractio?

PDF table extraction tool with simpler pricing than competitors:

  • Competitors charge per page ($0.02-0.05/page)

  • We charge per PDF (one credit = extract ALL tables from one PDF)

Example: 200-page document with 15 tables

  • Competitors: $4-10 (pay for every page)

  • Data Extractio: $0.19 (extract all 15 tables)

Built with Django, React, Python. Text-based PDFs work great. No OCR yet (scanned documents don't work—this is next).

Product: dataextractio.com

The Product Hunt "launch"

Launched April 14. Strategy: Post at 12:01am and pray.

Hour 17: 0 upvotes. Panic.

Hour 20: Posted on Reddit (r/excel). Got 8 upvotes total.

Conversions: Zero.

Lesson learned: You need an audience BEFORE launching, not after.

What I'm doing now (the pivot)

Completely changing approach:

1. Building in Public

  • Twitter: 3+ posts/week (progress, metrics, lessons)

  • Weekly updates here on IH (real numbers, even if they're bad)

  • Full transparency about what works/doesn't

2. SEO-First Growth

  • Blog posts targeting "pdf table extraction", competitor comparisons

  • Reddit engagement (r/ProductHunters helped with those 8 upvotes!)

  • Provide value first, promote second

3. Ship Missing Features

  • OCR integration (Priority #1 - unlock scanned PDFs)

  • RESTful API for developers

  • Zapier integration (later)

4. Audience > Features

  • Answer questions on Reddit daily

  • Help others here on IH

  • Build relationships, not just features

Current metrics (Day 1 of building in public)

Let's establish the baseline:

  • MRR: $0

  • Users: 1 (me)

  • Real signups: 0

  • Traffic: ~10 unique visitors total (mostly from PH/Reddit)

  • That PostHog spike? Me testing before launch

  • Time to first real user: TBD

This is what "starting from zero" actually looks like.

What I need from this community

1. Strategy check

  • Is SEO realistic for a side project (I work full-time as Data Engineer)?

  • Should I focus on getting ANY users first, or build OCR first?

  • Niche down (e.g., "for accountants") or stay broad?

2. Your zero-to-one story

  • How did you get your first 10 REAL users?

  • How long from launch to first paying customer?

  • What worked better than expected?

3. Pricing validation

  • For PDF table extraction, what would you actually pay?

  • Is per-PDF pricing confusing or clear?

Questions for you

  1. What would you focus on first: OCR or getting users for what exists?

  2. How did YOU get your first real user (not friends/family)?

Thanks for reading. Excited to document this journey from 0 → something (or fail spectacularly trying).

Following along = watching someone build an audience from literally nothing. Should be educational either way. 🚀


Product: dataextractio.com

Twitter: @MartCervt

Tech stack: Django, React, Python, Supabase, Paddle

Working on: OCR integration this week


P.S. If you process PDFs and want to be my first real user, DM me. I'll give you unlimited credits just to get honest feedback.

8 Comments

  1. 2

    Most data engineers realize too late that building a powerful technical solution is only half the battle if no one is looking for it. It is a tough pill to swallow when you realize that six months of engineering hasn't translated into a single signup, especially when your pricing model is actually more disruptive than the industry leaders. Since your current version only handles text-based PDFs, do you think narrowing your niche to industries that primarily use digital documents—like legal or software reporting—would be more effective than waiting for OCR to compete in the broader market?

    1. 1

      You're right.

      New approach: Target software/SaaS companies (digital PDFs work now) while building OCR in parallel. I can share OCR progress as I build so users can tell me if it'll work for their needs.

      Do you know any software companies I should reach out to? Or where to look? Planning to post in some Reddit communities.

      Thanks for this. 🙏

      1. 2

        Targeting SaaS companies while building OCR in public is the right move because it allows you to validate the tech with real feedback before it is even finished. Digital-first niches like fintech or data analytics already deal with high volumes of structured PDFs where your "per-document" pricing would be an easy sell.

        It reminds me of how I handle Digital PR and Media Placements where the strategy is all about targeting high-authority spaces like MSN or AP News. Instead of just waiting for traffic we focus on building that trust baseline first so the growth becomes a repeatable system rather than a one-off spike.

        For outreach you should definitely check out subreddits like r/SaaS or r/DataEngineering where people are constantly looking for ways to automate these specific workflows.

        1. 1

          Fintech/data analytics - that's the angle I was missing. Way more specific than "SaaS companies."

          Posting in r/datascience today, r/DataEngineering tomorrow.

          Thanks for the feedback!

          1. 1

            Checking back in, Martin! How did the outreach go in r/DataEngineering? I’m curious if you noticed any specific questions about the 'per-PDF' pricing there—that data is gold for framing your future PR stories. Let me know if you hit any roadblocks!

          2. 1

            That’s the spirit, Martin! r/DataEngineering is a goldmine for your tool. Once you get those first few users and start seeing some traction, we should talk about scaling that visibility. When you're ready to move beyond Reddit, getting a feature on a high-authority tech outlet can really solidify that 'Trust Baseline' we discussed. Keep me posted on how the Reddit outreach goes!

  2. 1

    Update: Made a critical tech decision this week

    Almost picked Docling (newer, "better" OCR) over OCRmyPDF.

    The catch: Docling is fully automated. No coordinate API. Would've killed my entire competitive advantage:

    • Interactive table selection (gone)

    • Template system (useless)

    • Zapier automation strategy (dead)

    Choosing OCRmyPDF instead:

    • Worse accuracy (85-90% vs 95%+)

    • 10x cheaper

    • Preserves everything that makes me different

    The lesson: "Better" tech that commoditizes you < "good enough" tech that preserves your moat.

    Also validated this week: 5 people said they use ChatGPT/Claude for PDF extraction. Changed my positioning from "we extract tables" to "automate what you're doing manually in Claude."

    Shipping OCR Monday. Then focusing on batch processing (biggest differentiator from free AI tools).

    Still 0 paying customers, but learning fast. 🚀

  3. 1

    Quick update: Fixed the Google Search Console canonical issue

    GSC was flagging duplicate content warnings (/?ref=producthunt vs / treated as separate pages).

    Implemented dynamic canonical tags in React. Validation started—should know in 2-3 days if it's resolved.

    Small fix, but matters for SEO.

About

Most table extraction tools are too difficult for the average user. I created this app to simplify the process, making it intuitive for users to select and get the data they want in just a few clicks.