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Synthetic-Engine — Hyper-Realistic Scene-Based Documents from Zero to One

Hi Indie Hackers,

I've been deep in the synthetic data space for a while. My previous project was very technical. I've now evolved it into Synthetic-Engine — a purpose-built engine that directly generates complete, photorealistic scene images of business documents.

We’re talking invoices, bank statements, receipts, contracts, delivery notes — whatever you need — generated from scratch with production-level realism. No base image required, no ControlNet-style hallucinations (the classic “text turns into gibberish” problem is basically solved), and the output looks like someone actually printed the document and photographed it in a real environment.

This is the new direction — scene-oriented, highly controllable, and visually convincing enough for serious use cases (VLM/OCR training).
The repo is the best way to evaluate it because one real output image is worth way more than a long description. I’ll keep adding more examples over time as I expand the supported document types and scenes.

I’m currently looking for the right partners — whether that’s teams working on document AI, FinTech/enterprise automation tools, data teams who need clean training data without privacy headaches, or anyone who wants to explore commercial applications or integrations.
If this solves a real pain point for you or your product, I’d love to hear from you.
Check it out here:
https://github.com/alrowilde/synthetic-engine

Thanks!

on May 27, 2026
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    This is a strong direction — especially moving from “generate text correctly” to “generate scenes that behave like real-world documents.”

    Most synthetic doc pipelines fail not at structure, but at realism under OCR/VLM conditions (lighting, distortion, noise, layout edge cases). If you’ve actually stabilized that layer, this becomes immediately useful for production training pipelines.

    The interesting unlock here isn’t just generation — it’s building a closed-loop system:
    generate → validate (OCR/VLM) → score → regenerate → dataset curation.

    That’s where synthetic data becomes a reliable asset instead of just samples.

    I’ve been working on similar ML/data pipelines and contributed to open-source tooling around reproducibility + automation — especially integrating generation systems with evaluation and QA layers.

    If you’re exploring partnerships or turning this into a production-grade pipeline (dataset generation infra, QA scoring, or integration with doc AI systems), I’d be interested in collaborating on a paid basis.

    WhatsApp: +1 (361) 332-6512

    1. 1

      Yeah, I built it a few months ago.
      Now it's no longer just a script .
      I've combined the entire pipeline into a visual interface.

  2. 1

    Please feel free to take a look at our showcase! 😊