Unsiloed AI
#1 on olmOCR-Bench
Details
- External ID
- 48271937
- Source
- HN
- Company
- —
- Product
- —
- Website domain
- —
- Launched
- May 25, 2026
- Cohort
- —
- Upvotes
- 9
- Upvotes percentile
- 0.5516962843295639
- Tags
- —
- Fetched at
- Sept. 7, 2026, 9:26 p.m.
- Updated at
- Sept. 7, 2026, 9:26 p.m.
Description
Most of the document parsers fail on real world challenges like complex tables, handwritten documents, historical document scans, equations, multi-column layouts, complex reading order, etc. We built Unsiloed Parser to handle exactly these cases.Our latest parser v3.1 achieved #1 rank and scored 88.0 strict pass-rate on olmOCR-Bench. We ran the evaluation across 1,403 PDFs and 8,413 unit tests using the unmodified upstream Allen AI scorer (olmocr==0.4.27) and found Unsiloed beats 18 other OCR services, including GPT-5.5, Claude Opus 4.7, LlamaParse, Reducto, Azure Document Intelligence, AWS Textract, and Unstructured.When we dug deeper into the failure cases, we found many errors were not OCR errors but things like \frac vs \dfrac, whitespace differences, or equivalent LaTeX renderings. We ran a secondary LLM-as-Judge evaluation to classify real misses vs semantic equivalents, which lifts the corrected score to 94.8 (explained deeply in the blog post).Blog with full methodology and examples: https://www.unsiloed.ai/blog/unsiloed-ai-achieves-1-rank-on-...Evaluation Code for reproducibility: https://github.com/Unsiloed-AI/unsiloed-olmocr-benchmarkFeel free to post your messiest PDFs in the comment and we'll run it through Unsiloed parser and share the output here.
Enrichment
- Theme
- lightweight and on-device AI runtimes
- Vertical
- Horizontal
- Function
- Dev tools
- Audience
- Developer
- AI stance
- AI feature
- Project type
- Commercial product
- Normalized one-liner
- ocr benchmarking tool
- Manually corrected
- False
Could you build this?
No Achieving state-of-the-art benchmark results on complex document layout OCR requires proprietary model architectures, massive multimodal pre-training, and extensive synthetic/curated datasets.
What it would actually take: A real version requires training a specialized vision-language model (VLM) or custom layout transformer on millions of high-resolution pages containing complex tables, math equations, and non-standard reading orders. The pipeline involves custom synthetic data generation (rendering LaTeX, complex HTML tables), layout segmentation models, and high-performance inference servers (TensorRT-LLM / vLLM) on high-end GPU clusters. This requires deep machine learning research expertise and substantial compute budgets.
Discussion
4 comments analyzed.
Concerns raised: No self-serve signup available, Manual setup required via email
Competitors
Other products that read as similar to this one — 91 launches clear the similarity bar, closest 8 shown.
Attention rank: #36 of 92 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).
Launched 206 days after the earliest competitor.
- Vision-Based, Vectorless RAG for Long Douments · hn · 2025-10-31 · 6 upvotes · similarity 0.49
- Irpapers · hn · 2026-02-23 · 5 upvotes · similarity 0.48
- Run open-weight OCR, VLM and vision models behind one API · hn · 2026-09-04 · 5 upvotes · similarity 0.43
- Built a tool solve the nightmare of chunking tables in PDF vs. Markdown · hn · 2025-11-23 · 15 upvotes · similarity 0.41
- Unicode Steganography · hn · 2026-04-07 · 59 upvotes · similarity 0.39
- We benchmarked 18 LLMs on OCR (7K+ calls) · hn · 2026-04-22 · 5 upvotes · similarity 0.38
- Ideogram 4.0 · hn · 2026-06-03 · 46 upvotes · similarity 0.38
- Unsiloed AI: Make Unstructured Data LLM-Ready · yc · 2025-10-31 · 52 upvotes · similarity 0.38
Other launches for this product
- No other launches for this product.
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