The problem is not a lack of data, but a lack of structure. For any AI agent or large language model tasked with analyzing a quarterly report, a legal contract, or a clinical study, the raw PDF is a dead end. Unsiloed AI, a San Francisco startup from Y Combinator's Winter 2025 batch, is building an API that aims to turn that dead end into a queryable data stream. Its bet is that parsing multimodal documents into clean, structured JSON and Markdown is a foundational bottleneck for enterprise AI [Y Combinator].
The wedge into regulated workflows
Unsiloed's approach is to combine vision models with traditional optical character recognition (OCR) to extract information from complex layouts, tables, and charts [Y Combinator]. The output is structured data formatted for immediate consumption by downstream LLMs and automated agents. The company claims its APIs are already processing millions of pages weekly for customers including Fortune 150 banks and NASDAQ-listed companies in sectors like finance, legal, and healthcare [Y Combinator].
Traction and the technical stack
The company reported $550,000 in revenue as of November 2025 [getlatka, Nov 2025]. This early commercial signal, combined with its Y Combinator pedigree and a $500,000 pre-seed round [PitchBook], provides a runway to refine its core technology.
| Competitor | Primary Approach | Key Differentiator |
|---|---|---|
| Unsiloed AI | Vision models + OCR | Targets complex layouts in regulated finance/legal workflows |
| Unstructured | Open-source library | Broad community adoption, extensible pipeline |
| LlamaParse | API from LlamaIndex | Tight integration with the LlamaIndex data framework |
| Reducto | API-focused | Emphasizes speed and developer experience |
The scale test: accuracy at volume
The technical premise is sound. A dedicated service for converting unstructured documents into LLM-ready formats addresses a clear pain point. The real test for Unsiloed will be performance under the scale and complexity its target customers demand.
- Schema variability: The API must either infer schemas dynamically or allow for extensive, maintainable customer-specific templates.
- Error propagation: In a pipeline where parsed data feeds directly into an autonomous agent, a single misread number in a table can cascade.
- Latency and cost: Processing millions of pages weekly implies a significant and variable compute load.
Success will depend on demonstrating consistent, auditable performance across entire document corpuses. The company's next twelve months will likely focus on moving from unnamed Fortune 150 pipelines to published case studies.