Unsiloed AI

What

Unsiloed AI is a document processing platform that converts multimodal unstructured data into structured, machine-readable outputs such as JSON and Markdown for use in LLMs, AI agents, and automation workflows. The product is aimed at developers, AI engineers, data engineering teams, and operations teams working with accuracy-sensitive document-heavy processes.

Its core workflow is to ingest source documents from existing storage systems, parse and transform content with vision-language models, and produce structured outputs that preserve context and hierarchy. Based on the page, it is positioned as enterprise infrastructure for document ingestion and extraction, especially where teams need higher accuracy on complex files such as PDFs, spreadsheets, slides, images, and domain-specific business documents.

Features

• Multi-format data ingestion: Ingests content from PDFs, slides, spreadsheets, wikis, databases, and document stores such as S3, GCS, Azure, and Minio to reduce manual collection and normalization work.
• Vision-model-based structuring: Uses a proprietary dual-stream vision-language model to understand text, tables, numbers, images, and hierarchical structure, helping convert complex documents into usable structured data.
• Domain-aware decoding: Applies domain-specific ontology during extraction so relevant information can be parsed while preserving business context and document hierarchy.
• Hierarchical indexing: Creates chunks with parent-child mappings and hierarchical indexing to support retrieval of related information in downstream AI workflows.
• Structured output for AI systems: Produces LLM-ready Markdown and JSON that can feed document understanding, agent workflows, and automation pipelines more directly.
• Flexible deployment options: Supports cloud-native, on-premise, and air-gapped deployment models for organizations that need to control where document processing runs.

Helpful Tips

• Validate output quality on your hardest documents: For products in this category, accuracy often varies by document complexity, so pilot with tables, nested layouts, scanned files, and domain-specific forms rather than simple PDFs.
• Check hierarchy preservation, not just extraction: If your downstream use case involves retrieval or agent reasoning, preserving section structure, parent-child relationships, and table context can matter as much as raw text accuracy.
• Map outputs to your target schema early: Structured extraction tools are most useful when JSON or Markdown outputs align with the entities, fields, and workflows your AI systems already expect.
• Review deployment and data-handling requirements carefully: If data residency, privacy, or air-gapped operation matter, confirm those operating models and security controls during technical evaluation.
• Plan a human-review path for low-confidence cases: The site mentions confidence-score-based reinforcement learning, which suggests a practical fit for workflows that route uncertain outputs to specialists before automation proceeds.