Unsiloed AI vs Reducto: Which is Better in June 2026?

If you're comparing Unsiloed AI and Reducto, both parse complex documents into structured JSON, both return confidence scores and citations, and both run on-premise for regulated data. Two things separate them. First, accuracy: on an open olmOCR-Bench harness that scores every parser the same way, Unsiloed leads at 88.0 to Reducto's 66.0. Second, scope: Unsiloed works as a drop-in parser you feed into your own stack and can also take a document all the way to a queryable vector index in the same platform, while Reducto stops at the parsing layer.

TLDR:

• Unsiloed AI and Reducto both parse complex documents into structured JSON with confidence scores and citations, and both support on-premise deployment. This is a comparison of scope and accuracy, not basic capability.
• Unsiloed can be used two ways: as a parser that feeds your existing stack, or as a full pipeline that chunks, embeds, and stores documents in a hosted vector index you can query. Reducto covers the parsing layer only.
• On Unsiloed's open olmOCR-Bench harness, which scores every parser with the unmodified Allen AI scorer, Unsiloed leads at 88.0 and Reducto scores 66.0.
• Choose Unsiloed for higher benchmark accuracy and the option of parsing plus retrieval in one platform. Reducto covers the parsing layer for stacks you already run.

What is Reducto?

Reducto is a document processing platform that turns complex documents into structured data. Its API covers parsing, extraction, classification, splitting, and editing, and it returns layout-aware output with bounding boxes, confidence scores, and citations. It handles scanned pages, handwriting, and multiple languages.

Reducto covers the parsing layer. It produces clean, LLM-ready output for retrieval-augmented generation (RAG), but it does not host embeddings, a vector store, or retrieval; those stages run in your own infrastructure.

What is Unsiloed AI?

Unsiloed AI is agentic OCR for AI pipelines that need to trust the page. It parses PDFs, scans, Office files, and images into Markdown and structured JSON, with a confidence score on every value and optional bounding-box citations. Parsing, extraction, classification, and splitting are all available through one API.

You can use Unsiloed two ways:

• As a parser that drops into your existing stack, returning structured, confidence-scored output you feed into your own embeddings, vector store, and retrieval.
• As a full pipeline, where a workflow layer chunks parsed content, embeds it, and stores it in a hosted vector index with a retrieval endpoint, so you can go from a raw document to a queryable index without leaving the platform.

Either way, every value stays auditable through its confidence score, and Unsiloed leads olmOCR-Bench at 88.0 on an open harness that scores every parser the same way.

How Unsiloed AI and Reducto Compare

Both platforms parse, extract, classify, and split, with confidence-scored, cited output, so the table below focuses on where their scope and accuracy diverge rather than on basic capability.

Capability	Unsiloed AI	Reducto
Usable as a drop-in parser for your own stack	Yes	Yes
Hosted chunking, embedding, vector index, and retrieval	Yes	No
olmOCR-Bench, same scorer	88.0 (#1)	66.0
Parsing, extraction, classification, and splitting	Yes	Yes
Per-field confidence scores and citations	Yes	Yes
Schema-based structured extraction	Yes	Yes
Cloud, on-premise, and air-gapped deployment	Yes	Yes
SOC 2 and HIPAA support	Yes	Yes

Drop-in Parser or End-to-End Pipeline

One clear difference is how much of the pipeline each one covers. Reducto turns a document into clean, structured, LLM-ready output and stops there; retrieval is out of scope, so you choose and run the embedding model, the vector database, and the retrieval service yourself.

Reducto covers the parsing layer. Unsiloed parses and can also chunk, embed, index, and retrieve, or hand its parsed output to your existing stack.

Unsiloed gives you both options on the same API. Use it purely as a parser and you get structured, confidence-scored output to feed into whatever stack you already run. Or use the workflow layer, and the chunking, embedding, hosted vector index, and retrieval are handled for you. For a RAG system, that is the difference between always maintaining three more services and being able to hand those stages to the platform when it suits you.

Choosing Unsiloed does not lock you into the full pipeline. It keeps the drop-in path Reducto offers and adds the end-to-end path Reducto does not.

Parsing Accuracy

Both tools are vision-based parsers that preserve tables, multi-column reading order, and scanned content that generic OCR loses. The cleanest way to compare them is on the same benchmark, scored the same way.

Unsiloed publishes an open olmOCR-Bench harness that runs each parser through the unmodified Allen AI scorer on the same dataset of more than 1,400 PDFs, so the scores are directly comparable. On that benchmark, Unsiloed scores 88.0 and Reducto's agentic mode scores 66.0, with GPT-5.5, Claude, and LlamaParse in between. Every row is reproducible: the per-vendor numbers from the harness, and Unsiloed's score through its public API.

Every parser run through the official Allen AI olmOCR-Bench scorer on the same dataset. Unsiloed leads at 88.0; Reducto's agentic mode scores 66.0. All rows are reproducible from Unsiloed's open harness.

Verifiable Output

For finance, legal, and healthcare workflows, traceability is a production requirement: each extracted value needs a confidence signal and a way back to the source.

Both platforms provide this. The difference is in the defaults: Unsiloed returns a numerical confidence score, from 0 to 1, on every value out of the box, while Reducto returns a categorical high or low rating by default, with numerical confidence and citations available as options. Either way, both give an audit-heavy pipeline traceable output, so verifiability is rarely the deciding factor between them.

Deployment and Compliance

Deployment is often where enterprise procurement decisions get made, and here the two are closely matched. Both run as managed cloud services, and both support on-premise and air-gapped deployment for organizations that cannot route sensitive documents through third-party infrastructure, along with SOC 2 and HIPAA.

For teams in regulated industries, neither tool is ruled out on deployment grounds, which returns the decision to scope and accuracy.

Where Each Tool Fits

• Unsiloed AI suits teams that want a parser they can drop into an existing stack, with the option to run chunking, embedding, indexing, and retrieval in the same platform when they need it.
• Reducto suits teams that only need the parsing layer and already run their own retrieval stack.

Why Unsiloed AI is the Better Choice for Document AI

Unsiloed matches Reducto on the fundamentals: confidence-scored, cited output, schema-based extraction, and cloud, on-premise, and air-gapped deployment with SOC 2 and HIPAA. Two things set it apart:

• Parser or full pipeline: use Unsiloed as a drop-in parser, or run parse, chunk, embed, index, and retrieve through one API. Reducto covers the parsing layer only.
• Benchmark accuracy, head to head: 88.0 on olmOCR-Bench versus Reducto's 66.0, both run through the same unmodified Allen AI scorer on the same dataset, and both reproducible.

If you only need the parsing layer and run your own retrieval stack, Reducto fits that narrower case. If you want a parser that can also grow into the whole pipeline, Unsiloed covers both.

Final Thoughts on Choosing a Document AI Platform

Unsiloed AI and Reducto both produce confidence-scored, cited output and support on-premise deployment. Two differences decide it: Unsiloed parses more accurately on a like-for-like benchmark, 88.0 to 66.0, and it covers the whole pipeline, from parsing through to a queryable index, where Reducto stops at parsing. If you want a parser that leads on accuracy, stands alone today, and can grow into the whole ingestion-to-retrieval path, try Unsiloed AI on your document set.

FAQ

How do I decide between Unsiloed AI and Reducto?

Both work as a parser you can drop into your own stack, so start with what you want to happen after parsing. If you also want chunking, embedding, indexing, and retrieval handled in the same platform, Unsiloed includes them. Reducto leaves those stages to your own infrastructure.

Do both tools return confidence scores and citations?

Yes. Both return confidence-scored output with citations back to the source, so either supports audit and compliance workflows. Reducto returns a confidence score with cited explanations; Unsiloed returns a confidence score between 0 and 1 on every value, with optional bounding-box citations.

Does Reducto handle retrieval, or just parsing?

Reducto covers the parsing layer: it produces clean, RAG-ready output, but embeddings, vector storage, and retrieval run in your own infrastructure. Unsiloed can do the same, and it also offers those stages through a workflow layer with a hosted vector index and a retrieval endpoint.

Can I deploy either tool on my own infrastructure?

Yes. Both offer cloud, on-premise, and air-gapped deployment with SOC 2 and HIPAA support, so either can run inside a controlled security perimeter where documents never leave your environment.

Which tool is more accurate on complex documents?

Both are vision-based parsers. On Unsiloed's open olmOCR-Bench harness, which runs every parser through the unmodified Allen AI scorer on the same dataset, Unsiloed scores 88.0 and Reducto's agentic mode scores 66.0. Every row is reproducible from the harness, or, for Unsiloed, through its public API.