Unsiloed AI vs Docparser: Which Is Better in July 2026?

Docparser works best when documents follow layouts you can configure once and reuse. Unsiloed AI is built for extraction where the schema travels with the request and the document layout may change.

We tested both tools on five documents, from Docparser's own invoice sample to claims, receipts, and IDs without presets. The difference was not whether both could read text. It was how much setup and stability each tool needed before the output was usable.

TLDR:

• Docparser works best for stable layouts where a team can configure and maintain parsing rules.

• Unsiloed works better when document types vary, fields are defined by an API schema, or the pipeline needs confidence scores and citations.

• Docparser matched Unsiloed on its own invoice sample, but the bank statement preset missed account and balance fields.

• Docparser SmartAI struggled on documents without presets: a medical claim, a photographed receipt, and a passport.

• Docparser bills by parsing credit, where one credit covers a document up to five pages. Unsiloed bills by page volume.

Results Across the Five Documents

Document	Docparser	Unsiloed AI
Clean invoice, using Docparser's sample	✓ All target fields correct	✓ All target fields correct, with confidence and citations
Standard bank statement	✗ Account number, holder, and balance empty; rows not split into columns	✓ Target fields extracted; transactions split into columns
Medical claim / EOB, no preset	✗ Member ID dropped; NPI returned as a phone-number-like value	✓ Target fields extracted
Photographed receipt, no preset	✗ Total extracted once, then missing on an identical re-run	✓ Line items and total extracted consistently
Passport / ID, no preset	✗ Passport number unstable; MRZ not extracted; fields dumped into a grid	✓ Target fields extracted, including both MRZ lines

How the Tools Work

Docparser is a template-and-rules system. You create a document parser for each layout, then tune parsing rules in the dashboard. Rules can use keywords, positions, patterns, zones, and table settings. That makes Docparser transparent, but every new layout becomes configuration work.

Unsiloed is an extraction API. The JSON schema travels with the request, so there is no parser to create before extraction. Each extracted field returns with confidence metadata and a source citation, which makes the output easier to route, review, and audit.

Capability	Unsiloed AI	Docparser
Best fit	Engineering teams embedding extraction into products, agents, or RAG pipelines	Ops teams parsing a stable set of known layouts
Extraction model	AI extraction against a request-time JSON schema	Templates and hand-tuned parsing rules
Setup per layout	No parser setup; schema is sent with the request	Create a parser and configure rules
Parser creation via API	No parser concept	API supports listing parsers, importing documents, and retrieving parsed data, but not creating parsers
Verification metadata	Confidence scores and source citations	Extracted values from rules; confidence support varies by parser type

Setup: Parser Rules vs Request-Time Schemas

The setup difference is where the two products first diverge: Docparser starts with a saved parser, while Unsiloed starts with the extraction schema.

Docparser Starts With a Saved Parser

Every Docparser API call is scoped to an existing parser, and parsers are created in the dashboard. The Docparser API docs expose endpoints to list parsers, import documents, and retrieve parsed data, but not to create a new parser.

That setup matters because each parser still needs rules. In the bank statement test, the default Account Number rule expected labels such as Account number: or Acct # and looked for the value on the same line. The statement printed the value in a different position, so the rule returned no data until the parser was adjusted.

Docparser SmartAI can generate rules automatically, but it does not remove the review burden. The parser decides what fields to produce, and the output still has to be checked against the fields your pipeline needs.

Unsiloed Sends the Schema With the Request

Unsiloed has no saved parser step. The schema is part of the extraction request:

curl -X POST "https://prod.visionapi.unsiloed.ai/v2/extract" \
  -H "api-key: $UNSILOED_API_KEY" \
  -F "pdf_file=@bank_statement.pdf" \
  -F "enable_citations=true" \
  -F 'schema_data={"type":"object","properties":{"account_number":{"type":"string"},"transactions":{"type":"array","items":{"type":"object","properties":{"date":{"type":"string"},"amount":{"type":"string"}}}}}}'

Document Test Results

The individual tests show where the setup model starts to matter: Docparser performs well on a matching preset, then relies on SmartAI or rule tuning as layouts move away from that preset.

Invoice: Both Tools Extracted the Target Fields

Docparser's own sample invoice is the strongest case for its preset approach.

• Docparser: Extracted invoice number 633451, PO PO99934, four line items, and totals.

• Unsiloed: Extracted the same target fields, plus confidence scores and citations.

This is where Docparser fits well: a known layout, a matching preset, and repeatable fields.

Bank Statement: The Preset Missed Key Fields

The bank statement was a standard checking statement with header fields and a transaction table.

• Docparser: Returned account number, account holder, and balance as empty. Transaction rows came back as unsplit strings.

• Unsiloed: Extracted the target header fields and split transactions into columns.

Docparser's OCR read the values, but the preset rules did not match this layout. That is fixable by tuning rules, but it is setup work the team owns for each layout.

// Docparser: fields empty, rows not columnized
"account_number": null,
"account_holder": null,
"balance": null,
"transactions": [
  { "key_0": "06/02/2026 Payroll Deposit ACH - GLENDOWER LLC 3,150.00 11,562.55" }
]

// Unsiloed: fields extracted and transactions split into columns
"account_number": { "value": "4471-88203-9", "score": { "grounding_score": 0.998 } },
"transactions": { "value": [
  { "date": "06/02/2026", "description": "Payroll Deposit ACH - GLENDOWER LLC", "deposit": "3,150.00", "balance": "11,562.55" }
]}

Medical Claim: SmartAI Missed Important Fields

The Explanation of Benefits had no Docparser preset, so the test used SmartAI.

• Docparser SmartAI: Extracted the service-line table, but returned the member ID as null and formatted the provider NPI as a phone-number-like value.

• Unsiloed: Extracted the target fields, including the member ID and provider NPI.

// Docparser SmartAI: member ID missing, NPI misformatted
"member_id": null,
"provider_2": { "formatted": "+271487206531" }

// Unsiloed: target fields extracted
"member_id": { "value": "MHP-583920164", "score": { "grounding_score": 0.998 } },
"rendering_provider": { "value": "Adaeze Osei, MD (NPI 1487206531)", "score": { "grounding_score": 0.997 } }

This is the failure mode to watch for: not just a blank field, but a plausible-looking value that represents the wrong thing.

Photographed Receipt: SmartAI Was Not Stable Across Runs

The receipt was angled, slightly blurry, and taken from the public CORD dataset. There was no receipt preset, so Docparser used SmartAI.

• Docparser SmartAI: Extracted the line items and total on the first run, then returned missing items and a null total on an identical re-run.

• Unsiloed: Extracted the line items, total, and cash amount consistently. The faint merchant name was missed by both tools.

// Docparser SmartAI: same receipt, same parser, two runs
run 1: "total": "91000", items: [ "J.STB PROMO 17500", "Y.B.BAT 46000", "Y.BASO PROM 27500" ]
run 2: "total": null,    items: [ "-", "-" ]

An extraction result that changes across identical runs is hard to route safely. The pipeline cannot distinguish a genuinely missing value from parser instability.

Passport: SmartAI Did Not Produce Clean ID Fields

The passport was a public-domain Norwegian specimen, so it contained no real personal data. There was no preset, so Docparser used SmartAI.

• Docparser SmartAI: Returned the passport number on its best run, but truncated it on re-run. Nationality, sex, dates, height, and place of birth were mixed into a generic table. The MRZ was not extracted.

• Unsiloed: Extracted the target fields, including passport number CCC002251, surname ØSTENBYEN, given names ÅSAMUND SPECIMEN, dates, height, CAN, and both machine-readable-zone lines.

// Docparser SmartAI: passport number unstable, MRZ missing
"passport_number": "CCC002251",
"mrz": null,
"table_data_11": [
  { "key_1": "NORSK/ NORWEGIAN", "key_2": "M N" },
  { "key_1": "23 APR /APR 56", "key_2": "179 CM" }
]

// Unsiloed: named fields plus MRZ
"passport_number": { "value": "CCC002251", "score": { "grounding_score": 0.996 } },
"mrz": { "value": "P<NOROESTENBYEN<<AASAMUND<SPECIMEN<<<...\nCCC0022514NOR5604230M3004157<<<<<<<<<04" }

Docparser's results screen: field captions appear inside values, several fields are grouped into generic table data, and the MRZ row reads "No data found."

For ID workflows, the MRZ is not a minor field. It is the machine-checkable line many downstream systems rely on.

Pricing and Scaling

The pricing models differ more than the headline monthly prices suggest. Docparser bills by parsing credit. As Docparser's credit documentation explains, one credit covers a document up to five pages, and longer documents consume more credits. Unsiloed bills by processed page.

Pricing item	Unsiloed AI	Docparser
Free tier	2,000 pages, then $0.015 per page	14-day trial (50 credits)
Billing unit	Page	Parsing credit, usually one document up to five pages
Entry paid plan	Standard: $250 per month for 20,000 pages	Starter: $39 per month for 100 parsing credits
Higher tier	Growth: $750 per month for 75,000 pages	Business: $159 per month for 1,000 parsing credits
Overage	Additional pages at $0.012 (Standard) or $0.010 (Growth) per page	Extra credits can be purchased or the account can upgrade
Layout scaling	No per-layout parser limit	Parser limits by plan, including 15 on Starter and 500 on Business
Enterprise	Custom page rate; VPC, on-premise, or air-gapped deployment	Custom credits and unlimited parsers

Verdict: Docparser Only Fits Narrow Template Workflows

Unsiloed is the better default choice for document extraction. It handled every test document with the same schema-first workflow, while Docparser only matched it on the invoice that fit its preset.

The exception is narrow: Docparser can make sense for a small set of stable layouts where an operations team is willing to maintain parser rules. Outside that template workflow, the tests were not close. The bank statement preset missed basic account fields. SmartAI dropped or misread important values on the medical claim, changed its answer on the same receipt, and never extracted the passport MRZ.

Those are not edge-case inconveniences. They are the failure modes that break downstream automation. If the document type varies, the schema belongs in the API request, or the pipeline needs confidence scores and citations, use Unsiloed.

If your workflow includes documents you do not fully control, book a demo and run your hardest examples through both tools.