When it comes to automating document workflows, extracting data is only half the battle. The real challenge is making sure the data is correct, complete, and structured in a way that your systems can understand.
That’s where JSON Schema becomes a game-changer.
DocuSchema doesn’t just extract data—it extracts data according to your schema. Here's how JSON Schema transforms document processing from guesswork into precision.
JSON Schema is a way to define the expected structure of your data. Think of it like a blueprint or contract for what your JSON data should look like.
With it, you can define:
Example schema:
json
{
"type": "object",
"properties": {
"invoice_number": { "type": "string" },
"date": { "type": "string", "format": "date" },
"total": { "type": "number" }
},
"required": ["invoice_number", "date", "total"]
}
Most OCR or AI tools just spit out whatever data they think is useful. This creates major problems:
DocuSchema flips the script: you tell it exactly what you want, and it extracts and validates data to match.
No surprises. You always get JSON that conforms to your schema.
DocuSchema won’t just guess and go—it ensures all required fields are present and valid before returning results.
Structured, schema-compliant JSON plugs directly into your APIs, databases, or workflows without post-processing.
Schemas make errors easier to identify. If a field is missing or misformatted, you know exactly where and why.
In all cases, JSON Schema ensures the output is consistent and production-ready.
At the heart of DocuSchema is a simple principle: structured data leads to better automation.
By using JSON Schema, you're not just extracting data—you’re ensuring data quality, integrity, and readiness for whatever comes next.
Ready to try schema-first document processing? Upload your first document on DocuSchema.com and see the difference in minutes.