How to Use Dots AI to Turn Text into Structured Data

Dots AI pulls clean, structured fields out of unstructured text — invoices, emails, support tickets, contracts. Instead of regex gymnastics, you describe the fields you want and it returns JSON.

What is Dots AI?

Dots AI is an extraction platform that takes text, PDFs, or documents and returns structured output against a schema you define. You specify fields like invoice_number, total, due_date, and it fills them in. It exposes a REST API and a dashboard. Pricing is usage-based, with a free allowance for testing and per-document pricing at volume.

How do you define an extraction schema?

1. Create a project and name the document type (e.g. “Vendor Invoice”).
2. Add fields with a name and type: string, number, date, currency.
3. Mark which fields are required.
4. Upload a sample document to test.
5. Fix any misses by adding a short description to the field, like “total amount including tax”.

How accurate is it on real documents?

On clean, machine-generated PDFs accuracy is high — fields fill correctly on the first pass. On scanned or faxed documents quality drops, so pair it with OCR upstream. Multi-page invoices where the total is on a different page than the header can confuse the mapping; splitting by section helps. Always keep a confidence score if the API returns one and route low-confidence records to human review.

How do you use Dots AI through the API?

Post your document to the extraction endpoint with your schema ID, then read the JSON response. A typical request sends the file plus the schema reference and returns each field with its value and confidence. Batch endpoints let you queue many documents. For a pipeline, wrap the call in retry logic — network hiccups, not the model, cause most failures.

Where does it stumble?

Handwriting is unreliable. Tables with merged cells often misalign. And the model will occasionally invent a plausible value for a blank field unless you mark the field as nullable, so always allow empty responses for optional data. For high-volume structured extraction from digital documents, Dots AI removes a lot of custom parsing code.

Quick Summary

• Dots AI extracts structured JSON from text, PDFs, and documents.
• You define a schema of named, typed fields, then send documents to the API.
• Accuracy is high on clean digital PDFs, lower on scans and handwriting.
• Mark optional fields nullable so it does not invent values.
• Route low-confidence results to human review.