AI + Bubble.io

How to Build an AI Form Filler or Data Extractor in Bubble.io

One of the highest-ROI AI features you can add to any Bubble.io app: automatically extracting structured data from unstructured text — turning manual form filling into an instant, AI-powered experience.

EliminatesManual data entry
Works WithAny text format
JSON ModeFor reliable parsing
The Use Case

Why AI Data Extraction Transforms Workflows

Manual data entry is the biggest time sink in most business applications. AI extraction eliminates it.

📋

CV / Resume Parsing

User pastes a CV. AI extracts name, email, phone, education, work history, skills into structured fields — populating a candidate record instantly.

🏢

Business Card / Contact

User pastes contact details in any format. AI extracts and normalises name, title, company, email, phone, LinkedIn into CRM fields.

🧾

Invoice Data Extraction

User pastes invoice text. AI extracts supplier name, invoice number, date, line items, quantities, unit prices, and total.

📰

Article Metadata

Paste any article URL content. AI extracts title, author, publication date, key topics, summary, and named entities.

🏠

Property Listing Parser

Paste a property listing. AI extracts bedrooms, bathrooms, area, price, location, key features, and contact details.

📝

Meeting Notes to Actions

Paste meeting notes. AI extracts attendees, decisions made, action items with owners and deadlines as structured records.

The Technique

Using JSON Mode for Reliable Extraction

The key to reliable data extraction is forcing the AI to return structured JSON — not prose.

Configure your OpenAI API call with response_format set to json_object. This guarantees a valid JSON response that Bubble can parse. The request body:

{
"model": "gpt-4o-mini",
"response_format": { "type": "json_object" },
"messages": [
{
"role": "system",
"content": "You extract structured data and return only valid JSON. Schema: { first_name: string, last_name: string, email: string, phone: string, company: string, title: string }. If a field is not found, return null for that field."
},
{
"role": "user",
"content": "Extract data from this text: "
}
],
"max_tokens": 500
}

📌 Always define the exact JSON schema in your system prompt, including what to return when a field is missing (null or empty string). Without this, the AI invents field names inconsistently.

Bubble Implementation

Parsing and Saving the Extracted Data

Once you have a JSON response, Bubble’s built-in JSON parsing operators handle the rest.

1

Call the API with user’s text

Wire the API call to a button or automatic trigger when text is pasted. Pass the raw text (from a multi-line input or a previous database field) as the raw_text parameter.

2

Store the raw JSON response

Save the full JSON string from the API response to a temporary field. This gives you a fallback if parsing fails.

3

Parse individual fields

Bubble’s ‘:extract with regex’ and ‘:parsed as JSON’ operators let you extract individual values. Use ‘Result of step N: body:parsed as JSON:first_name’ to pull specific fields from the response.

4

Populate form fields or create records

Assign each parsed value to the corresponding form input or directly create/update a database record. The entire form populates in under 2 seconds.

5

Show confidence and allow editing

Display the extracted data to the user for review before saving. Add an ‘Edit’ option on each field. This human-in-the-loop step catches the ~5% of cases where extraction is incorrect.

Advanced Patterns

Making Extraction More Powerful

🔄

Batch Extraction

If users paste multiple records (a list of contacts, multiple invoice lines), instruct the AI to return a JSON array. In Bubble, iterate over the array in a backend workflow to create one database record per extracted item.

📎

PDF Text Extraction

Combine with a PDF-to-text API (PDF.co or similar) to process uploaded documents. Extract text first, then pass to the AI extraction workflow. Fully automated invoice or document processing.

🎯

Domain-Specific Schema

Train your system prompt on industry-specific schemas — legal clauses, medical codes, financial line items. The more domain-specific your schema, the more accurate the extraction.

ROI Impact

What This Feature Is Worth

90%Reduction in manual data entry time
< 2sTime to populate a complete form
~95%Typical extraction accuracy on clean text
$0.001Approximate cost per extraction at gpt-4o-mini pricing

Want AI Data Extraction in Your Bubble.io App?

SA Solutions builds AI form-filling and data extraction features into Bubble.io applications across industries — CRM, recruitment, legal, logistics, and more.

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