AI Document Processing in Bubble.io: Extract, Analyse, and Act on Any Document
Every business handles documents — contracts, invoices, applications, reports, feedback forms. Most process them manually. Bubble.io combined with Claude AI and document extraction tools makes fully automated document processing achievable: upload a document, receive structured data, trigger the appropriate next action.
The Document Processing Architecture
Document intake
Bubble.io handles document intake via the file uploader element: users or automated systems upload PDFs, images, or Word documents to Bubble.io’s file storage (Amazon S3 via Bubble). The uploaded file gets a public URL that can be passed to external processing services. For automated intake: Make.com monitors an email inbox or a folder and uploads documents to Bubble.io via the API when they arrive, triggering the processing workflow automatically.
AI extraction
Two extraction paths depending on document type. For structured documents (invoices, forms, receipts with consistent layout): use Google Document AI or AWS Textract via Make.com — these specialised OCR tools extract data from known document structures with high accuracy. For unstructured documents (contracts, reports, emails, free-form text): pass the document text directly to Claude via the API Connector — Claude extracts the specific fields you define, understanding context and meaning rather than just layout.
Data storage and action
Claude returns the extracted data in a structured format (instruct it to respond in JSON for easy parsing). A Bubble.io workflow parses the JSON response and writes each field to the appropriate data type. The stored data triggers the next action: a contract with unusual clauses creates a review task, an invoice above a threshold creates an approval request, an application meeting the criteria advances to the next stage automatically.
Building the Invoice Processing System
Configure the extraction prompt
The invoice extraction prompt is the most important component. It defines exactly what Claude extracts and in what format. Effective prompt: ‘Extract all fields from this invoice text and return them as a JSON object with these exact keys: vendor_name, vendor_email, vendor_address, invoice_number, invoice_date (YYYY-MM-DD format), due_date (YYYY-MM-DD format), line_items (array of objects with: description, quantity, unit_price, total), subtotal, tax_amount, tax_rate, total_amount, currency, payment_terms, purchase_order_number (null if not present), notes (null if not present). If any field is not found in the document, use null. Return only the JSON object, no other text.’ The JSON-only instruction is critical — it prevents Claude from adding explanation text that would break the JSON parser.
Build the Bubble.io data model
Invoice data type: vendor_name, vendor_email, invoice_number, invoice_date, due_date, total_amount, currency, status (pending_approval / approved / paid / rejected), payment_terms, raw_document_url. LineItem data type: invoice (linked to Invoice), description, quantity, unit_price, total. Create these data types before building the workflow — the workflow will write to these fields.
Build the processing workflow
Trigger: a document is uploaded to a specific Bubble.io File field. Workflow: (1) retrieve the file URL from the database record. (2) Use a backend workflow to fetch the file content (for PDFs: call a PDF-to-text conversion API such as pdf.co or a custom Cloudflare Worker; for images: pass to Google Vision API for OCR). (3) Send the extracted text to Claude via the API Connector with the invoice extraction prompt. (4) Parse the returned JSON using Bubble.io’s detect data type feature. (5) Create an Invoice record and write each parsed field. (6) Create LineItem records for each item in the line_items array. (7) If total_amount exceeds the approval threshold: create an approval task assigned to the finance manager.
Add validation and error handling
AI extraction occasionally produces null values for fields that are actually present — either due to document quality issues or unusual formatting. Build validation: after writing the Invoice record, check for null values in required fields (vendor_name, invoice_number, invoice_date, total_amount). If any required field is null: flag the invoice for manual review, send an alert to the finance team with the document URL and the list of missing fields. Do not block the workflow — create the partial record and flag it rather than discarding the extraction attempt.
Contract Analysis: Extracting Risk and Key Terms
Contract processing is more complex than invoice processing because the relevant information is embedded in natural language paragraphs rather than structured fields. Claude’s language understanding makes it uniquely suited to this task — it can read a 40-page contract and identify the payment terms, liability caps, termination clauses, and non-standard provisions that require legal review.
The contract analysis prompt: ‘Analyse this contract and extract the following information as a JSON object: parties (array of {name, role}), contract_value (numeric, null if not specified), payment_terms (text description), contract_duration (text description), start_date (YYYY-MM-DD or null), end_date (YYYY-MM-DD or null), termination_notice_period (text description), liability_cap (text description, null if not specified), key_obligations_party_a (array of text, max 5), key_obligations_party_b (array of text, max 5), non_standard_clauses (array of {clause_description, risk_level: high/medium/low, location_in_document}), governing_law (text), dispute_resolution (text). Return only the JSON object.’
📌 The non_standard_clauses array is the highest value output of contract analysis — Claude identifies clauses that deviate from standard contract terms and rates their risk level. A contract reviewer who receives a list of non-standard clauses with risk ratings can focus their attention on the 3 to 5 items that actually require legal judgment, rather than reading the entire contract to find them.
What document types can this system process?
The system handles any document that can be converted to text: PDFs (text-based and scanned), Word documents, email content, and images of documents (via OCR). The extraction quality is highest for: well-formatted digital PDFs, typed documents, and standardised form types. Quality is lower for: handwritten documents, heavily formatted PDFs with complex layouts, and low-resolution scanned images. For the highest-accuracy extraction on structured documents like invoices and forms, combine OCR (Google Document AI or AWS Textract) with Claude refinement: OCR extracts the raw text and structure, Claude interprets and structures the extracted content.
How do I handle multi-page documents in Bubble.io?
Bubble.io’s API Connector has a request body size limit of approximately 50KB, which limits the amount of document text that can be sent in a single API call. For multi-page documents: split the document into chunks (pages or sections) and process each chunk separately, then merge the results. Alternatively, use a middleware service (a Cloudflare Worker or Python server) to handle large document processing and send only the extracted structured data back to Bubble.io. For most business documents under 20 pages: the text content fits within the API limit without chunking.
Want AI Document Processing Built in Bubble.io?
SA Solutions builds invoice processing systems, contract analysis tools, application review workflows, and any document extraction requirement in Bubble.io.
