Imagine building an application where a user uploads an invoice, and within seconds, the application extracts the invoice number, supplier details, dates, tax amounts, line items, currency and total payable amount.
You might immediately wonder: Do I need to train my own AI model? Do I need a specialized OCR engine? Or can I simply integrate an AI provider such as OpenAI?
The short answer is that you can build an invoice extraction feature using an existing AI provider's API. You don't necessarily need to train or deploy a separate AI model.
However, understanding the different approaches will help you make better architectural decisions.
1. Approach One: Use a General-Purpose AI Model
The simplest approach is to integrate an AI provider such as OpenAI.
Modern vision-capable AI models can process PDF documents and images, understand their contents and extract information into structured formats.
For example, a user uploads an invoice containing:
- Invoice number: INV-2026-001
- Supplier: ABC Supplies Ltd
- Invoice date: 5 November 2026
- Subtotal: KES 10,000
- VAT: KES 1,600
- Total: KES 11,600
You send the document to the AI API with instructions describing the information you want extracted.
The model returns something like:
{ "invoice_number": "INV-2026-001", "invoice_date": "2026-11-05", "supplier": "ABC Supplies Ltd", "currency": "KES", "subtotal": 10000, "tax": 1600, "total": 11600 } You can define a JSON Schema to ensure the response follows your application's expected structure.
The advantage is that you don't need to build an OCR engine, train a model or maintain your own machine learning infrastructure.
For an MVP, this is where I would start.
Useful documentation:
2. Approach Two: Use a Specialized Document Intelligence Service
There are also services specifically designed for extracting information from documents.
Some examples include:
Microsoft Azure AI Document Intelligence
Azure provides a prebuilt invoice model that can identify invoice-specific fields, including supplier details, invoice dates, totals, tax amounts and line items.
Prebuilt invoice model documentation
Google Document AI
Google provides specialized document processors, including an invoice parser for extracting invoice information.
Amazon Textract
Amazon Textract can extract text, forms, tables and expense-related information from documents.
AnalyzeExpense API documentation
These services become particularly interesting when dealing with:
- Large volumes of invoices.
- Different invoice layouts and templates.
- Scanned documents.
- Complex tables and line items.
- Enterprise document processing requirements.
The key difference is that these services are purpose-built for document processing, while general-purpose LLMs offer broader reasoning and interpretation capabilities.
3. Approach Three: Combine Document Intelligence and LLMs
For a more sophisticated production system, you can combine both approaches.
Consider this architecture:
Step 1: Document upload
The user uploads an invoice in PDF, PNG or JPG format.
Step 2: Document processing
A service such as Azure AI Document Intelligence extracts the text, tables, fields and document structure.
Step 3: AI interpretation
The extracted information is passed to an LLM such as OpenAI.
The LLM can normalize dates, interpret ambiguous descriptions, standardize currencies and map extracted fields to your application's data model.
Step 4: Validation
Your backend performs deterministic business validations.
For example:
- Does subtotal + tax equal the invoice total?
- Are all mandatory fields available?
- Is the invoice number already in the database?
- Does the currency match the expected format?
- Are quantities and unit prices consistent with line-item totals?
Step 5: Human review
Invoices with missing information, inconsistencies or uncertain extraction results are flagged for human review.
Step 6: Database persistence
Once validated and approved, the extracted information is saved to your database.
This separation is important: AI handles interpretation, while your backend enforces business rules.
4. What Does the Architecture Look Like?
A typical implementation could look like this:
Invoice Upload → Document Processing → AI Extraction → Validation → Human Review → Database.
Your application doesn't need to know how the underlying AI model works. It simply needs to communicate with the provider through an API.
This also means you can change providers later without rebuilding your entire application.
For example, you could start with OpenAI and eventually introduce Azure Document Intelligence if your testing demonstrates that it provides better results for your document types.
5. Do You Need to Train Your Own AI Model?
In most invoice extraction applications, no.
Training your own model introduces additional responsibilities:
- Collecting and labelling training data.
- Model training and evaluation.
- Infrastructure and deployment.
- Model monitoring and maintenance.
- Handling new invoice layouts and document variations.
Unless you have a very specific requirement that existing models cannot satisfy, this is unnecessary complexity.
Start with existing models and services. Consider fine-tuning or custom models only when you have sufficient evidence that they solve a problem existing solutions cannot handle adequately.
6. What About Accuracy?
This is where things get interesting.
An AI model returning valid JSON does not necessarily mean the extracted information is correct.
For example, a model might extract an invoice total of KES 116,000 instead of KES 11,600.
Your application should never blindly trust the model's output.
Some important safeguards include:
- Mathematical validation: Recalculate invoice totals and compare them with extracted values.
- Duplicate detection: Check whether an invoice from the same supplier with the same invoice number already exists.
- Field validation: Validate dates, currencies, quantities and mandatory fields.
- Human verification: Allow users to review and correct extracted information.
- Auditability: Keep the original document alongside the extracted data.
- Confidence handling: Use available field-level confidence information where supported, and don't treat an LLM's self-reported confidence as a calibrated probability.
For financial applications, these safeguards are not optional architectural luxuries. They are part of building a reliable system.
7. My Recommended Technology Stack
If I were building this feature today, I would start with:
| Component | Technology |
|---|---|
| Backend | Python / FastAPI |
| AI provider | OpenAI API |
| Document input | PDF, PNG, JPG |
| Structured extraction | JSON Schema |
| Database | PostgreSQL |
| File storage | Azure Blob Storage |
| Validation | Python business rules |
| Frontend | React or any existing application frontend |
I would initially avoid introducing too many services.
Build the first version using OpenAI, test it with 30-50 invoices from different suppliers, measure extraction accuracy, processing time and cost, and then decide whether a specialized document intelligence service is justified.
8. Useful Resources and Documentation
If you want to explore this further, these are good starting points:
AI and LLM APIs
Document Intelligence
Backend and Storage
Final Thoughts
The interesting thing about building AI-powered applications is that you don't always need to build or train the AI model yourself.
Sometimes, the real engineering challenge is building the system around the model.
Uploading documents, managing API requests, handling failures, validating extracted information, preventing duplicates, maintaining audit trails and integrating the results into existing business workflows are all important parts of the solution.
The AI model extracts the information. Your software makes that information useful, reliable and actionable.
That's where much of the real engineering value lies.
