Pulse AI and Amazon Bedrock for automating financial document processing

Processing financial documents is a pain for any organization. Receipts, invoices, tax forms, contracts often come in different formats, handwriting, with noise in scans. AWS offered a solution: combine Pulse AI (a service for document understanding) with Amazon Bedrock (a service for customizing AI models). The result is a complete pipeline that extracts data accurately and corrects itself through fine-tuning.

Pulse AI — understanding complex documents

Pulse AI specializes in reading documents as well as a human would. It sees not just text, but also structure: table borders, field positions, information hierarchy. This is critical for financial documents, where logic is hidden in the format.

For example, an invoice contains an amount not in a random place, but in the right corner. Details are arranged in a block on the left. A table with items has its own hierarchy.

Pulse AI learns this geometry of documents and can apply it to new documents from the same source. The result is that the service extracts fields not as text search "find the word 'amount'", but as understanding "here's where the amount is always placed in this document type".

Amazon Bedrock for customization to your data

Amazon Bedrock is a platform where you select a base model (Claude, Llama, and others) and customize it for your task through fine-tuning. In the context of financial documents, this works in two stages:

Extraction: After the first pass through Pulse AI, you collect examples of errors and successes. You feed them to Bedrock — the model learns to extract the required fields more accurately.

Validation and context: A fine-tuned model remembers what "amount" or "payment date" means for your organization. It can check that the amount is within reasonable bounds, the date has the correct format, details match your counterparty directory.

Full pipeline: from document to structured data

The entire process looks like this:

• Uploaded document goes through Pulse AI — structure analysis and field extraction
• Result is fed into Amazon Bedrock (fine-tuned model) for validation and normalization
• Model checks that all data is correct and matches your business logic
• If an error occurs — it is sent back to the pipeline as an example for retraining
• Output: structured, verified data in JSON or CSV formats

Practical result: an accountant or controller no longer reworks half of the extracted data by hand. The system does this automatically and learns from its own mistakes.

What this means

For finance teams, this is savings of weeks or months of work. Instead of manually processing stacks of documents — automation with corporate accuracy. For developers, this means you can build reliable financial document processing systems without code complexity — just data + platform + fine-tuning. For IT organizations, this is a path to faster deployment: AWS is already on the infrastructure, Bedrock is built in — no need for separate services.