Automate Your Firm's Invoice Processing with Python
Automate invoice processing by building a Python service that extracts data from PDFs using OCR. The service then matches line items to your chart of accounts and creates draft entries.
We built this exact pipeline for a 15-person accounting firm. They were spending six minutes manually entering each invoice. The automated system now processes an invoice every eight seconds, dropping their data entry error rate from 9% to less than 1%.
This is not a simple script. It is a production-grade system built to handle business-critical workflows. The scope involves connecting to your email inbox, parsing varied PDF layouts, integrating with your accounting software, and providing real-time alerts when an invoice cannot be processed automatically.
What Problem Does This Solve?
Most accounting firms first try their accounting software's built-in document scanner. QuickBooks's receipt capture works for a single-item receipt from a gas station, but it fails on a multi-page, multi-line-item invoice from a supplier. It misinterprets tables, combines line items, and cannot correctly match a vendor's product description to your internal chart of accounts.
A typical next step is a point-and-click automation tool. The workflow seems simple: when an email with an attachment arrives, send the file to an OCR service, then create a QuickBooks entry. But these platforms fail silently. An OCR service might time out on a large 10-page PDF, and the workflow just stops. There is no alert and no retry. An accountant discovers the unprocessed invoice days later, creating a backlog and damaging client trust.
These tools also lack the logic for complex matching. They cannot handle a vendor invoice that lists '10ft 2x4 Lumber' and map it to your QuickBooks account 'Cost of Goods Sold:Building Materials:Wood'. This requires fuzzy text matching and contextual understanding, which is beyond the scope of simple if/then automation paths.
How Does It Work?
Our process begins by setting up an AWS Lambda function triggered by new objects in an S3 bucket. When an invoice PDF arrives via email, it is automatically saved to S3, triggering the pipeline. We use AWS Textract for OCR because its table and form detection preserves the structure of line items, unlike basic text extraction tools. This initial step takes 3-5 seconds.
The structured output from Textract is then passed to the Claude API. We use a carefully engineered prompt to instruct the model to return a clean JSON object containing the vendor name, invoice date, total amount, and a detailed array of line items. Using an LLM for extraction means the system handles new vendor layouts without requiring custom code for each one. This extraction and structuring step completes in under 3 seconds.
With the structured data, a Python function using the `fuzzywuzzy` library matches each extracted line item against your firm's chart of accounts, which we cache in a Supabase table for fast lookups. We require a match confidence of over 85% to proceed. The validated data is then posted directly to the QuickBooks Online API as a draft journal entry using the `httpx` library for reliable, asynchronous requests. The system tracks the status of every invoice ('received', 'processing', 'complete', 'error') in Supabase.
The entire FastAPI application is deployed on AWS Lambda, ensuring you only pay for compute time when invoices are actually being processed. We use `structlog` for structured logging and `tenacity` for retry logic. If an invoice fails after three attempts, a CloudWatch Alarm sends a detailed report to a designated Slack channel, including the invoice ID and error message. For a volume of 2,000 invoices per month, hosting costs are typically under $40.
What Are the Key Benefits?
Process Invoices in 8 Seconds, Not 6 Minutes
Reduce manual data entry time by over 98%. Your team reclaims hours each day to focus on high-value client advising instead of tedious transcription.
Pay Once for the Build, Not Per Invoice
A single, fixed-price project replaces unpredictable monthly bills from per-task automation platforms. Monthly AWS hosting costs are minimal after launch.
You Get the Keys to the GitHub Repo
We deliver the complete Python source code and all deployment scripts. There is no vendor lock-in. It is your system, running in your cloud account.
Know About Errors Before Your Clients Do
Automated CloudWatch alerts notify a Slack channel the moment an invoice fails to process after all retries. No more silent failures discovered days later.
Integrates with QuickBooks and Your Email
The system ingests PDFs from any email provider, stores them in your AWS S3 bucket, and posts draft entries directly to your QuickBooks Online account.
What Does the Process Look Like?
Discovery and Scoping (Week 1)
You provide 15-20 sample invoice PDFs from different vendors and grant read-only access to your QuickBooks chart of accounts. We deliver a technical plan confirming the extraction and matching logic.
Core Pipeline Build (Weeks 2-3)
We build the end-to-end Python pipeline from email ingestion to QuickBooks posting. You receive a private staging environment to test with your own sample invoices.
Deployment and Integration (Week 4)
We deploy the system to your AWS account and connect it to your live email inbox and QuickBooks instance. You receive a list of the first 50 successfully processed invoices for verification.
Monitoring and Handoff (Weeks 5-8)
We monitor system performance for 30 days, fine-tuning logic as needed. You receive the full source code, deployment documentation, and a runbook covering common operational tasks.
Frequently Asked Questions
- How much does a custom invoice processing system cost?
- Pricing depends on the number of unique invoice layouts and the complexity of your chart of accounts. A firm with 20 consistent vendors is simpler than one with 200 ad-hoc suppliers. A typical project is a 4-6 week engagement. We provide a fixed-price quote after the initial discovery call, where we review your sample invoices and technical requirements.
- What happens if an invoice is scanned badly and the AI can't read it?
- The system tries to process every PDF three times. If all retries fail, it moves the original PDF to an 'error' folder in S3 and forwards the original email to a designated manual review inbox. A Slack alert is also sent with the file name. This ensures no invoice is ever lost and provides a clear queue for human intervention.
- How is this different from using an AP automation tool like Bill.com?
- AP automation platforms are full suites for managing vendor payments and approvals. Our system is not. It is for firms that have an existing process and want to eliminate only the data entry bottleneck. We build the single engineered component that gets invoice data into your accounting system accurately and quickly, without replacing your entire workflow.
- What is required to handle invoices from a new vendor?
- The Claude API handles new layouts without code changes in over 95% of cases. For a truly unusual format, the extraction prompt may need a minor update. This is a 15-minute change we can make. You just send us the new invoice PDF, and we deploy the updated prompt. No major development is required to onboard new vendors.
- How is our firm's financial data kept secure?
- All data resides within your own AWS account, giving you full control. Invoice PDFs are stored in a private S3 bucket with server-side encryption. Credentials for QuickBooks and other services are stored in AWS Secrets Manager, not in the code or configuration files. We build the system in your cloud environment so you maintain complete data sovereignty.
- What is the accuracy rate for the data extraction?
- We target over 99% field-level accuracy for key data like invoice numbers, dates, and totals. Line item details are typically over 95% accurate. The system calculates a confidence score for each extracted document. Any invoice with a score below our quality threshold is automatically flagged for manual review instead of being posted incorrectly to QuickBooks.
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