Stop Manually Processing Invoices with a Custom Voice AI System
Custom voice AI systems transcribe and extract data from AP documents with near-perfect accuracy. Off-the-shelf services struggle with non-standard invoice formats and require frequent manual review.
The main difference is adaptability. SaaS tools rely on predefined templates that fail when a vendor sends a scanned PDF or a multi-page bill of lading. A custom system is trained on your specific documents, learning to handle the messy, inconsistent formats that are common for small businesses.
We built an accounts payable pipeline for a 15-person logistics company processing 600 invoices a month. Manual entry took their clerk over 6 minutes per document. Our system, built in 3 weeks, processes each one in 8 seconds and flags outliers for review.
What Problem Does This Solve?
Most small businesses first try a SaaS AP tool like Dext or Bill.com. These work well for clean, standardized invoices from major suppliers. The problem is that a significant portion of a small business's invoices are not standard. They come as low-resolution scans, photos from a phone, or PDFs with handwritten notes. These SaaS tools fail on 20-30% of these documents, forcing the accounting team back into manual data entry and defeating the purpose of automation.
A more technical team might try using a platform like Amazon Textract. Textract is a powerful OCR engine, but it is not a solution in a box. It returns a massive JSON file of raw text and coordinates, leaving you to write hundreds of lines of code to parse the output, identify fields like 'Invoice Number' or 'Total Due', and then integrate that with your accounting software. Without an engineer, it is a non-starter.
This leaves the business stuck. A 12-person construction supplier we worked with was in this exact spot. They tried an off-the-shelf tool, but 4 of their 10 biggest material suppliers sent multi-page, scanned invoices the tool could not read. Their bookkeeper spent more time correcting extraction errors and manually processing the failures than she did with her original paper-based process.
How Does It Work?
Our process begins with a set of 50-100 of your actual accounts payable documents, including the most difficult formats. We use the Claude 3 Sonnet API to perform intelligent extraction, which goes far beyond simple OCR. We work with you to define a rigid JSON schema that matches the fields in your accounting system, including vendor name, invoice date, line items, and total amount.
We build the core logic in Python, using the Anthropic SDK. The key is crafting a specific prompt that provides the AI with examples of your invoice formats. This enables the model to correctly parse new documents from the same vendors, even with slight variations. The entire pipeline, from receiving an email with a PDF attachment to having structured JSON data ready, executes in under 8 seconds.
This Python script is wrapped in a FastAPI service and deployed on AWS Lambda. This serverless architecture means you only pay for the exact compute time used, typically costing less than $30 per month to process over 5,000 documents. We configure the system to trigger automatically when a new invoice arrives in a dedicated email inbox or is uploaded to a specific cloud folder.
The final step is integration. The extracted and structured data is pushed directly to your accounting software's API, such as QuickBooks Online or Xero. For the first 30 days, we route any extraction with a confidence score below 99% to a Supabase dashboard for a quick one-click human approval. You receive the complete source code in your GitHub, along with a runbook for maintenance.
What Are the Key Benefits?
From PDF to QuickBooks in 8 Seconds
Manual entry takes 6 minutes per invoice. Our system processes, extracts, and posts data before you can open the next email.
Fixed-Price Build, Pennies Per Document
One-time development cost and near-zero running fees on AWS Lambda. Avoids monthly per-seat or per-document SaaS subscriptions that penalize growth.
You Own the Code, Not a Subscription
You get the full Python source code in your company's GitHub repository. You are not tied to our service or any proprietary platform.
Monitoring with Proactive Slack Alerts
We use structlog for logging. If Claude's extraction confidence score drops below 95% on 3 consecutive documents, you get an immediate Slack alert.
Connects Directly to Your Systems
We build direct API integrations to your accounting software and use a Supabase dashboard for review. No new software for your team to learn.
What Does the Process Look Like?
Document & System Audit (Week 1)
You provide 50-100 sample AP documents and grant temporary API access to your accounting system. We deliver a defined data schema for extraction.
Core Extraction Engine Build (Week 2)
We build the Claude API pipeline and tune it on your documents. You receive a report showing extraction accuracy for each major vendor format.
Integration & Deployment (Week 3)
We deploy the system on AWS Lambda and connect it to your systems. You receive access to a staging dashboard to review the first 100 automated entries.
Live Monitoring & Handoff (Week 4)
The system goes live. After a 30-day monitoring period, you receive the full source code, API documentation, and a maintenance runbook.
Frequently Asked Questions
- How much does a custom AP automation system cost?
- The cost depends on the number of unique vendor invoice formats and the complexity of the accounting system integration. A typical build for a business with under 20 primary vendors connecting to QuickBooks Online takes 3-4 weeks. A more complex project with 50+ vendor formats and a custom ERP integration requires a larger scope. We provide a fixed-price quote after our discovery call.
- What happens if the AI misreads an invoice?
- The system is designed for high accuracy, but errors are possible. We build a validation step where any extraction with a confidence score below a 95% threshold is flagged for manual review in a simple web dashboard. This prevents incorrect data from entering your accounting system. The system can also learn from corrections to improve future performance on that vendor's format.
- How is this different from using a tool like Dext or Bill.com?
- Those are excellent SaaS products for standardized invoices. Syntora builds a system for businesses whose documents do not fit those neat templates. We handle scanned documents with handwritten notes, industry-specific forms like bills of lading, and multi-page supplier agreements. You are not renting a generic tool; you are owning a custom-built asset tailored to your specific operational documents.
- Can this system handle handwritten notes or signatures?
- Yes. The core technology uses advanced OCR to first convert any image-based document into text. Then, the Claude AI model interprets that text, including handwritten numbers or notes. We have successfully processed documents with a mix of typed text, handwritten PO numbers, and stamped approval marks. The key is providing enough examples during the build phase for the model to learn your specific patterns.
- What kind of maintenance is required after handoff?
- The AWS Lambda and Supabase infrastructure is serverless and requires almost no oversight. The primary maintenance task is occasional prompt tuning if a major vendor completely overhauls their invoice format. This process is documented in the runbook you receive and typically takes a developer less than an hour. We also offer an optional flat monthly plan to handle this for you and monitor for any API changes.
- Does this work for international invoices with different languages?
- Yes, the Claude 3 model family has strong multi-lingual capabilities. We have built systems that process invoices in Spanish, French, and German, correctly extracting line items and identifying currency symbols like € and £. During the initial audit, you provide samples of any international documents, and we confirm the model's accuracy on them before starting the build.
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