Automate Your Back Office with Custom AI Systems

Many SMBs first try off-the-shelf OCR software to automate document processing. These tools are good at extracting raw text but fail to understand business context. The software might extract a dollar amount but can't reliably distinguish between the 'Subtotal', 'Tax', and 'Total Amount' fields across different invoice layouts, leading to a manual review rate of over 20%.

A 15-person logistics company faced this issue. They received 80 vendor invoices per day as PDF attachments. Their OCR tool correctly extracted text but failed to structure it, leaving an administrator to manually copy and paste invoice numbers, due dates, and line items into their accounting system. This process took 3 hours daily and was the primary source of payment errors, as OCR mistakes on 10-15 invoices a day required correction.

Internal automation features within CRMs or ERPs also fall short. They are designed to trigger actions based on structured internal data, like changing a deal stage. They cannot ingest and understand unstructured external documents like a multi-page PDF claim form or a customer complaint email. This leaves a critical gap where manual work is the only option.

Syntora would begin an engagement by conducting a detailed discovery phase. We would work with your team to collect 100-200 representative source documents for each type needing automation. Using the Claude API, Syntora would analyze these documents to identify all required data fields – typically 20-30 per document type – and map them precisely to the fields in your existing back-office systems like CRM, ERP, or accounting platforms. This initial analysis is critical for defining the robust extraction logic.

The custom system Syntora would build centers on a Python-based processing pipeline, designed for deployment on AWS Lambda. Your team would provide an ingestion point, such as anS3 bucket. Upon file arrival, a Lambda function would trigger, utilizing PyMuPDF for efficient text extraction before passing the content to the Claude API. Syntora engineers would craft specific prompts to ensure the model returns structured JSON objects. Drawing from our experience with similar document processing architectures, this setup is designed for high accuracy on predefined data fields.

The structured data would then integrate directly with your chosen back-office software. Syntora would implement asynchronous API calls using the httpx library, ensuring secure data writing and including retry logic for network resilience. All successful transactions and processing errors would be logged to a Supabase database for a complete audit trail.

Monitoring would be established using structlog for structured logging, feeding into AWS CloudWatch. Syntora would configure alerts for critical failures, such as invalid API keys or schema validation failures. For volumes up to 5,000 documents monthly, the estimated cloud infrastructure cost is typically under $50. A build of this complexity generally spans 8 to 12 weeks, with deliverables including the deployed infrastructure, source code, detailed documentation, and monitoring access.