Get Custom AI Automation for Your Internal Ops

Many small businesses try to automate internal tasks using the built-in features of their accounting software like QuickBooks or Xero. These tools offer basic rules, like auto-categorizing expenses from known vendors. They fail when an invoice arrives in a slightly different PDF format or requires an approval step that is not a simple yes or no. The system cannot adapt, so the process reverts to manual data entry.

Ambitious teams might try a tool like Microsoft Power Automate to connect their inbox to other systems. This works for simple, linear tasks within the Microsoft ecosystem. The moment you need to parse a complex document, call an external API with OAuth2 authentication, or manage a multi-day approval process, the workflow becomes a brittle chain of actions. Error handling is notoriously difficult, and a single failed step can halt the entire process with no clear explanation.

Consider a 15-person services firm. An operations manager receives a vendor PDF invoice in Outlook. They open the PDF, manually type the invoice number, line items, and amount into QuickBooks, save the PDF to SharePoint, and then message the project manager on Teams for approval. This 10-minute, error-prone task happens 50 times a month. That is over 8 hours of skilled labor spent on low-value data transcription.

The structural problem is that these platforms are designed for simple triggers and actions, not for stateful, intelligent processes. They cannot read documents with understanding or manage a process that might be pending for 3 days waiting for human approval. They are built to connect apps, not to run a core business operation that requires logic, persistence, and reliability.

The engagement would begin with a 1-hour discovery session to map your exact workflow. Syntora would document every step, from how a document arrives to where the final data needs to live. We would identify the specific fields to extract, the business rules for validation (e.g., cross-referencing a PO number), and the logic for routing approvals.

The technical approach would use a serverless architecture on AWS Lambda, triggered by new files in an S3 bucket. An AWS SES rule would automatically route incoming emails with attachments to this bucket. The Lambda function would use the Claude API to perform structured data extraction from the PDF, which is highly effective for varied layouts. The extracted data would be stored in a Supabase PostgreSQL table to track its state (e.g., 'pending_approval', 'approved').

The delivered system is a production-grade pipeline that runs independently. Your team interacts with it via simple actions, like replying 'approve' to a Slack message. You receive the full Python source code in your GitHub repository, a runbook for operations, and a monitoring dashboard in AWS CloudWatch. The system is designed to process up to 1,000 documents per day with an average processing time of less than 15 seconds per document.