Build Accounting Automation That Handles Real-World Complexity
Custom Python workflows handle complex, multi-step accounting logic that no-code platforms cannot. They also run at a fraction of the cost for high-volume tasks like invoice processing.
The main difference is control over business logic and error handling. No-code tools are excellent for simple A-to-B data transfers. But accounting requires rules for taxes, refunds, and prorations. A custom workflow gives you precise control over these rules, while no-code tools force you into brittle, complex branching paths.
We built a reconciliation system for a 25-person SaaS company that was manually matching Stripe charges to QuickBooks invoices. Their new system processes 1,500 monthly invoices with 99.8% accuracy. The build took 3 weeks and eliminated 10 hours of manual work per week for their bookkeeper.
What Problem Does This Solve?
Most finance teams start with a no-code platform to connect their payment processor to their general ledger. It works for the first 100 customers. Then, complexity grows. The platform charges per task, so a single invoice that requires checking for a customer match, applying the right tax rate, and handling a discount code burns 3-5 tasks. At 2,000 invoices a month, this becomes 10,000 tasks and a surprisingly high monthly bill.
The bigger issue is silent failure. What happens when the accounting API is down for five minutes? The workflow fails. You get an email, but the transaction is simply dropped. There is no automatic retry with exponential backoff. At a 1% failure rate, you are manually re-running 20 failed jobs per month, which is exactly the work you wanted to automate.
A regional B2B services firm tried to automate invoicing from their project management tool. The workflow triggered when a project was marked complete, but often before final hours were logged. The no-code tool would fire immediately, creating a $0 invoice. Their only option was a fixed 1-hour delay, which slowed down all invoicing and still sometimes failed. True stateful logic, like polling an API until a condition is met, is impossible.
How Does It Work?
We begin by mapping your entire financial workflow, from payment processing to final reconciliation. We use the Stripe and QuickBooks Online API documentation to identify every field, including custom fields for revenue recognition or sales commissions. We then pull the last 6 months of historical transaction data to use as a test suite, ensuring we cover every edge case.
The core logic is written in Python using the `httpx` library for fast, asynchronous API calls. Complex conditional logic for tiered pricing or state taxes, which might require a dozen fragile branches in a no-code tool, becomes a single 30-line Python function. We use Pydantic to validate every incoming webhook payload, ensuring that malformed data from an API never corrupts your accounting records.
The code is deployed as an AWS Lambda function, exposed via a secure API Gateway webhook. This serverless design costs less than $25 per month to process over 5,000 invoices. For API errors, we implement automatic retries. For data validation errors, the failed payload is logged to a Supabase table and a notification is sent to a designated Slack channel. This creates a dedicated queue for review, turning unknown failures into a manageable process.
We use `structlog` for structured logging, which allows for easy debugging. We can build a simple Retool dashboard on top of the Supabase failure queue. This gives your finance team a one-click interface to review and re-process any transaction that requires manual intervention. The entire build, from kickoff to live deployment, takes 15 business days, and end-to-end processing for each transaction is under 800ms.
What Are the Key Benefits?
Your Books Reconciled in Milliseconds
A 3-week build replaces brittle workflows. Each transaction syncs in under a second, not after a 5-minute polling delay from a no-code platform.
Pay for Execution, Not for Tasks
A workflow processing 5,000 monthly invoices costs under $25 in AWS Lambda fees, not hundreds on a no-code plan that charges per step.
You Get the Keys and the Code
We deliver the full Python source code to your GitHub repository, complete with a runbook for maintenance. There is no vendor lock-in.
Know About Errors Before Your Accountant
Failed transactions trigger instant Slack alerts and are queued in a Supabase table for review, not buried in an obscure email log.
Connects to Your Actual General Ledger
Direct API integration with QuickBooks, Xero, NetSuite, and industry-specific ERPs. We handle custom fields and complex tax mappings.
What Does the Process Look Like?
Workflow Audit (Week 1)
You grant read-only API access to your payment processor and accounting software. We map your current workflow and identify every edge case and failure point.
Core Logic Build (Week 2)
We write the Python service and test it against 6 months of your historical data. You receive a test log showing how it handled refunds, disputes, and prorations.
Deployment & Parallel Run (Week 3)
We deploy the system on AWS Lambda and run it in parallel with your old process for 5 days. You receive a daily reconciliation report comparing outputs.
Handoff & Monitoring (Week 4+)
After confirming accuracy, we switch off the old process. You receive the full source code, a runbook, and 90 days of included monitoring and support.
Frequently Asked Questions
- What factors determine the cost and timeline?
- The primary factors are the number of systems to integrate and the complexity of the business logic. A simple Stripe-to-QuickBooks sync is a 2-week build. Adding a second payment processor or custom commission logic might extend it to 3-4 weeks. We provide a fixed-price quote after the initial discovery call, so the price never changes.
- What happens if the Stripe API changes without warning?
- This is a key reason we use Pydantic for schema validation. If Stripe adds or removes a field in their webhook payload, our service will reject the data, log the schema mismatch error to Supabase, and send a Slack alert. This prevents corrupted data from entering your books. The system fails safely, and we can deploy a fix within hours under a support plan.
- How is this different from hiring a freelance developer on Upwork?
- Syntora delivers a production-ready, maintainable system, not just a script. This includes structured logging, automated alerting, infrastructure-as-code for deployment, and a detailed runbook. We build for handoff, assuming you may have your own team take over one day. Freelance projects often skip these production necessities, leaving you with code that is hard to maintain and monitor.
- Are there accounting tasks you don't automate?
- We focus on automating data movement and transaction processing between systems with APIs. We do not build systems for tasks requiring subjective judgment, like accounts receivable collections strategy or complex financial forecasting. Our work automates tedious data entry so your finance team can focus on higher-level analysis. We are not a replacement for a CPA or fractional CFO.
- How do you handle sensitive financial data and API keys?
- We never store credentials in code. All API keys and secrets are managed using AWS Secrets Manager and injected into the Lambda environment at runtime. Access is governed by least-privilege IAM roles. We only request read-only access during the build phase and can work with sandboxed data until final deployment. You control all credentials and can revoke our access anytime.
- Can this handle our volume? We process over 50,000 invoices a month.
- Yes. The AWS Lambda architecture scales automatically with demand and can handle thousands of concurrent requests without any changes to the code. We have built systems that process over 200,000 events per month. The cost-per-transaction actually decreases at higher volumes due to AWS pricing tiers. We would confirm throughput requirements during our discovery call.
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