Build Production Python Automation for Your Business
Replace no-code workflows when they require complex logic, custom error handling, or sub-second latency. Custom Python automation is for business-critical processes where task-based billing becomes prohibitively expensive.
Syntora designs and builds custom Python automation systems to replace complex Zapier workflows in business-critical processes. Our engineering engagements focus on creating tailored, scalable solutions for specific data transformation and integration needs.
A build's scope depends on API complexity and data transformation needs. Connecting two modern APIs with structured JSON is a straightforward project. Integrating a legacy system via CSV exports or parsing unstructured data from emails requires significant data preparation and custom logic.
What Problem Does This Solve?
Many SMBs start with point-and-click automation tools. They are excellent for simple, linear tasks. The problems begin when a workflow becomes central to the business. A 15-person logistics company that uses a multi-step workflow for freight quotes is a common example. A customer submits a form, which must check a TMS for capacity, hit a third-party API for pricing, and email a quote.
In a no-code tool, this workflow is a series of brittle, sequential steps. If the TMS lookup fails but the pricing API succeeds, the entire process halts. Creating a separate branch for this one failure case doubles the task count and complexity. You cannot merge branches, so every potential error requires duplicating large parts of the workflow, making it impossible to maintain.
This approach also fails on cost and performance. The quote workflow burns 5 tasks per request. At 300 requests per day, that is 45,000 tasks per month, forcing a high-cost subscription for one process. The 5-15 second polling delay for triggers means customers wait for their quote, and when one fails, there is no central log to debug the issue efficiently.
How Would Syntora Approach This?
Syntora's approach would begin by auditing the client's current workflow to map the entire process into a state machine diagram. This discovery phase clarifies data flows, decision points, and potential integration challenges.
For event-driven automations, the system would typically use AWS Lambda functions written in Python, triggered by events like form submissions or API calls. This architecture aims for sub-50ms cold start times and provides cost-effective execution, scalable to high volumes of requests.
The core logic would be built as a FastAPI service. This service would use httpx for concurrent, asynchronous calls to external APIs, optimizing for reduced I/O wait time. Pydantic would be integrated for strict data validation on all API responses, mitigating the risk of bad data from partner systems. Every processing step would be logged with structlog to a Supabase table, creating a complete and searchable audit trail.
For unstructured data intake, such as quote requests arriving as emails, Syntora would integrate the Claude API to parse the text and extract structured data like origin, destination, and freight type. We've built document processing pipelines using Claude API for financial documents, and the same pattern applies to extracting critical information from these unstructured communications. This extracted JSON data would then feed directly into the FastAPI service, unifying intake channels.
The delivered service would be containerized with Docker and deployed on AWS Lambda using the Serverless Framework. CloudWatch alarms would be configured to trigger notifications for performance anomalies, such as elevated latency or error rates. A client-accessible dashboard would be provided for monitoring throughput and reviewing the audit log.
What Are the Key Benefits?
Sub-Second Responses, Not 15-Second Delays
Your process runs in under 400ms from trigger to completion. This speed is critical for customer-facing workflows like quote generation or support ticket routing.
$25/Month Hosting, Not $450/Month in Tasks
Pay for compute time on AWS Lambda, not per-task fees. A high-volume workflow processing 45,000 requests a month costs less than a daily coffee.
You Get the GitHub Repo and Runbook
The entire Python codebase and deployment configuration are delivered to your private GitHub. You own the intellectual property and can have any developer maintain it.
Know Instantly When Something Fails
We configure CloudWatch alarms to send Slack alerts on high error rates or latency spikes. You are not debugging by manually inspecting a list of past runs.
Integrate Any API, Not Just Pre-Built Apps
We write Python code to connect to any system with an API, including internal databases or legacy SOAP endpoints that no-code platforms do not support.
What Does the Process Look Like?
Workflow Audit (Week 1)
You provide API keys and a screen-share walkthrough of the existing process. We deliver a technical specification and state diagram for the new system.
Core Logic Build (Weeks 2-3)
We build the Python service and unit tests in a shared environment. You receive a private GitHub repository with access to the code as it is written.
Deployment & Parallel Run (Week 4)
We deploy the system to AWS and run it alongside your old workflow. You get a dashboard to compare outputs and confirm 100% accuracy before switching over.
Handoff & Monitoring (Weeks 5-8)
After retiring the old process, we monitor performance and error rates for 4 weeks. You receive the final runbook, system documentation, and a post-launch support plan.
Frequently Asked Questions
- What does a custom automation build typically cost?
- Pricing is scoped based on the number of systems to integrate and the complexity of the business logic. A simple two-API connection might take 3 weeks, while a multi-step process with a Claude API integration for document parsing could take 5-6 weeks. After a discovery call, we provide a fixed-price proposal with a detailed scope of work.
- What happens if a third-party API we rely on goes down?
- The Python code includes built-in retry logic with exponential backoff for transient API errors. For a hard outage, the function places the failed job into a dead-letter queue (DLQ) on AWS SQS. You get an alert, and once the external API is back online, we can re-process all the failed jobs from the queue with a single command, ensuring no data is lost.
- How is this different from hiring a freelancer on Upwork?
- Syntora builds production-grade systems, not just scripts. This includes structured logging, automated testing, infrastructure-as-code for deployment, and monitoring with alerts. The person you talk to on the discovery call is the engineer who writes the code. There is no project manager or hand-off, ensuring context is never lost and you get a system built to be maintained for years.
- Can we make changes ourselves after the project is done?
- Yes. You receive the full source code in your GitHub and a runbook explaining the architecture. The code is standard Python using the FastAPI framework. Any competent Python developer can understand, maintain, and extend the system. We use common, well-documented libraries specifically so you are not locked into our service for future changes.
- Our process involves reading data from PDFs. Can you automate that?
- Yes. We use the Claude API for its advanced document understanding capabilities. We can build a workflow where you upload a PDF to a specific folder, and a Lambda function sends it to Claude for data extraction. The structured JSON output is then used to kick off the rest of the automation, connecting unstructured documents to your structured systems.
- Is this overkill for a small 5-person company?
- It depends on the process. If the process is a core revenue-generating function like quoting or order processing and it's failing, it is not overkill. The goal is to solve a business problem that is costing you money or customers. If a 1-hour-per-day manual task can be fully automated, a 5-person team gets back 5 hours a week.
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