Build an AI System to Replace a 40-Hour Work Week
Automating a full-time job requires a one-time project investment, not a recurring salary. This investment builds a custom system to handle a core, high-volume process like document processing or support triage.
The project scope depends on the task's complexity. A defined data entry role with consistent inputs is a straightforward build. A role requiring multi-step decisions and interacting with legacy systems requires more discovery and integration work.
We built a document pipeline for a regional insurance agency with 6 adjusters. The system automated the work of one full-time data entry clerk, processing over 200 claims per week. The project took 3 weeks from kickoff to launch, handling work that previously consumed 40 hours of manual labor.
What Problem Does This Solve?
Most businesses first try a no-code tool to automate a repetitive task. They see a workflow that reads an email attachment, extracts data, creates a QuickBooks entry, and notifies a Slack channel. In Zapier, that is four separate tasks. At 50 invoices per day, this single workflow burns 4,000 tasks per month, pushing you into a high-cost plan immediately.
A logistics company we worked with received 40 PDF shipping manifests daily. An employee spent 6 minutes on each one, manually copying 15 fields into their ERP. They tried a no-code OCR tool, but it choked on the 5 different layouts their top clients used and couldn't read handwritten notes. The required business logic, like applying different rate tables per carrier, forced them to build duplicate, branching paths that were impossible to debug or update.
These tools are built for simple, linear triggers. They lack the robust error handling, logging, and state management required for a business-critical process. When the tool fails silently on 5% of inputs, you still need an employee to babysit the system, find the failures, and fix them manually. This defeats the purpose of the automation.
How Does It Work?
Our first step is to analyze 50-100 of your real-world documents, covering all layouts and edge cases. We use the Claude 3 API for its visual processing capabilities, which extracts structured JSON data from PDFs regardless of their format. This approach correctly interprets varied templates and even handwritten annotations, a common failure point for traditional template-based OCR.
We then build a Python application using the FastAPI framework. This application exposes a secure API endpoint that is triggered by an AWS Lambda function when a new document arrives. The function downloads the file, sends it to the Claude API, and receives structured data back in about 8 seconds. We use the httpx library for resilient, asynchronous API calls and pydantic for strict data validation, ensuring your ERP never receives malformed data.
The complex routing logic becomes a simple Python dictionary lookup, mapping specific clients to their business rules which are stored in a Supabase database. The application makes a validated API call to your ERP, creating the new record. The entire process, from email receipt to ERP entry, completes in under 15 seconds, a massive reduction from the 6-minute manual process.
The entire service is deployed on AWS Lambda, which keeps hosting costs under $30 per month for processing over 800 documents. We implement structured logging with structlog, sending all events to a central dashboard. If the system cannot parse a document or an API call fails, it automatically sends the original file and a detailed error message to a designated Slack channel for human review, reducing the unhandled error rate to under 0.5%.
What Are the Key Benefits?
Your First Full-Time AI Employee
Automate 40 hours of weekly manual work with a system that never gets tired or makes typos. The typical build takes 3 weeks from start to finish.
Pay Once, Own Forever
A single fixed-price project, not a monthly SaaS subscription that scales with your business. Monthly hosting costs are minimal, often under $50.
The Code Lives in Your GitHub
You receive the complete Python source code and deployment scripts. There is no vendor lock-in; you can modify or extend the system anytime.
Failures Alert You Instantly
Instead of silent failures, any processing error triggers a Slack notification with the source document and error details for immediate human review.
Connects Directly to Your ERP
We build custom API integrations to your existing systems, whether it is NetSuite, a custom SQL database, or an industry-specific platform.
What Does the Process Look Like?
Week 1: Scoping and Access
You provide sample documents and read-only access to the relevant systems. We deliver a detailed technical specification and a fixed-price project plan.
Week 2: Core System Build
We build the core processing pipeline and test it against your sample data. You receive a link to a private staging environment to see it work.
Week 3: Integration and Deployment
We connect the system to your live data sources and production ERP. You receive the full source code delivered to your company's GitHub repository.
Weeks 4-8: Monitoring and Handoff
We monitor the system in production, fine-tuning for any new edge cases. You receive a technical runbook detailing system operation and maintenance.
Frequently Asked Questions
- How much does a project like this typically cost?
- Pricing depends on the document complexity and the number of systems to integrate. A project automating a single, well-defined data entry task is often a 2-4 week build. A system requiring multi-step business logic and integrations with legacy software takes longer. We provide a fixed-price quote after our initial discovery call at cal.com/syntora/discover.
- What happens if the Claude API goes down or changes?
- The system is built with automatic retry logic for transient API outages. For major API version changes, we handle the necessary code updates as part of our optional monthly maintenance plan. Because you own the code, you can also have any developer make the update. The core business logic is isolated from the external API calls.
- How is this better than hiring a Virtual Assistant (VA)?
- A VA is a human who works 8 hours a day and costs a recurring monthly salary. This AI system works 24/7, processes documents in seconds, and has a near-zero error rate. The investment is a one-time capital expense that provides a permanent asset, rather than an ongoing operational cost. The system's capacity also grows without hiring more people.
- What if our business process changes?
- The system is designed to be maintainable. Small changes, like adding a new data field to extract, are typically a few hours of development work. We handle these on an hourly basis or as part of a maintenance retainer. You also receive the full source code and documentation, so your own team can make changes if you hire an engineer later.
- Can this handle more than just PDFs?
- Yes. The core engine is a data processing pipeline. We can configure the input to be emails, web forms, spreadsheets, or even images of paper documents from a scanner. The destination can also be anything with an API: your CRM, a Google Sheet, a project management tool, or a custom database. The transformation logic in the middle is the key.
- We have sensitive data. How is security handled?
- The system is deployed within your own cloud infrastructure, like an AWS account. Your data never passes through Syntora's servers after launch. All API keys and credentials are encrypted and stored securely using AWS Secrets Manager. We also implement role-based access controls for any internal tools we build, ensuring employees only see the data they need to.
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