Stop Paying the Tax on Manual Work
Taxes become your biggest “expense” when your team's manual work costs more than your software stack. This happens when you spend over 100 hours per month on repetitive tasks like data entry.
A custom AI system addresses this by replacing manual processes with code. The scope depends on document complexity and the number of integrations. A business processing a few hundred uniform invoices into one accounting system is a 2-week build. A firm handling thousands of variable-format legal documents for multiple internal platforms is a 4-week project.
We built a document processing pipeline for a 12-person logistics firm. Two employees spent 60 hours a month manually entering data from 800 monthly freight invoices into their ERP. The new system processes each invoice in 8 seconds with 99% accuracy and was fully deployed in 3 weeks.
What Problem Does This Solve?
The 'tax' starts small. You hire an operations person to handle invoices or customer support tickets. They spend a few hours a day copying and pasting data between a PDF, a spreadsheet, and your CRM. As the business grows, you hire another person. Soon, two full-time employees are dedicated to manual entry, and the cost of their salaries becomes a major line item.
A common mistake is trying to solve this with off-the-shelf OCR tools. These services extract raw text but fail to understand context; they cannot reliably distinguish a 'PO Number' from an 'Invoice Number' on varied layouts. The result is a messy text file that still requires a human to find the right data points, defeating the purpose of the tool.
Another path is a no-code platform. You might build a workflow that triggers on a new email attachment, sends it to an OCR tool, and then uses conditional logic to route the data. This works for 10 documents a day. At 100 documents a day, you discover each multi-page PDF burns 20 tasks. Your bill unexpectedly jumps to $400 per month for a single, brittle workflow that times out on large files and has no effective error handling.
How Does It Work?
We begin by analyzing a sample of 50-100 of your real-world documents. We use Python and the pdfplumber library to extract all text and layout data, creating a clean input for the language model. We define a rigid Pydantic schema for the output, ensuring every result is structured, typed, and validated before it is sent to your systems.
The core of the system is an API call to the Claude 3 Sonnet model. We engineer a specific prompt that instructs the model to act as a data entry specialist, locate 10-15 specific fields in the text, and return the data in a structured JSON format. This entire process, from receiving the PDF to returning the JSON, executes in under 8 seconds. For a regional insurance agency, we reduced their claims intake processing time from 7 minutes to just 10 seconds.
This logic is wrapped in a FastAPI application and deployed as a serverless function on AWS Lambda. This architecture costs less than $50 per month to process over 10,000 documents. We use Supabase for logging every transaction and its outcome, creating a 90-day searchable audit trail. The source code is delivered to your company's private GitHub repository.
Finally, we connect the API to your existing software. For a recruiting firm processing 400 applicants a month, we built a webhook that listens for new emails in Greenhouse. When a resume PDF arrives, our API extracts candidate details and uses the Greenhouse API to create a complete candidate profile in 20 seconds. We use httpx for asynchronous API calls and configure structlog to send alerts to a dedicated Slack channel if error rates exceed 1%.
What Are the Key Benefits?
Live in 15 Business Days
From our first call to a production-ready system in just 3 weeks. You see results and a reduction in manual work in the same quarter.
One-Time Build, No Per-User Fees
We complete a fixed-price project and you own the code. After launch, you only pay for cloud hosting, not a recurring SaaS subscription that grows with your team.
You Own the Code and Infrastructure
You receive the full Python source code in your GitHub. The system runs in your AWS account, so your data never leaves your control.
Monitoring That Catches Failures First
We configure AWS CloudWatch alerts that notify us if processing time or error rates spike. We often fix issues before your team even notices them.
Integrates Natively with Your CRM and ERP
The system connects directly to platforms like Salesforce, HubSpot, or NetSuite via their standard APIs. No new software for your team to learn.
What Does the Process Look Like?
Discovery and Scoping (Week 1)
You provide a sample of 50-100 documents and read-only access to the target system. We deliver a technical proposal detailing the exact fields to be extracted and the integration plan.
Core System Build (Week 2)
We write the Python code for data extraction and build the FastAPI endpoint. You receive a private API endpoint to test with your own documents and validate the output.
Integration and Deployment (Week 3)
We deploy the system to your AWS account and connect it to your production software. You receive the complete source code and a live demonstration with your team.
Monitoring and Handoff (Weeks 4-6)
We actively monitor performance and error rates for two weeks post-launch. You receive a runbook with documentation and instructions for maintenance and troubleshooting.
Frequently Asked Questions
- What factors determine the cost of a custom build?
- The primary factors are the number of unique document layouts to support and the number of fields to extract. A project with five similar invoice formats is simpler than one with thirty different legal contract types. Integrating with a modern API is also more straightforward than connecting to an older, on-premise system. We provide a fixed-price quote after the initial discovery.
- What happens if the AI cannot read a document correctly?
- If the Claude API returns a low-confidence score or the data fails our Pydantic validation, the system automatically flags it. The original document and the error message are sent to a designated email address or Slack channel for manual review. This ensures 100% of documents are processed, with difficult ones (typically under 2%) routed to a human.
- How is this better than an off-the-shelf document parsing tool?
- Off-the-shelf tools are generic. They force you into their workflow and charge per-seat or per-page fees that grow over time. Syntora builds a system for your exact process. You own the code, it runs on your infrastructure, and you are not locked into a vendor. The one-time build cost is often less than a single year's subscription to a SaaS alternative.
- Do we need an engineer on staff to maintain this?
- No. The system is designed for minimal maintenance. The AWS Lambda deployment is serverless, so there are no servers to manage. We provide a runbook that a non-technical operations person can follow for common issues. For code-level changes, we offer an optional flat monthly support plan.
- What kind of accuracy can we expect?
- For typed documents with consistent layouts, we consistently achieve over 99% accuracy at the field level. For handwritten notes or highly variable, low-quality scans, accuracy can drop to 90-95%. We determine a precise benchmark for your specific documents during the paid discovery phase before you commit to the full build.
- How do you handle sensitive data like PII or financial information?
- The system runs entirely within your own cloud environment (AWS). Your documents are never stored on Syntora's servers. We process data in-memory and pass it directly to your target systems. We can also integrate with Anthropic's Business Associate Agreement (BAA) for HIPAA compliance if you handle protected health information.
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