Build a Production Claude Wrapper for Internal Document Analysis
You build a Claude API wrapper using a Python web framework like FastAPI to create a private endpoint. This endpoint manages document preprocessing, system prompts, API calls, and structured output parsing.
Syntora offers expertise in building Claude API wrappers for internal document analysis, focusing on robust architecture and operational efficiency. We design systems that handle complex document types, providing predictable data extraction and cost-effective processing. Our approach emphasizes a tailored technical solution rather than a pre-packaged product.
For a production environment, a system of this nature requires robust handling of caching, cost tracking, and error management. The architectural complexity scales with your document types; accurately parsing structured PDFs with tables demands a different approach than summarizing plain text files, and the system must reliably process both. Syntora helps organizations architect and build these specialized systems. We have experience developing document processing pipelines using Claude API for sensitive financial documents, and these same technical patterns apply to internal document analysis across various industries.
What Problem Does This Solve?
Most teams start with a simple Python script using Anthropic's SDK. This works for a one-off proof of concept, but it breaks in production. It lacks caching, so you pay to re-analyze the same document multiple times. It has no robust error handling, so a temporary network glitch or an API rate limit error crashes the entire process. Large documents that exceed Claude's context window cause silent failures or incomplete analysis.
A common next step is an internal app builder like Retool. This seems promising for creating a user interface, but the business logic quickly becomes unmanageable. Complex document chunking, conditional prompting, and multi-step API calls get crammed into long Javascript queries that are difficult to debug and impossible to version control. When the logic needs to change, it requires untangling a 500-line script inside a GUI text box.
We saw this with a regional insurance agency that had 6 adjusters handling 200 claims per week. Their Python script to extract data from claim forms would time out on any document over 10 pages. When two adjusters ran it simultaneously, they hit API rate limits, halting all work for an hour. With no logging, they had no idea what caused the failure until someone manually checked the Anthropic billing dashboard.
How Would Syntora Approach This?
Syntora would begin an engagement by collaborating with your team to precisely define the fields required from each document type. We would formalize these requirements into strict output schemas using Pydantic. For optimal data extraction from PDFs, we would integrate a library like PyMuPDF to accurately parse text blocks and tables, ensuring clean input for the large language model. This initial definition and schema design phase typically takes 2-3 days and is crucial for predictable and reliable outputs. Your team would provide example documents and clarify extraction rules during this phase.
The core logic would be implemented as a FastAPI service. This service would expose an endpoint designed to accept documents for processing. For documents exceeding Claude's context window, the system would incorporate a recursive text splitter for automatic chunking. Syntora would engineer a tailored system prompt to instruct Claude to extract data according to the defined Pydantic schema, returning a JSON object. All API calls to Anthropic would be made asynchronously using httpx, allowing for efficient concurrent document processing.
To manage costs and improve performance, we would design and implement a caching layer, potentially using a Supabase Postgres database. Before submitting a document to the LLM, the service would check for a hash of the document; if a pre-computed result exists, it would be returned rapidly, saving on API expenses. Error handling would include exponential backoff for API retries to manage transient issues. For operational visibility, usage data, including token counts, costs per call, and latency, would be logged to a separate Supabase table using structlog, providing detailed analytics.
The FastAPI application would be containerized with Docker for consistent deployment. We typically deploy such services to AWS Lambda, often managed via a Vercel pipeline, to provide serverless auto-scaling that adapts to varying request volumes without manual configuration. The typical monthly hosting cost for this architecture is usually under $20. We would configure CloudWatch alarms to provide notifications, for example, via Slack, if predefined operational thresholds like API error rates or p99 latency are exceeded. The deliverable would be a fully deployed, tested, and documented API service ready for integration into your internal systems.
What Are the Key Benefits?
From Raw Docs to Structured Data in 2 Seconds
Our AWS Lambda-based system processes most documents in under 2 seconds, a 90% reduction from manual review or slow internal scripts.
Pay for Compute, Not Per-Seat SaaS
Your only ongoing costs are for the Claude API and AWS Lambda usage, typically under $50/month, instead of a $200/user/month SaaS tool.
You Own the Code, Your GitHub, Your Control
You receive the full Python source code in your private GitHub repository, along with a runbook for maintenance. No vendor lock-in.
Alerts Before Your Users Notice a Problem
We configure CloudWatch and Slack alerts for API errors and high latency. You know about issues in real-time, not from user complaints.
A Private API for Your Entire Stack
The FastAPI endpoint can be called from Retool, Salesforce Apex, a Slack bot, or any system that can make a standard HTTP request.
What Does the Process Look Like?
Document & Schema Review (Week 1)
You provide 5-10 sample documents and the desired output fields. We build the Pydantic schemas and confirm the extraction logic. You receive the schema definitions for approval.
API Wrapper Development (Week 2)
We build the core FastAPI service, including document chunking, prompt engineering, and caching. You receive a private staging URL to test.
Deployment & Integration (Week 3)
We deploy the service to your AWS account. We help your team integrate the new endpoint into their existing workflow or tool. You receive deployment credentials.
Monitoring & Handoff (Week 4)
We monitor the system for one week post-launch to ensure stability and accuracy. You receive the GitHub repo access and a final runbook.
Frequently Asked Questions
- How much does a custom wrapper cost?
- A typical build takes 3 weeks. Pricing is fixed-scope based on document complexity (PDF tables vs plain text) and the number of distinct document types. We provide a firm quote after the initial document review during our free discovery call. Book a call at cal.com/syntora/discover to discuss your specific needs.
- What happens if Claude's API is down or returns garbage?
- The wrapper has built-in retry logic with exponential backoff for transient API errors. If Claude returns malformed JSON that fails Pydantic validation, the wrapper retries the request up to 3 times with a modified prompt. If it still fails, the request is logged as an error with full context for manual review, and the calling system receives a clear error message.
- How is this different from just using the Anthropic Python SDK?
- The SDK is a library for making API calls. It is not a production system. Our wrapper builds on the SDK, adding essential services: a web endpoint, caching, cost tracking, structured logging, automatic retries, and context window management for large files. A raw SDK script cannot do this without significant custom engineering.
- How do you handle sensitive documents?
- The wrapper is deployed in your own AWS account, so your documents never pass through Syntora's servers. We access your environment via temporary IAM credentials that are revoked after the engagement. All data remains within your control boundary, ensuring confidentiality and compliance.
- Can this handle our volume?
- The AWS Lambda deployment scales automatically. We have deployed systems that handle bursts of 500 documents in 5 minutes for financial reporting analysis. The architecture is designed for spiky, unpredictable workloads without manual scaling or performance degradation. It can handle from zero to thousands of requests per hour.
- What if we need to change the analysis logic later?
- The system prompts are stored as plain text files alongside the code, not hardcoded. The runbook we provide includes instructions on how to safely edit these prompts and test the changes in a staging environment before deploying to production. You can easily adapt the system to new requirements without a full rebuild.
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