Build Production Apps with Claude's Million-Token Window
You use Claude's 1 million token context window via Anthropic's API with the Claude 3 Opus model. Access is a paid service, billed per token.
Syntora designs and builds custom systems that leverage Claude's 1 million token context window for document processing. We provide engineering expertise to create robust data pipelines, API integrations, and validation logic, ensuring efficient and reliable operation for industries handling large volumes of specialized documents.
Building a production system with Claude's 1 million token context window requires addressing several engineering challenges. These include managing long processing times, implementing response streaming for user experience, optimizing API costs, and ensuring reliable parsing of structured output. A functional system needs to wrap the core API calls in a service layer that handles these operational details.
Syntora provides engineering expertise to design and build custom systems that leverage large context windows. We focus on creating efficient, reliable data pipelines and API integrations. We have experience building document processing pipelines using Claude API for financial documents, and the underlying architectural patterns apply to other document-intensive industries. Typical engagements for a system of this complexity involve a discovery phase, architectural design, and a build period often ranging from 6 to 12 weeks, depending on document volume and integration requirements. Clients would provide access to their document repositories, domain expertise, and specific output requirements.
What Problem Does This Solve?
Many people first try the large context window on the Claude.ai web chat. While it can accept large documents, it is not an API. You cannot programmatically submit files, extract structured data, or integrate it into a business process like screening applicants from your ATS. It is a manual tool with usage limits that are much stricter than the API.
A developer's next step is often a Python script using a library like LangChain. These wrappers are excellent for prototypes but introduce production risks. When a 750,000 token API call fails, the library's abstraction can hide the specific Anthropic API error code, making debugging difficult. A simple `httpx` request with explicit timeouts and structured logging is easier to troubleshoot than a complex, multi-layered library call.
A 12-person recruiting firm tried to build a candidate screener this way. They combined a 25-page role description with a candidate's resume and public data into a 150k token prompt. The script would hang and then crash. The library's default 60-second timeout was shorter than the 95 seconds the API needed to respond, but the error message was a generic 'request failed' with no details.
How Would Syntora Approach This?
Syntora would approach the problem by first designing a dedicated data pipeline for document ingestion. This would typically involve a FastAPI endpoint configured to receive documents, such as PDF files. We would use a library like PyMuPDF to extract clean text, storing it in a Supabase Postgres table. To manage costs and avoid redundant API calls, a pgvector index would be implemented to create a semantic cache, checking if a nearly identical document has been processed recently.
For the core processing logic, Syntora would implement direct API calls to Anthropic using Python's httpx library for asynchronous requests. This approach avoids high-level wrappers, providing granular control over request timeouts and enabling custom exponential backoff retry strategies with the tenacity library. The system would be designed to stream responses, aiming for initial content display within seconds, even for prompts requiring significant generation time.
We would engineer the system prompt to guide the model towards returning a specific JSON schema. The application would then use Pydantic to parse and validate the incoming response as it streams. To ensure high reliability, if output from Claude 3 Opus fails validation after a set number of retries, the system could automatically fall back to Claude 3 Sonnet with a simpler prompt. This robust design aims for a very high success rate for the entire workflow, with logging in place for review of any initial failures.
The delivered service would typically be packaged in a Docker container and deployed on a serverless platform like AWS Lambda, triggered via an API Gateway. This architecture is designed to support the processing of thousands of documents daily, with hosting costs often remaining low, depending on exact usage. We would implement structured logging using structlog, sending JSON-formatted logs to AWS CloudWatch for effective monitoring and alerting on operational metrics like latency or error rates.
What Are the Key Benefits?
A Production Endpoint in 2 Weeks
From prompt design to a deployed API endpoint in 10 business days. Start processing documents immediately, not after a quarter-long project.
Pay API Costs, Not Platform Fees
The system runs on your own AWS account. Your monthly bill is for compute and API usage, with no recurring per-seat SaaS subscription fee.
You Own the Python Source Code
You receive the complete source code, Dockerfile, and deployment scripts in your own private GitHub repository at the end of the engagement.
Get Failure Alerts Before Your Users Do
We configure CloudWatch to send a Slack alert if API latency exceeds 5 seconds or the error rate climbs. You know about problems instantly.
Connects Directly to Your Workflow
The service integrates with your existing tools. We build direct API connections to systems like Greenhouse, Salesforce, or your internal database.
What Does the Process Look Like?
Week 1: Workflow and Prompt Design
You provide sample documents and define the desired output. We co-author the system prompt and finalize the structured JSON output schema.
Week 2: Core Application Build
We build the FastAPI service, data processing logic, and error handling. You receive a staging URL to test the endpoint with your own data.
Week 3: Deployment and Integration
We deploy the system to your AWS account and connect it to your source systems via webhook or API. You receive the full source code.
Weeks 4-6: Monitoring and Handoff
We monitor performance, cost, and accuracy for two weeks post-launch. You receive a runbook detailing system operation and maintenance.
Frequently Asked Questions
- What does a custom system for this cost to build?
- The cost depends on the complexity of the documents and the number of integrations. A system for analyzing a single document type can be built in 2-3 weeks. A more complex workflow connecting to multiple external systems might take 4-6 weeks. Pricing is a one-time project fee, which we scope on the discovery call based on your exact requirements.
- What happens if the Anthropic API is down or slow?
- The system is built for resilience. For slow responses, the streaming and long timeouts ensure the request completes. If the API is fully down, our wrapper catches the error. The system can either queue the job for a later retry using Amazon SQS or immediately fall back to a smaller model like Claude 3 Haiku for a degraded but functional response.
- How is this different from using Amazon Bedrock?
- Amazon Bedrock provides managed API access to Claude models, which is a great infrastructure choice. Syntora builds the entire application that uses that infrastructure. We write the code for data ingestion, prompt management, structured output parsing, caching, and integration. We often deploy the systems we build on top of Bedrock using AWS Lambda.
- How is my confidential data handled?
- Your data is processed within your own cloud environment. We deploy the entire system into your AWS account, so your documents never pass through Syntora's servers. When using the Claude API, Anthropic does not use your data to train their models. We only require temporary, credentialed access to your environment during the build phase.
- Can I update the system prompt myself after you build it?
- Yes. The system prompt is not hard-coded. We store it as a text file in the source code or in a database record. You can edit this file and redeploy or update the database to change the model's instructions without needing a developer. The runbook we provide includes instructions for how to safely test and roll out prompt changes.
- How fast is a 1 million token request, really?
- It is not instantaneous. A request with a 900k token prompt and a 4k token generation can take 60-120 seconds to complete with Claude 3 Opus. This is why we build systems with streaming enabled, so your application receives the first tokens within a few seconds. We design the workflow around this latency, using it for asynchronous tasks rather than real-time user-facing interactions.
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