Build Production-Grade Claude Systems, Not ChatGPT Prototypes
Claude's larger context window and structured tool-use are better for production business workflows. This enables complex document analysis and multi-step agentic tasks that fail in ChatGPT.
Syntora specializes in designing and building custom AI systems that leverage Claude API for complex document analysis and workflow automation. They focus on architecting production-ready solutions by defining precise data models and integrating external tools for reliable business processes.
Building a production system with Claude requires more than basic API calls. It involves structured output parsing, context management for Claude's 200K token window, and robust error handling. Syntora specializes in building these system wrappers, not just making model calls. The complexity of an engagement depends on the number of integrated tools and the required output structure, such as JSON schema validation.
We have experience building similar document processing pipelines using Claude API for sensitive financial documents, and the same architectural patterns apply to various industry-specific documents. A typical engagement for a system of this complexity often takes 6-10 weeks from discovery to initial deployment. Clients would provide access to relevant document types, data schemas, and internal APIs for tool integration. Deliverables typically include a deployed backend API, structured code, and comprehensive documentation.
What Problem Does This Solve?
Many dev teams start with OpenAI's function calling in a Python script. This works for simple demos but fails on complex, multi-step tasks. ChatGPT's function calling can hallucinate arguments or fail to follow sequential logic, requiring complex retry loops that become brittle. The limited context window also prevents reasoning over large documents or long conversation histories.
A 15-person logistics company tried to automate their shipment quoting process. The workflow needed to: 1) parse an email request, 2) look up rates in a private API, 3) check warehouse capacity in a Google Sheet, and 4) draft a quote. Their Python script using ChatGPT consistently failed at step 3, often hallucinating warehouse availability or misinterpreting the rate data from step 2, leading to a 30% error rate on quotes.
These proof-of-concept scripts lack production necessities. They have no logging for failed API calls, no caching to reduce costs on repeat queries, and no fallback logic if the primary model is slow or down. When a junior developer leaves, the unmonitored script that runs on a cron job becomes a black box of technical debt, silently costing money and producing errors.
How Would Syntora Approach This?
Syntora would begin an engagement by auditing your current workflow to identify specific document types, external APIs, and decision points. This discovery phase helps define a precise data model, such as a Pydantic model for structured inputs or an XML schema for external API interactions. We would then engineer system prompts that explicitly define the tools Claude can use, their parameters, and the desired sequence for task execution. This focuses on precise prompt engineering rather than open-ended queries.
The core logic of the system would be built using a FastAPI application. We would implement Anthropic's Tool Use API to create a structured request-and-response loop. Each external API call would be wrapped in httpx for async performance, including built-in exponential backoff for retries to ensure system reliability. To optimize for cost and speed, successful API results could be cached in a Supabase Postgres instance for a defined period.
For deployment, the FastAPI application would be containerized with Docker and deployed as an AWS Lambda function, fronted by an API Gateway. This architecture is designed for cost-efficiency at varying volumes and can scale to handle fluctuating demand. We would implement structured logging with structlog, sending JSON-formatted logs to AWS CloudWatch for real-time monitoring and to enable alerting on specific error codes.
Syntora would also build a lightweight usage dashboard, potentially using a framework like Vercel, to connect to the Supabase database. This dashboard would track API calls, latency, token counts, and estimated costs, providing transparency into system operations. The final deliverables include the deployed system, comprehensive codebase, and operational documentation, empowering your team with full ownership and understanding.
What Are the Key Benefits?
Your Code, Your GitHub Repo
You get the complete Python source code and deployment scripts. No vendor lock-in. Your engineering team can take over and extend the system anytime.
From Chaos to 25-Second Execution
We replace brittle scripts with a production-grade system. The logistics quoting workflow now runs in 25 seconds, down from 15 minutes of manual copy-paste.
Pay for Usage, Not Seats
A single project fee and direct pass-through API and hosting costs. An entire workflow might cost less than $50/month to run, not hundreds in SaaS fees.
Alerts Before Your Users Complain
We configure CloudWatch alarms that trigger Slack notifications for high API latency or a spike in 500 errors. We know it broke before you do.
Connects to APIs, Not Just Apps
We integrate directly with internal databases, private APIs, and Google Sheets. This is not limited to pre-built connectors in a marketplace.
What Does the Process Look Like?
Week 1: Scoping and Access
You provide API keys and documentation for your internal systems. We deliver a detailed technical specification and system architecture diagram.
Weeks 2-3: Core System Build
We build the core application and host it in a staging environment. You receive access to a private GitHub repository with daily code commits.
Week 4: Deployment and Testing
We deploy the system to your production AWS account. You receive a runbook with deployment instructions, monitoring dashboards, and API endpoint documentation.
Weeks 5-8: Monitoring and Handoff
We monitor the live system for performance and accuracy, making adjustments as needed. At the end of the period, we conduct a final handoff session.
Frequently Asked Questions
- What does a typical project cost and how long does it take?
- A single-workflow system with 2-3 integrations typically takes 4 weeks. Pricing is scoped per project, not hourly, based on the number of tools and the complexity of the required output structure. We provide a fixed-price proposal after our initial discovery call, so you know the exact cost before work begins. Book a discovery call at cal.com/syntora/discover to discuss scope.
- What happens if the Claude API is down or slow?
- Our production wrappers include fallback logic. If a request to Claude 3 Opus times out after 10 seconds, the system automatically retries with Claude 3 Sonnet, a faster and cheaper model. If both fail, the request is logged as an error and a Slack alert is sent. This ensures the entire workflow does not crash due to a temporary model outage.
- How is this different from hiring a freelance developer on Upwork?
- Syntora is a consultancy, not a marketplace. You are not hiring a temporary coder; you are engaging an engineer who has built and deployed these specific Claude-based systems before. The person on the discovery call is the person who writes every line of production code. We provide a full system with deployment, monitoring, and documentation, not just a Python script.
- Can this work with our proprietary internal software?
- Yes, as long as it has a REST API we can access. We have connected Claude to custom-built CRMs, internal inventory databases, and legacy SQL systems. During the discovery phase, we review your API documentation to confirm feasibility before the project starts. This is a core reason businesses choose custom development over off-the-shelf tools.
- Why not just use the ChatGPT Enterprise plan?
- ChatGPT Enterprise provides an API and a UI, but it does not build the surrounding application for you. You still need to write the code for caching, logging, cost tracking, and integration with your other systems. Syntora builds that entire production wrapper around the Claude API, which is a service OpenAI does not offer.
- Do we need an AWS account for this?
- Yes, you will need your own AWS account. We deploy all infrastructure into your account, so you have full ownership and control over the code and data. We provide a CloudFormation or Terraform script that automates the entire setup. You are only responsible for the direct AWS hosting costs, which are typically very low for this architecture.
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