Stop Gambling on AI Outputs. Build Production-Grade Systems.
Improve AI development by wrapping models like Claude in a production-ready service. The best tools are custom-built systems with structured output parsing and cost controls.
Syntora helps clients achieve predictable AI outputs for document processing by engineering reliable API wrappers and orchestration layers. This approach ensures reliability and audibility, moving beyond unpredictable raw API calls for critical business operations.
The "blind box" feeling comes from using raw API calls without the necessary engineering. Production systems require validation, automatic retries, fallback models, and detailed logging to be reliable. The goal is to make AI behavior predictable and auditable, not to hope for the best with each call.
Syntora helps clients achieve predictable AI outputs by engineering reliable API wrappers and orchestration layers. We have experience building document processing pipelines using Claude API for financial documents, and the same patterns apply to other industry documents, such as shipping manifests or legal contracts. The scope of such an engagement typically involves a discovery phase, architecture design, development, and deployment, with timelines depending on the specific document types and complexity of validation rules.
What Problem Does This Solve?
Most development with AI models starts with direct API calls using a library like httpx. This works for prototypes, but in production, the blind box problem appears. You get inconsistent JSON, occasional hallucinations, and unpredictable latency. The model might apologize instead of returning data, or the API call might time out under load, breaking your workflow.
Then teams try a simple wrapper tool. These tools help manage prompts but they hide the operational complexity. A user sees a simple interface but has no control over the retry logic, no fallback model if the primary one is slow, and no visibility into per-transaction costs. The system works until it silently fails, and you only discover it when a customer complains about missing data.
A regional insurance agency with 8 adjusters faced this exact issue. They used a script to send scanned claim forms to Claude for data extraction. It worked for about 80% of the forms. For the other 20%, it returned malformed data or a vague error, forcing an adjuster to manually fix 1 out of every 5 claims. This manual review process completely negated the time savings and cost the company over 40 hours of skilled labor per month.
How Would Syntora Approach This?
Syntora's approach to improving AI development for document processing begins with a discovery phase to understand specific operational needs and document characteristics.
The first step would be to define the exact output schema for your task using Pydantic. This establishes a strict contract for the AI's output. Syntora would analyze a sample of your documents to engineer a prompt and tool-use strategy designed to reliably produce this schema. This process ensures that if the AI model's output does not match the structure perfectly, it is immediately flagged.
The core of the system would be a Python service built with FastAPI, designed for deployment on platforms like AWS Lambda to ensure high availability. When a request comes in, the service would call a primary model, such as Claude 3 Sonnet. The Pydantic model would validate the response. Should the call fail or the validation not pass after a configured number of retries, the system could fall back to an alternative model, like Claude 3 Haiku, potentially with a simplified prompt, to maintain uptime.
To ensure operational visibility and cost control, every API call, its token count, latency, and cost would be logged, potentially to a Supabase table using structlog. This provides an auditable trail. A caching layer, also managed through Supabase, could be integrated to return previous results for identical inputs within a set timeframe, optimizing API costs. Typical processing times for a multi-page document through such a system are usually within a few seconds, depending on the model and document complexity.
A simple dashboard could be developed, perhaps using Vercel, to visualize key operational metrics from the Supabase logs. This dashboard would typically display daily costs, average processing time, and validation failure rates, turning the AI's behavior into a measurable component of operations. An engagement for building this level of system typically spans 3 to 6 weeks, depending on the complexity of the documents and specific client requirements, with client input needed for document samples and schema definition.
What Are the Key Benefits?
From Prototype to Production in 3 Weeks
We build and deploy the complete, production-ready system in 15 business days. Your team starts using a reliable tool immediately, not after a quarter-long project.
See Per-Transaction Costs, Not Just a Monthly Bill
Our logging provides a detailed breakdown of your AI usage. You know the exact cost of processing a single document, which allows for accurate ROI calculation.
You Get the GitHub Repo and Supabase Schema
We deliver the full source code and database architecture. You have zero vendor lock-in and can have your own developers extend the system in the future.
Proactive Alerts for Cost or Latency Spikes
We set up automated monitoring that sends a Slack alert if daily costs exceed a set threshold or if average API response time degrades by more than 20%.
A Simple API, Not Another SaaS Platform
The system integrates with your existing software via a standard REST API. There are no new dashboards or platforms for your team to learn.
What Does the Process Look Like?
Scope and Schema Definition (Week 1)
You provide sample inputs and desired outputs. We analyze them and deliver a detailed Pydantic schema and a fixed-scope proposal for the build.
Core Logic and Wrapper Build (Week 2)
We build the FastAPI service with the core prompting, validation, and fallback logic. You receive a private API endpoint to test against your own data.
Deployment and Integration (Week 3)
We deploy the service to AWS Lambda and provide the production API endpoint. We assist your team with integrating the API into your existing workflow.
Monitoring and Handoff (Week 4)
We monitor system performance and costs for one week after launch. You receive the complete GitHub repository, Supabase dashboard access, and a runbook.
Frequently Asked Questions
- What factors determine the cost and timeline?
- The primary factors are input complexity and output schema size. Extracting three fields from a plain text email is a 2-week build. A system that needs to process scanned PDFs, handle 50+ output fields with nested objects, and apply custom business rules is more likely a 4-6 week project. The scope is fixed before any work begins.
- What happens if the Claude API itself is down?
- The system is designed for resilience. If we detect a major outage from Anthropic's API, a circuit breaker trips. It can queue incoming requests in a Supabase table for up to 60 minutes, then process them automatically when service is restored. For critical, real-time workflows, we can configure a final fallback to a different model provider's API.
- How is this different from using a framework like LangChain?
- LangChain is a developer toolkit, not a production system. It gives you building blocks but you still have to architect, build, and manage the caching, logging, monitoring, and deployment infrastructure yourself. We deliver a fully managed, production-grade API endpoint that includes all of those components from day one, saving your engineers weeks of work.
- Can I give my developers this page and have them build this?
- Yes, absolutely. The architecture is standard practice for production AI systems. The value we provide is execution speed and experience. We have already solved the common edge cases, optimized AWS Lambda cold-start times, and built the monitoring dashboards multiple times. An experienced engineer can build this; we can build it for you in 3 weeks.
- What if the model's accuracy degrades over time?
- The logging system tracks all inputs and outputs. We can build a simple feedback mechanism, like a thumbs-up or thumbs-down button in your internal tool, that writes to the Supabase log table. This allows us to easily identify low-performing examples, refine the system prompts, and deploy an update within a day without rebuilding the entire application.
- How do you handle context window management for large documents?
- We never pass a full large document directly to the model. We implement a Retrieval-Augmented Generation (RAG) pattern using the pgvector extension in Supabase. The document is split into smaller chunks and embedded as vectors. The system retrieves only the 5-10 most relevant chunks to fulfill the request, keeping token counts low and improving response accuracy.
Ready to Automate Your Technology Operations?
Book a call to discuss how we can implement ai automation for your technology business.
Book a Call