Find an AI Automation Partner Who Writes Production Code
Choose a partner who delivers full source code and has direct engineering experience. Verify they have built production systems for businesses your size without project managers.
Syntora offers AI engineering expertise for manufacturing companies seeking to automate data processing and integrate AI solutions into existing workflows. Syntora approaches challenges like order entry or quote generation by building custom document processing pipelines and agent platforms on Claude API, designed for specific operational needs.
For a small manufacturing company, this means finding a hands-on engineer, not a large consulting firm. The right partner focuses on solving a specific, high-value problem like order entry or quote generation with a production-grade system. They integrate with your existing ERP and shop floor software, not force a new platform on you.
Syntora has direct experience building production document processing pipelines and AI agent platforms using the Claude API for structured output parsing and multi-step workflows. We apply these capabilities to automate data extraction from varied documents or to create intelligent systems that streamline complex operational tasks within an existing software environment.
What Problem Does This Solve?
Many small manufacturers first look at large consulting firms for an AI transformation. These firms assign junior analysts, spend months on discovery, and deliver a PowerPoint strategy deck. The final bill is substantial, but you are left with no working software to solve your immediate problem.
Next, teams try off-the-shelf SaaS tools for document processing or automation. The problem is that these tools are generic. They cannot handle the non-standard PDF layouts from your top 5 suppliers or integrate with your industry-specific ERP. You end up paying a per-document fee for a tool that only works on 60% of your volume, forcing your team to manually handle the rest.
A custom metal fabricator tried an OCR tool that failed on 40% of their incoming quotes because it could not parse tables with merged cells. They then hired a freelancer who built a Python script. The script was undocumented, had no error handling, and broke permanently when a key supplier changed their PDF format. They were back to full-time manual entry in two months, having wasted time and money.
How Would Syntora Approach This?
Syntora's approach typically starts by analyzing a sample of your historical documents, such as purchase orders or material specs. From this set, we define the key data fields for extraction. This analysis guides the creation of custom prompts for the Claude API, which handles varied document layouts effectively without requiring a separate model for each supplier.
The delivered system would typically be a Python application built with the FastAPI framework. When a new document arrives, an endpoint would accept the file, use an OCR service to extract raw text, and then send it to the Claude API. Syntora often uses httpx for asynchronous API calls to optimize the processing flow. All results would be written to a Supabase Postgres database for a permanent, queryable record.
The application would be deployed on AWS Lambda. This serverless architecture helps manage infrastructure costs by charging only for execution time, and it removes ongoing server management overhead. A Supabase database would provide a secure, reliable data store with a built-in API suitable for future integrations.
Finally, we connect the system to your existing workflow. For example, a new purchase order arriving in an email inbox could automatically trigger the Lambda function. Once processed, the structured data would be pushed directly into your Fishbowl or NetSuite ERP via their native API. Syntora delivers the full source code in your company's GitHub, including a runbook for monitoring and maintenance.
What Are the Key Benefits?
A Production System in 2-4 Weeks
We scope, build, and deploy your custom AI tool in under a month. No 6-month discovery phases or endless meetings with project managers.
No Per-Seat Fees or Surprise Bills
You pay a fixed price for the build. After launch, your only cost is direct cloud usage, typically under $100/month, not a recurring SaaS subscription.
You Own the Code and the Infrastructure
We deliver the complete Python source code to your GitHub. It runs on your AWS account. There is no vendor lock-in, ever.
Alerts Go to the Engineer Who Built It
Our optional flat monthly maintenance plan includes proactive monitoring. If an API fails, the alert goes directly to the developer who wrote the code, not a Tier 1 support desk.
Connects to Your Manufacturing ERP
We build direct API integrations to systems like Fishbowl, NetSuite, and ECI M1. Data flows into the tools your team already uses.
What Does the Process Look Like?
Week 1: Discovery and Scoping
You provide access to sample documents and system credentials. We deliver a fixed-price proposal with a detailed technical specification and build timeline.
Weeks 2-3: System Development
We build the core application and share progress via a private staging link. You receive weekly updates on development and test results.
Week 4: Deployment and Integration
We deploy the system to your cloud infrastructure and connect it to your ERP. Your team processes the first live documents with our direct support.
Post-Launch: Monitoring and Handoff
We monitor the system for 30 days to ensure stability. You receive the full source code, documentation, and a runbook for ongoing maintenance.
Frequently Asked Questions
- How is the price and timeline determined for a project?
- The primary factors are the number of unique document types to process and the complexity of API integrations. A single document pipeline connecting to one ERP is a 2-week build. Supporting five different supplier invoice formats and integrating with both a CRM and an ERP might take four weeks. We provide a fixed-price quote after the initial discovery call at cal.com/syntora/discover.
- What happens when an external service like the Claude API is down?
- The system is built with retry logic and a dead-letter queue. If an API call fails after three retries, the original document and error message are saved. This prevents data loss and sends an immediate alert to our team. Your staff can re-process the failed document later without interrupting the entire workflow, ensuring business continuity.
- How is this different from hiring a freelance developer on Upwork?
- Freelancers build scripts. We build and maintain production systems. This includes structured logging, automated testing, infrastructure-as-code for deployment, and monitoring with alerts. You get a complete, documented system built to professional software engineering standards, not just a standalone Python file. We also provide an optional, ongoing maintenance plan after the initial build is complete.
- Do we need an engineering team to maintain this?
- No. The systems are designed for minimal maintenance, running on serverless infrastructure like AWS Lambda that requires no server management. The optional maintenance plan covers all updates and troubleshooting. The provided runbook and source code allow any future Python developer to take over if you choose to bring the work in-house down the road.
- Our internal processes are unique. Can a custom tool really fit?
- That is precisely why a custom build is necessary. Off-the-shelf tools force you into their workflow. We map our build directly to your existing process, however specific it is. For one manufacturing client, we built custom logic to handle partial shipments and backorders directly from purchase orders, a feature their SaaS ERP couldn't support. The goal is to augment your process, not replace it.
- What kind of performance can we expect?
- For document processing, typical end-to-end time from receiving a PDF to having structured data in your database is 8-12 seconds. For internal AI agents or API wrappers, response times are typically under 500 milliseconds. We establish specific performance benchmarks in the project scope and build automated tests to ensure the live system meets them before launch.
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