A Done-For-You AI System Built in Under a Month
A custom AI automation engagement for a 20-person company is a 2-4 week build. You get a production-grade system with flat-rate monthly maintenance and full source code ownership.
The timeline depends on integration points and data complexity. Connecting to a standard CRM like HubSpot is faster than integrating with a legacy on-premise database. Clean, structured inputs require less data preparation than parsing unstructured PDFs.
We built a document intake system for an 8-person law firm in 18 days. The system uses the Claude API to parse PDFs, classifies them into 14 matter types, and creates records in their Clio practice management software automatically.
What Problem Does This Solve?
Many teams start with email parsing tools like Parsio or Mailparser. They work for structured emails from a single sender, but fail with varied formats. A customer might send a PDF attachment one day and a plain text order in the email body the next. These tools cannot handle that variance and require constant rule updates.
The operations manager then tries a point-and-click automation platform. They build a workflow that triggers on new emails, but the logic gets complex fast. A simple check to see if the customer is already in their TMS requires one path. A second check for priority status requires another branch. Soon they have a 50-step workflow with duplicated logic that is impossible to debug and costs over $400/month in task usage.
The core problem is that these platforms are designed for simple A-to-B connections, not business-critical logic. They lack proper error handling, version control, and the ability to process complex, unstructured data. When a workflow fails overnight, no one gets an alert, and 150 shipping requests are simply lost. This is not a tooling issue; it's an engineering problem.
How Does It Work?
We start by collecting 100-200 examples of real inbound emails and attachments. We map out every field you need to extract: Purchase Order number, shipping addresses, delivery dates, and item SKUs. We use the Claude API to build prompts that can reliably extract this data from varied formats, including messy PDFs and poorly formatted email bodies, with over 98% accuracy.
The extraction logic is built into a Python service using FastAPI. For each incoming email, the service calls Claude, validates the extracted data (e.g., checks if a SKU exists in your product database stored in Supabase), and formats it into a clean JSON object. This entire process executes in under 2 seconds. The service is containerized with Docker for consistent behavior from local testing to production.
We deploy the service to AWS Lambda, which costs pennies per execution. An endpoint is exposed via Amazon API Gateway. We configure your email server to forward relevant messages to this endpoint. The service then makes a secure API call to your Transportation Management System, creating a new shipment record in under 500ms. This replaces the manual data entry that previously took 5-10 minutes per request.
You get a Vercel-hosted dashboard showing processing volume, average latency, and an error log. If the Claude API fails to parse a document three times in a row, the system sends an alert to a shared Slack channel with the problematic email attached for manual review. You receive the full source code in your private GitHub repo and a runbook explaining the architecture.
What Are the Key Benefits?
Production-Ready in 18 Days, Not 18 Weeks
From our first call to a live system processing real data in under four weeks. We bypass the typical months-long cycles of agencies and large consultancies.
Your System, Your Code, Zero Lock-In
You receive the complete Python source code in your GitHub repository. If you hire an engineer later, they can take over and extend the system.
Fixed Build, Flat Maintenance
One clear project fee for the build. Afterwards, a simple flat monthly rate covers hosting, monitoring, and on-call support. No per-seat or per-task pricing.
Alerts Before Your Team Finds a Problem
We build monitoring into the system from day one. You get Slack or email alerts for API failures or data validation errors, often before users notice an issue.
Connects Directly to Your Core Tools
We build native integrations with the tools you already use, like HubSpot, Clio, or Salesforce. No new dashboards or logins for your team to manage.
What Does the Process Look Like?
Week 1: Discovery and Scoping
You provide access to your current tools and sample data. We deliver a detailed technical spec and a fixed-fee proposal outlining the exact system we will build.
Weeks 2-3: Core System Build
We write the production code in a shared GitHub repo you can access. You get weekly video updates showing progress and a staging environment to test key features.
Week 4: Deployment and Go-Live
We deploy the system to your cloud environment, run final integration tests, and process the first live transactions. We deliver a runbook and monitoring dashboard.
Post-Launch: Monitoring and Support
For the first 30 days, we provide intensive support to handle any edge cases. After that, the system moves to our flat-rate maintenance plan for ongoing peace of mind.
Frequently Asked Questions
- What does a typical engagement cost and how long does it take?
- Most builds are completed in 2-4 weeks. The cost depends on the number of systems to integrate and the complexity of the AI logic. A simple lead router connecting two APIs is different from a multi-stage document parser. After a 30-minute discovery call, we provide a fixed-price quote.
- What happens when an external API like Claude or HubSpot has an outage?
- The system is built with resiliency. API calls have automatic retries with exponential backoff. If an external service is down for an extended period, the system queues incoming requests in a Supabase table and processes them once the service is back online. Critical failures trigger an immediate alert.
- How is this different from hiring a freelancer on Upwork?
- Syntora offers accountability and production-readiness. Freelancers often disappear or deliver code that is not documented, tested, or monitored. We provide a complete system: source code, a runbook, monitoring dashboards, and an ongoing maintenance plan. The person who scopes the project is the one who writes the code.
- Can this system scale if our company grows from 20 to 50 people?
- Yes. We build on serverless architecture like AWS Lambda, which scales automatically. The system we build for 100 transactions a day can handle 10,000 a day with no code changes. The hosting costs scale linearly, so you only pay for what you use, typically remaining under $50/month even with significant growth.
- What kind of access do you need to our systems?
- We work with the principle of least privilege. For CRMs like HubSpot or Salesforce, we typically need a dedicated API key with restricted permissions to only the objects we need to read or write. We never require administrative access and will provide you with the exact permission scopes needed.
- We are not a tech company. How do we know what is possible?
- You don't need to. The discovery call is for us to understand your business process, not for you to propose a technical solution. You show us the manual, repetitive task that is causing pain. We will tell you exactly how an AI automation system can solve it and what the build would look like.
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