Custom AI Automation for Your 5-50 Person Business
Yes, you would buy if you need a production-grade AI system without hiring an engineering team. We build custom AI automation for small businesses that need reliable, hands-on engineering.
This is not a no-code tool or an offshore agency. It is a one-person consultancy where the founder on your discovery call is the engineer who writes every line of your code. The focus is on business-critical workflows that must run reliably.
We recently built a document processing pipeline for a 12-person insurance agency. They handled 400 claims a month, with each one taking 6 minutes of manual data entry. Our system, using the Claude API, now processes each claim in 8 seconds and was deployed in 3 weeks.
What Problem Does This Solve?
Many businesses start with visual, task-based automation platforms. They connect common apps quickly, but the cost model is punishing. A workflow that reads an email, saves an attachment, extracts data, and updates a CRM burns 4 tasks. At 200 emails a day, that is 800 tasks, driving the monthly bill into hundreds of dollars for a single process.
A regional logistics company with 25 employees faced this issue. Their dispatch workflow needed to check inventory in their ERP and customer status in their CRM before sending an SMS. In a visual builder, this required two separate conditional branches that could not merge. This forced them to duplicate the final SMS step, doubling task consumption and creating a maintenance headache whenever the message template changed.
The alternative, hiring a full-time AI engineer, costs over $150,000 per year and is overkill for building one or two core systems. Large consultancies will take the project, but they assign junior developers managed by a non-technical project manager. You pay for overhead, not for senior engineering talent focused exclusively on your build.
How Does It Work?
Our process begins by mapping your manual workflow into a state machine, with each step becoming a Python function. For a document pipeline, we first pull PDFs from an S3 bucket or email inbox. We use optical character recognition (OCR) if needed, then pass the raw text to the Claude API with a detailed prompt engineered for structured data extraction, typically JSON. This initial data capture step is instrumented with structlog for detailed JSON-formatted logs.
The core logic is a FastAPI application. We use httpx for asynchronous calls to external services like the Claude API, which keeps processing times low. For a typical invoice, the entire workflow from receiving the file to getting structured data back takes under 8 seconds. All data validation is handled by Pydantic models, ensuring that malformed data from the AI is caught and routed to a manual review queue in Supabase.
The system is deployed on AWS Lambda, which means you pay nothing when it is not running. A workflow processing 5,000 documents a month typically costs under $50 in monthly AWS fees. We use a Supabase Postgres database to log every transaction, store processed results, and manage the exception queue. This provides a full audit trail and makes debugging specific failures straightforward. The entire infrastructure is defined as code for repeatable deployments.
We integrate directly with your existing software. The final step is a secure API call that pushes the extracted data into your CRM, ERP, or industry-specific platform. After a 2-4 week build, we deliver the complete Python source code to your private GitHub repository. You are not locked into any platform and have full control over the system an in-house engineer would have built.
What Are the Key Benefits?
Live in 2-4 Weeks
From discovery call to a production system your team is using. We build and deploy the core workflow in under 20 business days.
Fixed Price, Zero Subscriptions
One scoped price for the entire build. After launch, you only pay for cloud usage, typically under $50/month, with no per-seat fees.
You Get The Source Code
We deliver the full Python codebase to your company's GitHub account. You have zero vendor lock-in and can modify the system yourself later.
Alerts for Failures, Not Noise
We configure monitoring that alerts on critical failures, like an external API being down for 5 minutes. No spam, just actionable alerts.
Connects to Your Real Tools
We build direct API integrations to your CRM, ERP, and other platforms. The automation happens inside the software your team already uses.
What Does the Process Look Like?
Week 1: Discovery and Architecture
You provide access to current systems and walk us through the manual process. We deliver a technical design document and a fixed-price proposal.
Weeks 2-3: Core System Build
We write the production code and deploy to a staging environment. You receive a link to test the system with real data and provide feedback.
Week 4: Deployment and Handoff
After your approval, we deploy the system to production. We transfer the GitHub repository and AWS account access to you.
After Launch: Monitoring and Support
We monitor the system for two weeks to ensure stability. You receive a runbook for common issues and the option for a flat monthly maintenance plan.
Frequently Asked Questions
- What determines the final cost and timeline?
- The primary factors are the number of systems to integrate and the predictability of the input data. A workflow connecting two modern APIs with clean PDF inputs is faster to build than one connecting three systems, one of which is a legacy database. The discovery call determines this scope, which is locked into a fixed-price proposal.
- What happens if the Claude API fails or a document is unreadable?
- The system is built with retry logic for transient API errors. If an API is down for an extended period or a document is fundamentally unreadable (e.g., a blurry photo), the item is automatically sent to a human review queue with an alert. This ensures no data is lost and the process does not halt completely.
- How is this different from hiring an engineer on Upwork?
- We build production systems, not just scripts. This includes structured logging, automated testing, infrastructure-as-code for deployment, and monitoring. A freelancer might deliver a Python script that works. We deliver a maintainable system with a runbook and a clear support path, which is what a business-critical process requires.
- Why do you use Python and AWS Lambda?
- We use Python because its data processing and AI libraries are mature and widely supported. We deploy on AWS Lambda because it is serverless. You pay only for the compute time used (per millisecond) and never have to worry about managing, patching, or scaling a server. This combination provides high performance at a very low operational cost.
- How much of my team's time is required during the build?
- We need one subject matter expert for a 1-hour discovery session and a 1-hour walkthrough. After that, we require a 30-minute check-in once a week to show progress and ask questions. The total time commitment from your side is typically less than 4 hours over the entire project.
- How is our sensitive data handled?
- We build and deploy the system within your own cloud infrastructure (e.g., your AWS account). Syntora does not store your data on our systems. Data is processed in-memory on AWS Lambda and passed directly to your destination systems. We write code that avoids logging personally identifiable information (PII) to comply with privacy standards.
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