Build a Business That Runs Itself, Not One You Restock Daily
Vending machines are worth it when they provide reliable income with minimal management. Custom AI systems offer the same benefit, turning manual tasks into self-running business processes.
We built a document processing pipeline for a 6-person logistics company. They were manually keying 150 bills of lading per day, taking 5 minutes each. The new system uses the Claude API and OCR to process them in 8 seconds with 99% accuracy. The project was completed in 3 weeks.
The complexity depends on the process. Automating a simple lead qualification form is a 2-week build. Processing thousands of variable-format invoices with OCR and human review loops takes 4 weeks.
What Problem Does This Solve?
Many businesses try point-and-click automation platforms to connect their inbox to a spreadsheet. The goal is simple: parse details from emails and attachments to track new work, like an insurance agency processing claims. This approach fails quickly.
These platforms use generic parsers that cannot handle varied document formats, leading to a 30% error rate where fields are missed. Their pricing models charge per task, so a single email can trigger 5 separate tasks, leading to a bill for thousands of tasks per month. A workflow that was supposed to save money ends up costing more than the manual labor it replaced.
The logic is also brittle. If an email has two attachments when the workflow expects one, the system either fails or silently ignores the second document. There are no built-in retry queues or robust error handling. Teams spend more time finding and fixing the automation's mistakes than they spent on the original manual process.
How Does It Work?
We start by building a secure ingestion point using an AWS Lambda function triggered by new emails. Instead of a generic parser, we use the Claude API's multimodal capabilities to analyze both the email body and attachments like PDFs or JPEGs. Claude extracts key entities from unstructured text and images. This entire ingestion component is written in Python.
The extracted data is passed to a FastAPI service for validation. This service checks the data against business rules, like confirming a policy number exists by querying the main CRM via an API call using httpx. If data is ambiguous, it is flagged for human review in a simple interface, not silently dropped. All events are recorded with structlog for clear, debuggable logs.
Validated data is stored in a Supabase Postgres database, which provides a clean API for any internal dashboards. The whole pipeline, from email receipt to database entry, takes an average of 11 seconds. We deploy the FastAPI service on Vercel and the ingestion function on AWS Lambda, costing under $30 per month for a volume of 200 claims per week.
The client receives the full Python source code in their private GitHub repository. We provide a complete runbook detailing system monitoring, log interpretation, and how to handle new document formats. This prepares your team for long-term ownership of the system.
What Are the Key Benefits?
Your First Automated Task Runs in 3 Weeks
We move from a discovery call to a live production system in 15 business days. Your team sees the impact immediately, not next quarter.
Escape Per-Task and Per-Seat Pricing
A one-time build fee and an optional flat monthly maintenance plan. Your costs are predictable and do not scale with your team's size or activity.
You Get the Keys and the Blueprints
We deliver the complete Python source code to your GitHub repo. There is no vendor lock-in. The system is your asset to modify or extend.
It Fails Loudly, Not Silently
We configure alerts that notify us instantly if an API fails or a new document format breaks the parser. Errors are queued for review, not lost.
Connects Directly to Your Core Systems
The system integrates with your existing tools, whether it is a modern CRM or an industry-specific platform, through direct API calls.
What Does the Process Look Like?
Workflow Mapping (Week 1)
We map your manual process step-by-step. You provide sample documents and read-only access to necessary APIs. You receive a detailed project scope document.
Core System Build (Week 2)
We write the production code for the processing pipeline. You receive access to a staging environment to test the system with your own sample documents.
Deployment and Integration (Week 3)
We deploy the system to production on your cloud infrastructure. You receive the full source code in your GitHub repository and a live system processing real data.
Monitoring and Handoff (Weeks 4-8)
We monitor the live system, tune performance, and handle edge cases. You receive a final runbook and we transition to an optional monthly support plan.
Frequently Asked Questions
- How much does a custom AI automation build cost?
- The price is fixed based on the project scope. Automating a single, well-defined document workflow typically falls into a set price range we can discuss on a call. Factors that increase cost include the number of unique document types, the number of systems to integrate with, and the need for a human-in-the-loop review interface.
- What happens if the Claude API is down or gives a bad result?
- The system is built with retries and dead-letter queues. If an API call to Claude fails, it automatically retries up to 3 times. If it still fails, the original document is sent to a specific queue for manual review, and an alert is triggered. No data is ever lost due to a third-party outage.
- How is this different from hiring a freelancer on Upwork?
- A freelancer might deliver a script, but we deliver a production system. This includes structured logging, automated testing, deployment infrastructure as code, and a runbook for long-term maintenance. We build for handoff, assuming another engineer will manage it someday. The person on the discovery call is the engineer who writes the code.
- Do we need to have our own AWS or cloud account?
- Yes. We deploy all code and infrastructure into your own cloud account (AWS, Vercel, etc.). You own the infrastructure and have full control. This ensures you are only billed for usage and have complete ownership of the running system. We provide instructions for setting up the necessary permissions for deployment.
- Can you work with on-premise software that doesn't have an API?
- It depends. If the software has a database we can connect to, yes. If it is a closed desktop application, it is not a good fit for our approach. We focus on API-driven automation and do not build integrations that rely on screen scraping or controlling a user interface, as they are too brittle for production use.
- What kind of ongoing maintenance is required?
- For most systems, maintenance is minimal. The optional flat-rate plan covers dependency updates, security patches, and fixing anything that breaks due to an external API change. It also includes 2 hours per month for minor enhancements. If you have an engineering team, they can handle this in-house using the provided runbook.
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