Stop Chasing SaaS Dreams. Build Real AI Automation.
The most overrated way to make money online is building a generic AI wrapper SaaS on a no-code platform. These projects fail because they solve no specific business problem and have no defensible technology.
Real businesses create value by automating internal, mission-critical work, not by reselling a public API. This involves building systems that directly attack operational bottlenecks like manual data entry, lead qualification, or customer support triage. The goal is saving hundreds of hours per month, not finding a few hundred subscribers.
We built a document processing pipeline for a 15-person logistics firm. They processed 2,000 invoices per month, with each one taking 5 minutes of manual entry. The new system uses the Claude API and OCR to extract and validate data in 8 seconds, saving the company over 160 hours of work every month.
What Problem Does This Solve?
The trap starts with a no-code tool like Bubble. It seems perfect for building a simple user interface for an AI-powered idea, like a social media post generator. The problem is performance and reliability. Every call to an AI model is slow, and routing it through a no-code platform's infrastructure adds hundreds of milliseconds of latency, creating a sluggish user experience.
A common next step is to use a tool like Make to connect the Bubble front-end to an AI API. This introduces another point of failure and a disastrous cost model for a user-facing product. A workflow that runs 3 tasks per API call and is used 500 times a day will burn through 1,500 tasks. This can lead to a surprise bill for hundreds of dollars on a pre-revenue product.
This entire approach is fundamentally flawed because it creates a product with no unique value. If your entire "tech stack" is a public API connected via a visual workflow builder, anyone can replicate it in an afternoon. You are not building a business; you are building a demo that is expensive to run at any meaningful scale.
How Does It Work?
We begin by analyzing the actual business process you want to automate. For a document processing task, we take 100 sample invoices and map every field your team extracts manually. We write a Python script using the `pytesseract` library for initial OCR, then feed the raw text to the Claude API with a structured XML prompt to get back clean JSON data.
The core of the system is a custom FastAPI application. A secure endpoint receives the raw document, and an async function manages the OCR and the API call to Claude using `httpx` for non-blocking requests. We use Pydantic to strictly validate the data from Claude's response. This guarantees the final output always matches the required schema for your downstream systems and handles any unexpected API outputs gracefully.
We containerize the FastAPI app and deploy it on AWS Lambda, where it is triggered by file uploads to an S3 bucket. This serverless architecture means you pay only for the compute time used, which is typically under $30 a month for 2,000 documents. Processed data is written to a Supabase Postgres database. The end-to-end processing time for a single document is under 8 seconds.
For observability, we use `structlog` to generate structured JSON logs that are sent to AWS CloudWatch. We configure CloudWatch Alarms to send a Slack alert if the 99th percentile processing latency exceeds 15 seconds or if the API error rate is over 2% in any 5-minute window. This provides real-time visibility into system health.
What Are the Key Benefits?
From Kickoff to Production in 3 Weeks
An efficient build cycle gets the system live in 15 business days. Your team sees the impact immediately, not after a long implementation project.
Pay for Compute, Not for Headcount
Your AWS Lambda bill for processing 2,000 invoices is under $30/month. No per-seat licenses or recurring SaaS fees that grow as your team does.
Your Code, Your GitHub, Your Control
We deliver the complete Python source code and deployment configuration to your private GitHub repo. You are never locked into our service.
Alerts That Matter, Logs That Tell a Story
We configure alerts for specific failure modes like API timeouts. Structured logs make debugging a 10-minute job, not a multi-hour hunt.
Connects Directly to Your Systems
The output isn't a spreadsheet. It's a direct API call that pushes structured data into your ERP, CRM, or accounting software like NetSuite or QuickBooks.
What Does the Process Look Like?
Kickoff & Scoping (Week 1)
You provide access to source systems and a sample of 50 documents. We deliver a detailed technical specification outlining the exact logic, tools, and schema.
Build & Internal Demo (Week 2)
We build the core system and provide a video walkthrough of it processing your sample data. You receive access to a staging environment to test it yourself.
Deployment & Integration (Week 3)
We deploy the system to your infrastructure and connect it to your live data flow. You receive a complete deployment runbook with all configurations.
Post-Launch Support (Weeks 4-5)
We monitor the live system for two weeks to catch edge cases. We then deliver the full source code repository and a final handoff document.
Frequently Asked Questions
- How much does a custom AI automation project cost?
- Pricing is a fixed fee based on scope. The main factors are the number of data sources, the complexity of the business logic, and the number of systems to integrate. A system that parses one type of PDF is simpler than one that qualifies leads using three different APIs. We determine the fixed price after our initial discovery call.
- What happens when the Claude API is down or returns garbage?
- The system has built-in retry logic for transient API errors. If an error persists or the API returns malformed data that fails validation, the file is moved to a quarantine queue. A Slack alert is sent with the file ID and error details, so your team can review it manually. No data is ever lost.
- How is this different from hiring a freelancer on Upwork?
- A freelancer often delivers a script that runs on their machine. We deliver a production-ready, containerized application deployed in your cloud environment. The package includes structured logging, monitoring, automated alerting, and a detailed runbook for maintenance. It is a maintainable asset, not just a piece of code.
- Do we need our own AWS or cloud account?
- Yes. We deploy all code and infrastructure into your own cloud account. You own and control everything from day one, including the data, code, and billing. This avoids any vendor lock-in and gives you full authority over security and access. We provide instructions for setting up the required permissions for deployment.
- What kind of maintenance is required after the project?
- Most systems require minimal maintenance, such as updating an API key or adjusting a prompt. Our optional flat monthly plan covers these tasks and includes monitoring. Alternatively, your team can use the handoff documentation and runbook to manage the system internally. The system is designed to be low-touch after launch.
- Can you integrate with our proprietary on-premise software?
- If your software exposes a REST or GraphQL API, integration is usually straightforward. If it requires direct database access, has a SOAP API, or relies on scheduled CSV file transfers, the project scope will be larger. We assess the viability of any proprietary integrations during the discovery call before beginning a project.
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