Choosing the Right Engineer for Your Custom AI Reporting System
Choose an agency that delivers the full source code and builds with production-grade tools. Verify they have built similar systems and can articulate the specific technologies used.
This is not a dashboarding project; it is an engineering build. The right partner is a hands-on developer who writes production code, not a firm that assigns you a project manager. They should be able to explain their choice of database, API framework, and deployment strategy for your specific reporting needs.
We built a custom reporting pipeline for a 12-person recruiting firm processing 400 applicants a month. Their manual process of compiling reports took 6 hours weekly. The new system runs in 8 seconds every morning, summarizes key metrics using the Claude API, and posts the results directly to a management Slack channel.
What Problem Does This Solve?
Many businesses start by trying to build reports in Google Sheets or Excel. These tools are familiar, but break down quickly. A VLOOKUP across 50,000 rows of sales data times out, and scripts that pull from external APIs fail silently, leaving you with stale data and no error messages.
Business Intelligence tools like Tableau or Power BI seem like the next logical step, but they create a new problem. They are powerful visualization engines that require a clean, structured data source. If your data lives across a CRM and a proprietary ERP, the BI tool cannot join them correctly. You end up paying $70 per user per month for a tool your team cannot use because the underlying data engineering work was never done.
This leads teams to visual automation platforms. These platforms are great for connecting two standard APIs, but fail at complex data transformation. A workflow that pulls invoices from Stripe and orders from Shopify cannot easily calculate cohort-based profit margins. It often requires multiple, chained workflows that become slow, hit API rate limits, and burn through your monthly task allowance, turning a simple report into a $400/month liability.
How Does It Work?
We start by connecting directly to your data sources using their native APIs. We use Python with the httpx library for asynchronous requests, pulling data from systems like your CRM, ERP, and payment processor. This raw data is loaded and structured in a Supabase Postgres database, creating a stable foundation for all reporting logic.
With a clean data source in place, we write the core business logic in Python. This allows for complex transformations impossible in other tools, like joining data on calculated fields or performing time-series analysis. For a recent logistics client, we processed 18 months of delivery data from three separate systems to generate driver performance scorecards. The entire process ran in 75 seconds.
We then use the Claude API to add an analytical layer on top of the raw numbers. The system can read through 500 new customer support tickets, categorize them by issue type, and write a 150-word summary of emerging problems. This replaces a manager's daily hour of manual ticket review with a concise, actionable paragraph delivered to their inbox.
The entire reporting pipeline is packaged as a FastAPI service and deployed on AWS Lambda, where it runs on a schedule. This serverless architecture is highly reliable and typically costs under $30 per month to operate. Final reports are sent to their destination, whether it's a formatted PDF to an email list, a message to a Slack channel, or an update to a custom field in your Salesforce instance. The total runtime from data pull to report delivery is under 3 minutes.
What Are the Key Benefits?
Your First Report in 3 Weeks
From our first call to a live production system in 15 business days. Your team gets automated reports immediately, not after a quarter-long BI implementation project.
Pay Once for the Build, Not Per User
We deliver projects on a fixed-price basis. After launch, you only pay for minimal cloud hosting, with no recurring SaaS subscription that grows with your team size.
You Own the Code and Infrastructure
We deliver the full Python source code to your company's GitHub repository. The system is deployed in your own AWS account, giving you complete control and ownership.
Alerts on Data Source Failures
We configure CloudWatch alarms that trigger if an upstream API fails or data is missing. You receive an immediate Slack notification, so you always know your reports are accurate.
Connects to Your Business Systems
We build direct integrations to your CRM, ERP, or industry-specific platforms. Data flows automatically without requiring your team to learn or log into any new software.
What Does the Process Look Like?
Week 1: Scoping and API Access
You provide read-only access to the necessary data sources. We perform a data audit and deliver a technical specification document outlining the exact logic and report format.
Week 2: Core Pipeline Build
We build the data extraction and transformation logic. You receive access to a staging database to review the cleaned, structured data and verify its accuracy.
Week 3: Deployment and Delivery
We deploy the system to your cloud infrastructure and configure the reporting schedule. Your team receives the first automated report in its final destination (e.g., Slack, email).
Weeks 4-6: Monitoring and Handoff
We monitor the system for three weeks to ensure stability and accuracy. At the end of the period, we deliver a complete runbook with documentation for ongoing maintenance.
Frequently Asked Questions
- How is a project priced and how long does it typically take?
- Pricing is a fixed fee based on scope. The main factors are the number of data source integrations, the complexity of the business logic, and the cleanliness of the source data. A typical custom reporting system takes 2-4 weeks from kickoff to deployment. We provide a fixed-price quote after our initial discovery call, so you know the full cost upfront before committing.
- What happens if an external API is down when a report is scheduled to run?
- The system is built with resilience in mind. We implement exponential backoff and retry logic for API calls using the httpx library. If an API is down for an extended period, the process will time out gracefully and send a failure alert to a designated Slack channel. This ensures you are immediately aware of the issue without receiving an incomplete or inaccurate report.
- How is this different from hiring a freelance data analyst on Upwork?
- A freelance analyst typically delivers a one-off analysis, often in a Jupyter Notebook or a spreadsheet. We deliver a production-grade, automated software system deployed on AWS Lambda that runs reliably every day without manual intervention. We focus on software engineering principles like logging, monitoring, and automated testing, ensuring the system you get is maintainable for the long term.
- How do you handle sensitive data security?
- We never store your data on our systems. The entire reporting pipeline is built and deployed directly within your own secure cloud environment (e.g., your AWS account). We use temporary, read-only credentials during development which are revoked upon project completion. You maintain full ownership and control over your data and the infrastructure it runs on at all times.
- Can I request changes to the reports after the project is complete?
- Yes. We scope and price minor changes, like adding a new field to a report, as small, one-off projects. For ongoing needs, we offer an optional flat monthly maintenance plan that covers a set number of change requests and provides continued monitoring. This gives you flexibility without locking you into a long-term contract for support you may not need.
- Why use custom Python code instead of just connecting a BI tool?
- BI tools are excellent for visualization but poor at data transformation and integration. Python provides unlimited flexibility to connect to any API, enforce complex business rules, and integrate AI capabilities like the Claude API for text summarization. Our approach does the hard data engineering work in code, providing a perfectly clean data set that you can optionally plug a BI tool into later.
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