Data analytics is not scary, not scary at all.

We tend to overcomplicate some subjects. But when a new terminology appears, it appears in the same way as you learned in school, math, physics, chemistry, etc. It appears because it is needed. Data analytics is required to make a decision and a strategy. For example, the question is: what price should I put for my product? You manually go through the markets, search for similar products and set your price. You did the work of a data analyst, and it is a data-driven decision. The same thing in business, and it is Business Intelligence.

New Problem: For data analytics using the same DB (MySQL, PostgreSQL...) for analytics can cause live production db to slow down, and even corrupt, interfere important transactions. Why? Because of read operations for analytics require looking up for millions of rows.

Solution: Copy live db to new one and use newly created for analytics.

If you have created an automation workflow using n8n and want to present it to users in a clean and modern chat interface, you can use Agenytics. It transforms your workflow into a chat interface with tables, charts, and graphs. The project is built on the Laravel + Vue stack and is production-ready. You can use it to create chat interfaces for RAG systems, Business Intelligence analytics reports, and other applications.

First, you should understand that Agenytics is a self-hosted starter kit with open source code. You have complete access to the codebase and can modify or extend it according to your needs.

Below is a simple example demonstrating how to quickly integrate your n8n workflow into the chat interface.

This post walks through implementing Purchasing Power Parity (PPP) pricing per country for a Stripe Checkout purchase flow. But you can use it for any other payment gateway, such as Paddle, PayPal, Lemon Squeezy, e.t.c, just understand the logic from high level. Live demo is available at https://agenytics.com.

The reference implementation behind this article is built in PHP (Laravel 12) and it's available in https://github.com/MuhammadQuran17/ppp_stripe, but the approach is language-agnostic. This package uses World Bank PPP data and TrustIP to detect VPN/Proxy. The goal is to understand the logic and architecture so you can reproduce it in any programming language (JS, Python, Go, Ruby etc.).