Event Tracking Architecture for SaaS Workflow Engines

Project Overview:

Event Tracking Architecture for SaaS Workflow Engines

📌 Introduction

In an ultra-competitive digital marketplace, pricing is no longer static—it’s strategic. This project began as a response to a growing challenge faced by mid-sized e-commerce businesses: how to stay price-competitive without eroding margins or exhausting internal teams with manual pricing updates.

The objective was ambitious yet critical—build an autonomous, intelligent pricing engine that could monitor market signals, understand demand elasticity, and update product pricing in real-time.

⚠️ The Problem

Manual pricing strategies were not only time-consuming but also inefficient in adapting to market fluctuations. The business stakeholders highlighted three core pain points:

Delayed responsiveness to competitor pricing, often resulting in lost traffic or overpricing.
Limited data analysis capabilities to identify demand trends or optimal price points.
Risk of human error and outdated tools for managing dynamic pricing across hundreds of SKUs.

They didn’t just need automation—they needed pricing intelligence.

🛠️ The Solution

I architected and implemented a fully modular real-time pricing engine with the following layers:

🔄 Data Acquisition

Developed automated web scraping routines using Puppeteer and headless Chromium to pull competitor pricing data at regular intervals.
Integrated product performance metrics from internal APIs to track conversion rates, inventory status, and demand volatility.

⚙️ Decision Engine

Created a set of pricing rules and elasticity models, using historical sales data and market positioning logic.
Engine built with Python + FastAPI, running asynchronous jobs through Celery and managed with Redis queues.
Prices were evaluated every 10–20 minutes per category, enabling near-real-time responsiveness.

📤 Price Deployment

Used AWS Lambda to trigger updates directly to the store’s backend API after price validation.
Integrated rollback protocols to prevent edge-case pricing drops or spikes.

📈 Monitoring & Analytics

Built an internal dashboard (React + Chart.js) to visualize price changes, competitor deltas, and revenue impact.
Alerts via Slack for anomalous behaviors like underpricing thresholds or sudden competitor moves.

💡 Key Decisions & Innovations

Chose FastAPI over Flask due to its superior performance and async capabilities—critical for handling high-frequency pricing checks.

Chose FastAPI over Flask due to its superior performance and async capabilities—critical for handling high-frequency pricing checks.

Implemented elasticity scoring: price changes were weighted against past performance to avoid “race-to-the-bottom” behavior.

Emphasized fail-safe mechanisms: including hard-coded floor/ceiling limits and daily price logs for rollback capabilities.

🚀 Results & Impact

✅ Revenue Boost

I architected and implemented a fully modular real-time pricing engine with the following layers:

✅ Operational Efficiency

Pricing updates that once took 3–4 hours of manual effort daily were now handled automatically in under a minute, every 20 minutes.

✅ Scalability

The system launched with 600 products and scaled seamlessly to manage over 3,500 SKUs across 7 categories.

✅ Market Responsiveness

Client could react to market changes faster than 90% of competitors, gaining both agility and trust.

🔍 Tech Stack

Python, FastAPI, Celery, Redis – Core backend logic and job orchestration

PostgreSQL, MongoDB – Persistent storage and historical pricing data

AWS Lambda, CloudWatch, S3 – Serverless deployments and logging

Puppeteer (Node.js) – Headless scraping layer

React, Chart.js, TailwindCSS – Monitoring dashboard

📂 Supporting Links

📊 Live Dashboard Snapshot

📖 Technical Write-Up

🔐 GitHub Repo (Private/On Request)

📂 Supporting Links

This project reinforced one of my core beliefs: Automation is powerful, but intelligent automation is transformative. Beyond just speeding up processes, the pricing engine enhanced decision-making, introduced resilience, and became a competitive advantage for the business.

I’d love to take these principles into the next project that demands real-time decision systems, scalable architecture, or high-impact automation.

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Related Work

Modular CMS for Scalable Newsrooms

Problem:

Traditional CMS platforms were bloated and inflexible, leading to bottlenecks in high-frequency publishing workflows.

Solution:

Architected a fully decoupled, headless CMS using Next.js and GraphQL, with custom content schemas and dynamic rendering logic. Integrated editorial automation tools to cut publishing time by 65%.

Tech Stack:

Next.js, GraphQL, MongoDB, Tailwind, Vercel, Node.js

Impact:

• 3× faster time-to-publish
• Zero outages in 18 months
• 100% editorial adoption

Live Demo Case Study GitHub Repo