AI-POWERED AUTOMATION PLATFORM
CRM for Price Analysis Centralized Platform for Managing and Analyzing Scraped Prices
A CRM designed to consolidate price data scraping, storage, and analysis for company and competitor services.
60%
Faster Decisions
10K+
Automated Tasks
99.9%
Uptime
AI Accuracy
99.7%
Real-time Ops
1.2M/s
Project Overview
Overview Project Background
This product serves as a centralized system for scraping and analyzing prices across company and competitor services.
It streamlines processes by consolidating data collection, analysis, and management in one tool.
The CRM targets administrators and analysts to provide an efficient workflow for price monitoring.
AI / SaaS
Industry
2024
Year
Web / Cloud
Platform
B2B Enterprise
Type
The Challenge
Challenges
Key Issues Addressed
The project focused on improving data scraping performance, integrating various service components, and enhancing user experience for admins and analysts.
01
Performance Bottlenecks
Price scraping processes were slow and inconsistent, limiting timely analysis.
02
Service Integration
Connecting multiple scraping and management services into a cohesive platform was difficult.
03
Complex User Flows
Admins and analysts required an intuitive workflow to manage data and perform analysis effectively.
Product Experience
Product Experience
User Interaction
The CRM provides different pages tailored for data scraping, information management, and analysis to support user roles.
Platform
Main Dashboard
Live System
Active Tasks
2,847
AI Models
24
Data Streams
156
Success Rate
99.7%
Scraping Management
Tools to configure and monitor price scraping from various services.
Info Pages
Centralized pages for managing collected data and service information.
Analysis Pages
Pages focused on analyzing scraped data to support competitive pricing decisions.
Core Features
Core Features
Functionality
The CRM includes capabilities for data scraping, information management, analysis, and administrative controls.
Price Scraping Automation
Automated collection of pricing data from multiple service sources.
Information Management
Centralized pages for viewing and editing scraped data.
Data Analysis Tools
Functions to analyze and compare pricing data across services.
User Role Administration
Admin and analyst roles with appropriate access and workflows.
Our Approach
Approach
Development Focus
Focused on performance improvements, system integration, and user interface refinement for admins and analysts.
01
Performance Optimization
Enhanced scraping efficiency and reduced latency.
Deliverables
02
System Integration
Connected scraping services and management modules within a monolithic Node.js app.
Deliverables
03
User Interface Design
Developed tailored pages for different user tasks to improve usability.
Deliverables
Development Timeline
Development Timeline
Project Phases
The project followed a scrum process with defined phases from discovery to release.
Phase 1 – Discovery
Requirements gathering and initial architecture planning.
Phase 2 – Implementation
Development of scraping, analysis, and management modules.
Phase 3 – Integration
Connecting all services and modules into the monolithic system.
Phase 4 – Testing
Performance and user acceptance testing to ensure stability.
Phase 5 – Release
Deployment of the CRM for internal use by admins and analysts.
Technical Architecture
Technical Architecture
System Structure
Integrated Service Connections
Multiple scraping and management services linked through a single backend.
Centralized Data Storage
PostgreSQL stores all pricing and analysis data.
Team & Collaboration
Team Collaboration
Roles and Workflow
A team of 10 members using scrum methodology with regular meetings and Jira for project tracking.
Project Manager
Coordinated project progress and facilitated team communication.
Developers
Handled backend development, scraping services, frontend integration, and testing.
Regular Communication
Daily and sprint meetings to maintain alignment.
Task Tracking
Using Jira to manage development tasks and bugs.
Project Facts
Industry
AI / Automation
Platform Type
SaaS
Architecture
Cloud-Native
Deployment
Global
Technology Stack
Technology Stack
Tools and Frameworks
The system uses standard backend and database technologies supported by cloud services.
Backend: Node.js
Database: PostgreSQL via Supabase
Project Management: Jira
Impact & Results
Impact and Results
Outcomes
The solution improved scraping speed, system scalability, and overall user experience for price analysis.
Improved Speed
Optimizations reduced delays in price scraping and data availability.
Enhanced Scalability
System architecture supports growing data volumes and users.
Better User Experience
Streamlined workflows for admins and analysts through clear interfaces.
Expanded Analysis Capabilities
Added dedicated pages to support detailed price data analysis.
Key Outcomes
Centralized price data management
Faster and reliable scraping process
Improved collaboration among analytics team
SUP.AI transformed how we operate. What used to take our team hours now happens automatically in seconds. The platform does not just automate tasks — it makes intelligent decisions that improve our business every day.
Michael Chen
Chief Operations Officer
Enterprise Technology Company
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