Overview of the Project
Our client was a mid-sized digital marketing agency managing competitive strategy and brand positioning for B2B companies. Their team was responsible for tracking competitor product launches, blog content, feature announcements, partnerships, and market positioning changes across dozens of websites and media sources.
Manually monitoring these channels had become time-consuming and prone to missed updates. Important signals (e.g., new feature rollouts, pricing changes, or expansion into new markets) were often discovered too late to react accordingly.
- Region: Global
- Industry: Marketing / Digital Strategy
- Timeline: 2–3 months
Challenge
Marketing teams often struggle to systematically track competitors on websites, blogs, product pages, and press releases. The client, in turn, required an automated, intelligent solution capable of scanning web sources and extracting actionable insights. This way, the main challenges included:
- Manual monitoring of multiple web sources.
- Difficulty recognizing truly relevant updates among frequent publications.
- Lack of structured history and change tracking.
- Tardy awareness of new product launches or strategic shifts.
- No centralized system for filtering and summarizing competitor signals.
Main Goals
To build a feature-rich competitor intelligence system, we decided to divide the project into the following goals:
- Automate web scraping across selected competitor domains.
- Filter and classify updates based on relevance.
- Summarize long-form content into concise marketing insights.
- Maintain a structured history of tracked changes.
- Provide notifications through an intuitive interface.
- Support scalability for tracking multiple competitors at the same time.
Project Overview
We engineered an AI-agent-based monitoring system powered by agentic workflows. The platform continually scanned competitor websites, detected new or updated content, and processed it through an AI analysis pipeline.
The system then extracted meaningful updates, removed noise, generated concise summaries, and categorized information by product launches, feature updates, strategic announcements, or market positioning signals.
A Telegram-based interface allowed users to receive notifications and request on-demand summaries through conversational commands.
Solution
The final solution was a production-ready AI competitor tracking agent that unified automated scraping, intelligent filtering, content summarization, and notification delivery into a single streamlined workflow.
Relevant insights were summarized and delivered to marketing teams in near real time, significantly reducing manual monitoring.
Key Features
- Automated web scraping and update detection.
- AI-based relevance filtering and content classification.
- Concise summarization of news posts and blog updates.
- Historical tracking and change comparison.
- Real-time notifications via Telegram bot.
- Multi-competitor monitoring with structured categorization.
Technology Stack
To support proper agent orchestration and monitoring, we used the following technologies and tools:
AI Workflow Orchestration
- LangChain
- LangGraph
Web Scraping Engine
- Crawl4AI
Interface
- Telegram Bot API
Backend
- LLM-based coding agents
- AI image analysis pipelines
Data Storage
- Structured history management layer
Core Team
- Project Manager: Coordinated requirements, scope, and delivery milestones.
- Solution Architect: Created an agentic workflow and monitoring architecture.
- AI Engineers: Implemented scraping orchestration, filtering logic, and summarization pipeline.
- Backend Developer: Built history tracking and notification services.
Results
The delivered solution automated competitor monitoring across 40+ digital sources, including product pages, blogs, press sections, and landing pages. Marketing teams began receiving structured summaries and instant Telegram notifications within minutes of detected updates.
On average, the system processed 200+ new or modified pages per week, automatically filtering out more than 70% of irrelevant content and surfacing only high-impact changes. As a result, reaction time to competitor activity improved by approximately 60%, while manual monitoring workload was cut by over 80%.