With 16 years of experience in software development, Ahmed Hegab understands that efficiency lies in automating repetitive, high-volume tasks. Seeking to integrate AI into his career, Ahmed built a tool to solve a common pain point for any company with a mobile app: managing customer feedback.
The challenge is clear: Reviewing thousands of customer comments on the Play Store or App Store one by one to detect issues and open corresponding ITSM tickets is a time-consuming, tedious task. This delay slows down issue resolution and impacts app quality.
Ahmed’s solution? An Agentic AI designed to handle this workload instantly.
🛠️ The Solution: Automated App Review Sentiment Analysis
The Agentic Workflow
- Input & Scraper: The process begins by collecting customer reviews. Ahmed uses Apify to reliably scrape reviews from the Play Store (using the package name) or App Store (using the Bundle ID). This data forms the input stream.
- Sentiment Analysis & LLM: The scraped reviews are fed into the system. A core node performs sentiment analysis and connects to an LLM model (like Google Gemini). The LLM’s role is critical: it quickly processes the high volume of text to accurately detect and flag negative comments that represent actual bugs or issues.
- Data Output: The final step involves saving the processed, negative comments into a structured database, such as a Google Sheet. From here, the data can be easily reviewed or automatically piped into an ITSM (Issue Tracking System) to open a bug ticket.
Business Impact
This agent provides immense value by:
- ✅ Saving Time: Eliminating the need for manual review of thousands of comments.
- ⌛ Accelerating Issue Detection: Allowing companies to detect and fix critical app issues quickly.
- ⚡ Improving App Quality: Ensuring a constant feedback loop between users and the development team.
Future Development
Ahmed notes that the solution can be made more robust and generic by implementing authentication features to collect credentials directly from app owners, removing the dependency on external scrapers like Apify.
Furthermore, he sees a huge opportunity to enhance customer loyalty by adding features like:
- 🔔 Automated Notifications: Sending a message back to the customer, informing them that their customer care team is working on the reported issue.
- 😄 Suggestion Collection: Using the LLM to identify and collect valuable improvement suggestions hidden within positive and neutral comments.
📈 The Architect's Pivot
This project demonstrates how a seasoned solutions architect can pivot into the AI domain. By applying his years of expertise in system architecture, Ahmed built a practical, scalable agent. His work proves that the skills acquired in traditional software development—system design, integration, and process flow—are directly transferable to building effective, business-driving AI solutions.
💡 Why Companies Should Take Notice
Ahmed Hegab isn’t just a solutions architect—he’s a builder of automated service excellence and a specialist in creating reliable feedback loops.
By combining his 16 years of hands-on software development expertise with Agentic AI, he represents the next generation of IT professionals who:
✅ Reduce Support Costs: Proactively detect and triage issues hidden in customer feedback (like app reviews), reducing the manual load on Tier 1 support and streamlining ITSM ticketing.
✅ Boost Productivity: Enable development teams to get back to fixing critical bugs faster by providing instant, classified, and actionable issue data.
✅ Ensure Architectural Reliability: Apply years of experience in system design to build scalable, robust, and dependable automation workflows using tools like Apify and Google Gemini.
✅ Drive Customer Loyalty: Build in AI features like automated customer notifications, enhancing satisfaction and improving the perception of IT service responsiveness.
For organizations aiming to lead in IT Service Management, Customer Experience Automation, or leveraging unstructured data to drive product improvement, Ahmed Hegab is the future-ready expert they need.
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