Case Study
Modernizing a High-Scale Reputation Management Platform
Our client is a leading product company providing review management and analytics solutions. It serves as a centralized hub for businesses - primarily in the restaurant and retail sectors - to aggregate and respond to customer feedback. They work with a wide range of clients, from large companies with many locations to smaller businesses.
COLLABORATION PERIOD: Jan 2025 - Jan 2026
INDUSTRY: Reputation Management
CLIENT'S WEBSITE: localyser.com
The Goal and The Challenge
The objective was to modernize product aging infrastructure to support market expansion. The client needed to:
Transition from outdated frameworks to a modern, maintainable stack.
Ensure the platform can handle a 10 or more times increase in users and data throughput.
Implement AI to categorize and respond to reviews, reducing manual effort for end-users.
Several critical "legacy" bottlenecks hindered the project:
The core was built on Sails.js with outdated React components, making new feature releases slow and risky. The system was pretty much a "monolith," meaning the code was tightly connected. So, even a small change often risks messing things up somewhere else unexpectedly.
Heavy background tasks (fetching reviews, syncing data) were handled via server-side cron jobs, which frequently overloaded the main API.
A lack of proper database indexing led to slow query performance as the volume of reviews grew.
A lack of automated signup and localization processes acted as a brake on rapid business growth.
Migration to NestJS
We transitioned the backend to NestJS, introducing a modular architecture with strict typing that significantly improved maintainability and developer velocity. NestJS allows us to build the app like a set of "Lego blocks." Each feature (like Reviews, Coupons, or User Settings) is now a separate, independent module.
As part of this migration, we extracted the most important logic into a "Shared NPM Package." This means the same "brain" that runs the website can now run AWS Lambda functions and the Mobile App, ensuring data is handled exactly the same way across the entire ecosystem.
Serverless Efficiency (AWS Lambda)
To resolve performance issues, we replaced traditional cron jobs with AWS Lambda functions. When you’re dealing with thousands of reviews and social media messages arriving at once, you need a way to organize them so nothing gets lost. We used AWS SQS (Simple Queue Service) to act as a digital waiting room. Even if one part of the system fails, the rest of the platform continues to run perfectly.
Our Client only pays for the exact seconds the Lambda functions are working. They don’t have to pay for expensive, powerful servers to sit idle during quiet times. Because AWS can handle almost any amount of traffic, a product can now sign up a client with 1,000 new locations tomorrow without worrying about system crashes.
AI Auto-Response & Categorization
As soon as a review is collected from the web, it’s sent to the AI engine. The AI "reads" the review, understands the context, and tags it instantly. The AI analyzes the rating and customer comments to suggest a professional, context-aware response. Because the application operates globally, the AI can draft replies in any language. This ensures a consistent, high-quality brand voice across every region. While the AI drafts the reply, the manager still has the final say. They can review, tweak, and hit "Send" in seconds.
Scalable Review Sources
A robust engine that aggregates data from Google, Facebook, Deliveroo, Talabat, and HungerStation. Instead of a giant, slow sync once a day, we used an "event-driven" approach. The system identifies which locations need updating and triggers small, fast syncs throughout the day. As reviews are collected, they are put into an AWS SQS queue. This ensures that even if 5,000 reviews arrive at the same second, the system remains stable, processing them one by one so no data is dropped or lost.
Mobile App
Previously, the App was a desk-bound platform. For restaurant managers who spend 90% of their time on the floor, this was a major barrier. We built a high-performance mobile app to bridge that gap. We integrated Firebase Cloud Messaging to send instant push notifications. Instead of checking an email hours later, a manager’s phone buzzes the second a customer leaves a review or sends a DM. We brought the same OpenAI engine from the web into the mobile app. A manager can tap a new review, let the AI draft a professional response, and hit "Send" while walking between tables. It turned reputation management from a "chore you do at the end of the shift" into a quick, 30-second task that happens throughout the day. This speed is what turns a frustrated customer into a loyal fan.
Kiosk
By catching negative feedback via the Kiosk and responding faster via the Mobile App, businesses naturally see their public star ratings climb. The Mobile App and Kiosk Mode turned the App from a passive reporting tool into an active intervention tool. It stopped being about reading what happened yesterday and started being about influencing what happens today.
Technologies Used
NestJS
Sails.js
Node.js
React.js
Ant Design
AWS Lambda
AWS SQS
BullMQ
Terraform
MySQL
OpenAI API
Google GMB & Performance APIs
Puppeteer
Facebook/Instagram Graph API
Twilio
SendGridNestJSSails.jsNode.jsReact.jsAnt DesignAWS LambdaAWS SQSBullMQTerraformMySQLOpenAI APIGoogle GMB & Performance APIsPuppeteerFacebook/Instagram Graph APITwilioSendGridThe Outcome
By turning project technical burden into a modern, AI-driven engine, we enabled the platform to scale its impact and help thousands of businesses turn customer feedback into a strategic growth tool. The technical modernization acted as a catalyst for massive business growth.
75% Increase in Managed Locations . In just one year, the platform scaled from managing parts to full businesses.
Enterprise Onboarding Success . The new architecture enabled seamless onboarding of large portfolios.
Hyper-Growth for Clients : Some clients grew their internal presence by 9x without encountering platform limitations.
Operational Efficiency . Automated categorization and AI-assisted responses allowed the client to manage a significantly larger user base with the same internal headcount.
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