CASE STUDY
Elevating Customer Stories with Node, MongoDB, Flutter and Azure – Tatvam
Tatvam — a unified customer feedback analytics and sentiment intelligence platform aggregating social media, online reviews, surveys, and support logs with drill-down analytics built on Node.js, MongoDB, Flutter, and Azure.
Cognic Tatvam
Search feedback channels, sentiment trends or topics…
Multi-Channel Voice of Customer (VoC) & Topic Sentiment
1.2M+
Aggregated Customer Reviews
94.6%
Topic Sentiment Precision
< 500ms
Multi-Dimension Filter Speed
+42%
Customer NPS Improvement
| Feedback Source | Topic Identified | Customer Narrative Snippet | Calculated Sentiment | Action Status | Confidence |
|---|---|---|---|---|---|
| Google Reviews (Boston) | Check-In Experience | “Front desk staff was extremely helpful and swift…” | undefined | Positive (+0.88) | 99% |
| Twitter / X Mention | Mobile App Usability | “Biometric login failed after latest OS update…” | undefined | Negative (-0.65) | 97% |
| Post-Visit Survey | Service Cleanliness | “Immaculate facilities, will definitely return…” | undefined | Positive (+0.92) | 100% |
| Zendesk Support Log | Billing Transparency | “Disputed unexpected recurring service charge…” | undefined | Negative (-0.72) | 96% |
| TripAdvisor Review | Staff Hospitality | “Concierge went above and beyond for our anniversary…” | undefined | Positive (+0.95) | 100% |
1.2M+ Reviews Customer Feedback Aggregated
Ingested from social media, review sites, surveys, and support logs
Real-Time Topic & Sentiment Analysis
Automated NLP categorizing customer themes and sentiment scores
Flutter iOS/Android Cross-Platform Mobile App
Executive mobile dashboards built on Google Flutter for iOS & Android
Azure Cloud High-Scalability Node & Mongo
Enterprise cloud hosting with sub-second multi-dimensional querying
What is Tatvam Voice of Customer (VoC) Analytics?
Tatvam is an enterprise customer feedback analytics and sentiment intelligence platform that aggregates data from Google Reviews, TripAdvisor, Twitter, survey tools, and support desks, applies Natural Language Processing (NLP) to detect operational topics, scores customer sentiment, and delivers actionable insights via web and Flutter mobile apps.
A Smarter Way to Listen to the Voice of the Customer
The Challenge
Customer feedback scattered across isolated review sites, social channels, and emails with no way to measure sentiment objectively.
The Solution
End-to-end cloud platform combining Node.js, MongoDB, Flutter, and Azure with specialized NLP topic-clustering models.
The Outcome
Eliminated feedback silos, enabled rapid resolution of operational bottlenecks, and increased client NPS scores by 42%.
The Challenge
Customer experience and marketing leaders struggle to make data-driven improvements when customer opinions are fragmented across dozens of review sites and social networks.
Customer feedback fragmented across social media, review sites, surveys, and support tickets
No unified single view of customer sentiment across different store and hotel locations
Manual compilation of feedback in spreadsheets delaying the discovery of critical complaints
Inability to drill down into specific operational topics (e.g., cleanliness, billing, mobile app)
Lack of defensible quantitative metrics to prove the impact of customer experience investments
Executives lacking mobile access to real-time customer satisfaction pulses on their phones
Difficulty alerting location managers immediately when severe negative reviews are posted
High latency when running analytical queries across millions of historical unstructured feedback records
Fragmented Manual Compilation vs. Tatvam Automated VoC Platform
| Workflow Stage | Traditional Manual Workflow | Cognic AI-Powered Engine |
|---|---|---|
| Data Aggregation | Marketing interns manually copying reviews from Google and TripAdvisor | Automated multi-channel API collectors syncing reviews, surveys, & social posts 24/7 |
| Topic Categorization | Manual tagging in spreadsheets with subjective and inconsistent categories | NLP semantic topic extraction classifying feedback into 40+ operational areas |
| Sentiment Scoring | Basic star ratings that fail to capture nuanced written feedback | Granular sentence-level sentiment analysis scoring text from -1.0 to +1.0 |
| Mobile Access | No mobile tools—reports only accessible as monthly desktop PDF slide decks | Native Flutter iOS and Android mobile app with live KPI notifications |
| Root Cause Drill-Down | Impossible to trace high-level sentiment dips back to specific store shifts | 1-click drill-down from corporate macro scores to exact customer review text |
| Executive Action | Action taken months after customer issues occurred | Automated negative sentiment alerts triggering same-day manager follow-up |
The Tatvam Customer Intelligence Engine
Three core pillars connecting feedback collection, semantic NLP intelligence, and mobile delivery.
01
Multi-Channel Ingestion Collectors
Aggregates customer reviews, survey responses, social media mentions, and support tickets automatically.
• Google & TripAdvisor scrapers
• Social listening webhooks
• Zendesk & SurveyMonkey connectors
→
02
NLP Topic & Sentiment Classifier
Performs semantic analysis to categorize operational themes and calculate granular customer sentiment scores.
• Operational topic clustering
• Sentence-level sentiment analysis
• Real-time negative alert triggers
→
03
Flutter Mobile & Azure Web Hub
Provides executive dashboards, location comparisons, and cross-platform iOS/Android mobile apps.
• Flutter cross-platform mobile apps
• Azure Node.js & MongoDB architecture
• Interactive Power BI reporting
→
Multi-Channel Ingestion
Aggregates all reviews & social feeds
Semantic NLP
Granular topic & sentiment classification
Flutter Mobile
Real-time executive mobile dashboards
Platform Architecture & Processing Pipeline
A scalable, cloud-native customer feedback intelligence pipeline.
1 Feedback Ingestion
› Review Site Scrapers
› Social Media Webhooks
› Survey API Feeds
› Support Ticket Logs
› Azure Event Hub
2 NLP Sentiment Tier
› Topic Extraction Model
› Sentiment Scoring Engine
› Keyword Cloud Indexer
› Spam & Noise Filter
› MongoDB Atlas Store
3 Analytics & Search
› Elasticsearch Index
› Node.js REST API Tier
› Location Hierarchy Matcher
› Trend Line Calculator
› Alert Dispatcher
4 Delivery & Mobile
› Flutter iOS / Android App
› React.js Web Portal
› Power BI Embed Dashboards
› PDF Executive Digest
› Azure Cloud Shield
From Online Review to Resolved Customer Experience Issue
An end-to-end customer feedback to management action workflow.
Customer Review
→
API Collector
→
NLP Sentiment
→
Topic Drill-Down
→
Mobile Alert
→
Resolved Issue
Customer NLP & Mobile Engineering Capabilities
Key technical innovations powering the Tatvam platform.
Granular Operational Topic Clustering
Automatically organizes unstructured text into specific operational categories like Front Desk, Billing, Food, and Speed
Sentence-Level Sentiment Scoring
Accurately measures mixed reviews where a customer praises the location but complains about wait times
Cross-Platform Flutter Mobile Apps
Native 60fps performance on both iOS and Android with customized push notifications for critical feedback
High-Performance MongoDB & Node.js Backend
Executes sub-500ms multi-dimensional aggregate queries across millions of customer reviews
Automated Manager Anomaly Alerts
Dispatches real-time alerts when negative review velocity exceeds normal baseline thresholds
Customer Experience Director & Feedback Workbench
Customer experience managers inspect live sentiment trends, drill down into regional complaint clusters, and trigger resolution tasks.
| Feedback Source | Topic Identified | Customer Review Text | Calculated Sentiment | Action | ||
|---|---|---|---|---|---|---|
| Google Reviews (Boston) | Check-In Experience | “Front desk staff was extremely helpful and swift…” | +0.88 Positive | Positive | 99% | |
| Twitter / X Mention | Mobile App Usability | “Biometric login failed after latest OS update…” | -0.65 Negative | Action Taken | 97% | |
| Post-Visit Survey | Service Cleanliness | “Immaculate facilities, will definitely return…” | +0.92 Positive | Positive | 100% | |
| Zendesk Support Log | Billing Transparency | “Disputed unexpected recurring service charge…” | undefined | Escalated | 96% |
Enterprise Cloud Governance & Security
Robust cloud data architecture safeguarding corporate feedback and analytics.
Azure Cloud Security
Encrypted MongoDB Atlas
OAuth 2.0 Auth
Role-Based Access
AES-256 Data Protection
Immutable Audit Trail
Technology Stack
Key technologies powering the production deployment.
Node.js & Express
MongoDB Atlas
Flutter (Dart)
Microsoft Azure
Elasticsearch
Python NLP
REST APIs
Docker Containers
TailwindCSS
What Changed
Key operational and efficiency improvements.
Aggregated over 1.2M+ customer reviews across dozens of previously disconnected channels
Topic and sentiment accuracy reached 94.6% across diverse customer text formats
Executive team gained real-time mobile visibility on iOS and Android into customer satisfaction
Operational response time to critical customer complaints dropped from 3 weeks to under 4 hours
Client Net Promoter Score (NPS) improved by 42% within 6 months of platform rollout
Built for Customer Experience & Brand Leaders
Engineered for Chief Customer Officers, VP of Marketing, and operational brand directors.
Chief Customer Officers (CCOs)
Obtain defensible, real-time metrics proving customer satisfaction and ROI across all locations.
VP of Marketing & Brand Strategy
Identify emerging customer sentiment trends and protect brand reputation before issues escalate.
Regional Operations Directors
Drill down into specific location performance and benchmark customer satisfaction scores.