Challenge
Today, the company ingests tens of thousands of monthly survey entries—via mobile apps, web forms, and terminal kiosks—and must manually consolidate them with CSV exports and Excel macros. This three-day turnaround introduces delays, errors, and version conflicts, and critical service alerts often arrive too late for corrective action, eroding customer satisfaction and agility [2].
Solution
Serverless Ingestion: Raw CSV, JSON or PDF files trigger Event Grid → Azure Functions for schema validation, cleansing, entity extraction and timestamp enrichment [4].
Durable Storage & Indexing: Cleaned records land in Cosmos DB; Azure Cognitive Search indexes structured fields plus GPT-4 embeddings for hybrid keyword/vector search [3].
AI-Powered Clustering & Scoring: Embeddings drive thematic clustering and continuous sentiment scoring, spotting emerging praise or complaint patterns in real time [5].
Interactive Dashboards: A React SPA backed by FastAPI delivers sentiment heatmaps, trend charts, top-theme bar graphs and drill-down comment tables—accessible to non-technical executives via role-based controls.
This architecture emphasizes elastic scale, minimal ops overhead, and easy extension (e.g. social streams, call-center transcripts).
Results
- Projected reduction in feedback turnaround from 72 hours to under 10 minutes—>98% efficiency gain [1][2].
- Anticipated 50% cut in report-generation delays as executives adopt self-service analytics.
- Up to 30% of analyst effort reallocated from manual tasks to strategic insights.
- Targeted >80% dashboard adoption among senior leaders in the first month post-launch.
Introduction
The company processes 50,000+ monthly feedback entries from mobile apps, web forms and kiosks. Each arrives in different formats—CSV, JSON, PDF—and today must be manually normalized in Excel.
These batch workflows introduce delays, errors and reporting conflicts, leaving leaders without timely visibility into passenger sentiment [2]. A real-time, automated pipeline is needed to shift from reactive slide decks to proactive decision-making.
Architecture & Data Pipeline
Using Azure PaaS, files land in Blob Storage and fire Event Grid events to Azure Functions [4].
Functions run Python scripts to validate schemas, enrich with geolocation/timestamp metadata, extract entities (e.g. station, service category), then write clean records to Cosmos DB.
Azure Cognitive Search ingests both structured fields and OpenAI GPT-4 embeddings to build a hybrid index for high-speed keyword + semantic queries [3].
AI-Powered Insights
An Azure Function calls OpenAI to generate GPT-4 embeddings for each feedback entry [5], capturing nuance in tone and theme.
Cognitive Search’s vector search clusters similar entries—surface emerging topics like “ticketing delays” or “cleanliness praise”—and computes continuous sentiment scores.
Configurable thresholds detect sudden spikes in negative feedback and push alerts to operations teams in real time.
Dashboard & Self-Service
A React SPA (backed by Python FastAPI) displays modular cards: sentiment heatmaps, top-theme bar charts, and time-series trend lines.
Users filter by date range, service category or location hierarchy. Drill-down tables show raw comments alongside AI-summaries.
Interactive elements—hover tooltips, legend toggles, search bars—empower non-technical leaders to explore without analyst support.
Business Impact & Next Steps
In pilot forecasts, feedback turnaround time will drop by 98% and analysis effort by 30%, freeing resources for customer-experience programs.
Executive surveys expect >80% dashboard adoption versus 20% on legacy tools.
Phase 2 will onboard social media and call-center streams, and add predictive sentiment alerts via time-series models.
Lessons Learned & Conclusion
- Serverless scaling minimizes ops overhead and handles bursty feedback volumes automatically.
- Hybrid search (vector + keyword) balances precision & recall for natural-language queries.
- Interactive UIs drive rapid adoption by non-technical executives.
- Continuous loops feed telemetry back into retraining pipelines to keep models accurate as data evolves.
References
- [1] Noel Yuhanna, “Demystifying Real-Time Data For Analytics And Operational Workloads,” Forrester blog, September 8 2023
- [2] Jill Zheng, “How Many Days Does It Take For Respondents To Respond To Your Survey?,” SurveyMonkey Curiosity blog
- [3] “Azure Cognitive Search,” Wikipedia
- [4] “Azure Blob Storage,” Microsoft Azure
- [5] “Embeddings,” OpenAI Platform