Public complaint processing system
Case Study Summary
Client: Confidential
Industry: Public sector
Role: AI Solution Architect
Impact Metrics:
- 70% reduction in manual complaint handling time
- 91% complaint categorization accuracy
- Top 5 complaint themes identified within the first week
- Unified five intake channels into one processing pipeline
- Automated anonymization before analysis to meet privacy requirements
I built an end-to-end complaint processing system that ingests text, voice, and handwritten submissions, normalizes and anonymizes them, and delivers clustered insights to a dashboard so public-sector teams can prioritize responses quickly.
The Challenge
The entity received complaints from email, WhatsApp, voice, and handwritten letters, but every channel required different handling and manual triage. The team needed a single pipeline that could standardize content, protect personally identifiable information, and surface top themes without slowing response times.
The Solution
→ Implementation
I designed a staged pipeline (ingestion → normalization → anonymization → topic modeling → insights) on Google Cloud to keep processing consistent across formats. Cloud Functions orchestrated ingestion, Document AI handled OCR for handwritten letters, and Speech-to-Text converted voice messages into text. A small Vertex AI LLM standardized complaint language, clustered topics, and summarized themes for a lightweight web dashboard used by staff.
→ Solution Architecture
Architecture diagram placeholder per request.
→ Tech Stack
- Infrastructure: Google Cloud Platform
- Data Platform: Cloud Storage
- CI/CD: Cloud Build
- Containerization: Serverless Cloud Functions
- Backend: Cloud Functions, Cloud Run (dashboard API)
- Modeling: Vertex AI, Document AI, Speech-to-Text API
- Vector Index: Not used (topic clustering in Vertex AI)
Key Learnings
- Multichannel normalization: Standardized text, voice, and OCR outputs early to keep downstream logic simple.
- Privacy compliance: Stripped PII before any LLM processing to meet regulatory requirements.
- Cost control: Chose a small LLM in Vertex AI to balance accuracy with operating costs.
- Operational relevance: Tailored cluster summaries to the language used by frontline teams.
Measurable Impact
- 70% reduction in manual complaint handling time
- 91% complaint categorization accuracy
- Top five complaint themes identified within the first week
- Faster prioritization of response plans using cluster summaries
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