# AI-Powered Property Tax Query Resolution Pilot Model for Smart City Using DeepQuery

### **1\. Background & Objective**

Bilaspur, one of the 100 Smart Cities selected under the National Smart Cities Mission, aimed to improve citizen-centric digital services—starting with property tax. Historically, property tax queries were managed via static websites and in-person counters, both of which failed to provide real-time, comprehensible, multilingual support to residents.

To address this, Bilaspur Smart City Ltd. partnered with **Presear Softwares Pvt. Ltd.** to deploy **DeepQuery**, a multilingual AI-powered assistant trained specifically for municipal property tax services.

**Objective:**

* Enable 24×7 query resolution for property tax via WhatsApp and web.
    
* Simplify legal provisions and policy explanations using natural language.
    
* Increase tax compliance and citizen satisfaction using AI.
    

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### **2\. Dataset Preparation & Knowledgebase Design**

The bot’s core intelligence was developed from statutory documents and real citizen pain points.

**Key Sources:**

* Chhattisgarh Municipal Corporation Act – Part IV Chapter XI: Taxation
    
* Property tax demand notices, rebate policies, penalty circulars
    
* Citizen RTI responses and past FAQs from Bilaspur Municipal Corporation
    
* Internal municipal SOPs and mutation workflows
    

**Knowledgebase Highlights:**

* 300+ bilingual Q&A pairs created manually
    
* Covered intents like tax calculation, rebates, penalty, payment deadlines, receipts, mutation status
    
* Structured into contextual categories for retrieval
    

**Example Intent Coverage:**

* "How much tax do I owe this year?"
    
* "क्या छूट मार्च के बाद मिलती है?"
    
* "I paid online but didn’t get a receipt."
    

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### **3\. Language Support & NLP Tuning**

Given Bilaspur’s linguistic diversity, DeepQuery was built with multilingual capability at its core.

**Approach:**

* All content was translated into Hindi with contextual integrity.
    
* Hinglish and misspelled inputs were normalized using phonetic matching.
    
* Regional dialect queries were supported via synonym mapping (e.g., “bhugtan”, “jama”, “kar”).
    

**Enhancements:**

* Used custom embeddings for semantic similarity in both Hindi and English
    
* Integrated fallback keyword detection to handle out-of-scope queries gracefully
    
* Voice-to-text (STT) and text-to-speech (TTS) modules were added for accessibility
    

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### **4\. Model Architecture & Training**

DeepQuery used a hybrid **Retrieval-Augmented Generation (RAG)** approach, tuned for government document comprehension.

**Architecture:**

* Embedding-based semantic search (using in-house vector database)
    
* Transformer-based response generation tuned on government corpus
    
* Custom logic for contextual grounding (e.g., ward-specific rules, due dates)
    

**Training Loop:**

* Initial supervised Q&A-based fine-tuning
    
* Weekly retraining with new queries from real usage
    
* Feedback from BMC officials and citizens used to refine answer sets
    

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### **5\. Integration & Deployment**

**Platforms:**

* Embedded Web Widget on Bilaspur Smart City official portal
    

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### **6\. Results & Impact**

**Within 60 days of deployment:**

* 25,000+ citizen queries handled
    
* < 5 seconds average response time
    
* 92% satisfaction score
    
* Significant reduction in load on municipal helplines and counters
    

**Citizen Experience:**

* Queries answered in natural language
    
* Voice support enabled for illiterate and elderly users
    
* Proactive reminders about rebate expiry via WhatsApp
    

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### **7\. Challenges & Learnings**

| Challenge | Solution Implemented |
| --- | --- |
| Variability in ward-level rules | Ward-specific filters and rule mapping |
| Phonetic Hindi and hybrid input | Phoneme-matching and transliteration layer |
| Legacy record inconsistencies | Introduced fallback messaging with manual redirection |
| Data security and citizen identity | OTP login and request-based access to personal tax details |

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### **8\. Alignment with Cities Innovation Exchange (CiX)**

DeepQuery directly supports CiX goals of scalable, AI-powered urban solutions.

* Demonstrates scalable use of LLMs in urban governance
    
* Promotes inclusion via multilingual, multimodal access
    
* Reduces human dependency for high-volume citizen services
    
* Builds a replicable template for other urban local bodies in India
    

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### **9\. Way Forward**

* Expansion into other domains: water tax, building approvals, trade licenses
    
* Automated multilingual reminders for tax deadlines
    
* Integration with Digital Property Ledger for real-time mutation updates
    
* Real-time grievance lodging and tracking within the same AI assistant