1. Overview
We developed a Proof of Concept (PoC) for a public-sector organization, focused on building an AI-powered prime data assistant. The assistant is designed to analyze structured crime data and provide intelligent insights through both vector-based AI search and structured statistical queries. The core objective of the project was to enable authorized users to query records in natural language and receive both factual and predictive answers in real time.
2. Problem Statement
Although the organization maintains detailed crime records, actionable insights are often buried inside free-text narratives. Manual analysis is slow, error-prone, and unsuitable for real-time or predictive decision-making.
- Incident records include dates, locations, nationalities, and detailed textual descriptions
- Important insights are embedded within free-text narratives
- Manual report review is time-consuming and error-prone
- Difficult to answer statistical and predictive questions using traditional queries
- Requires a hybrid system combining natural language understanding, structured analysis, and prediction
3. Proposed Solution
Our proposed PoC integrates AI-based semantic understanding with structured data processing. The system we designed uses a multi-agent architecture and Large Language Model (LLM) capabilities. The AI Assistant also supports vector search, allowing it to find information embedded within unstructured textual descriptions, enabling users to retrieve insights beyond the limitations of conventional SQL searches.
- Intent Detection: Identify the intent behind user queries.
- Query Classification: Distinguish between descriptive, statistical, and predictive questions.
- Query Generation: Generate structured SQL-like queries to extract data.
- Data Analysis: Perform data analysis and produce concise, contextual answers.
4. Implementation
The PoC follows a multi-stage processing pipeline designed for reliability and explainability.
Intent Analysis Agent
Parses the user’s question (e.g., “What is the most common case in Deira last week?”)
Determines the intent (e.g., type, time range, area, predictive or statistical)
- Query Formulation Agent
- Maps the identified intent to the data structure
- Builds a structured query compatible with the database schema
- Execution Agent
- Runs the generated query on the dataset
- Handles error detection and passes back any failed queries for correction
- Analysis & Response Agent
- Interprets the dataset returned from the query
- Performs statistical or predictive analysis based on the query type
- Produces final responses in natural language
For predictive analysis, the system utilizes historical data to identify trends and anticipate future patterns using built-in statistical models.
5. Results and Impact
The PoC successfully demonstrated accurate intent detection, dynamic query generation from natural language, and hybrid processing across structured records and unstructured narratives. Early predictive modeling showed promising pattern detection across regions and timeframes, validating feasibility for future scale.
- Intent Detection: Accurate intent detection for complex, multi-layered queries.
- Query Generation: Dynamic query generation from natural-language inputs.
- Hybrid Processing: Hybrid AI capability, combining structured and unstructured data processing.
- Predictive Modeling: Early success in predictive modeling of regional data patterns across multiple administrative areas.
6. Conclusion & Future Scope
The AI-based Prime Data Assistant represents a significant step toward intelligent, data-driven governance systems, with planned future developments including expanding predictive capabilities using multi-year machine learning models, integrating real-time data sources such as live reports and sensor inputs for dynamic insights, and building a user-friendly dashboard with both voice and text query support, ultimately positioning the system as a cornerstone of a broader smart governance initiative that enables teams to leverage AI for decision support, forecasting, and rapid response.
At Gravity Base, our AI team designs public-sector PoCs like this to demonstrate how advanced analytics and AI can support data-driven decision-making at scale.