Use Cases
Use Case 1 – Productivity
AI analysis of petitions received at PS/SP/CO to analyse the content and plan of action
Objective
The AI-Based Grievance Redressal Analyser is designed to assist senior police officers in identifying low-quality grievance closures by leveraging multiple GenAI agents. It aims to automate and enhance the process of extracting discrete claims from citizen complaints, guiding investigation officers with tailored evidence checklists, evaluating the quality of responses, and determining the overall petition status. By streamlining these tasks, the system supports faster, more consistent, and higher-quality grievance resolution, enabling officers to focus on critical follow-ups and improving public trust in grievance handling.
Scope
The solution encompasses four specialized AI agents working collaboratively within a minimal user interface tailored for law enforcement workflows. It covers ingestion of digital complaints, automated claim extraction and verification by senior officers, generation of evidence checklists for investigation officers, response quality assessment, and status determination for petitions. The system supports five predefined claim categories to ensure consistency during the hackathon prototype phase. Key features include an IO petition screen for detailed claim and response management and a senior officer dashboard to monitor petition statuses and finalize case closures. Evaluation metrics focus on extraction accuracy, checklist relevance, scoring alignment, and end-to-end petition closure success.
Use Case 2 – CCTNS
Al powered voice based natural language querying and report generation from CCTNS data
Objective
To create an intelligent, voice-enabled system that allows police personnel at all levels to seamlessly access and interact with the CCTNS database using natural language voice commands. This solution aims to democratize data access by removing technical barriers such as SQL query formulation, enabling officers—from constables to senior officials—to retrieve accurate and timely crime and investigation data effortlessly. The system will interpret spoken queries, translate them into precise database requests, and generate structured, easy-to-understand reports. By incorporating real-time error detection, step-by-step clarifications, and multi-language support, the system ensures reliable communication and enhances user confidence in accessing critical information, thereby improving decision-making and operational efficiency.
Scope
The solution encompasses a voice command interface supporting English and optionally Telugu, integrated with an AI-powered natural language processing engine that converts spoken queries into safe SQL statements for accessing the CCTNS database. A middleware layer ensures secure data handling, input sanitization, and access control. The system will generate readable reports featuring tables, charts, and summaries suitable for field operations, intelligence review, and management meetings. Interactive error handling will assist users in refining queries through clarifications and suggestions, preventing misinterpretation or data inaccuracies. This prototype will demonstrate a scalable client-server architecture adaptable to various police stations, paving the way for comprehensive AI-assisted utilization of CCTNS by police personnel across Andhra Pradesh.
Use Case 3 – Social Media
AI-Powered Detection and Reporting of Sponsored Scam Ads on Social Media Using Hashtag Intelligence and Content Analysis for Law Enforcement Action
Objective
Enable law enforcement to rapidly and reliably handle digital investigation tasks that typically require time-consuming manual effort. The AI-driven system automates three critical workflows: (1) identifying administrators of suspicious WhatsApp groups from shared invite links and populating legally formatted Section 69(A) request forms with the group ID, group name, and admin phone numbers; (2) extracting Instagram usernames and ad content from uploaded images to generate corresponding legal requests; and (3) deploying AI-powered WhatsApp chatbots that adopt customizable personas to engage scammers in conversation, capturing actionable intelligence such as bank details and scam patterns. These automated processes reduce delays, ensure legal accuracy, and allow officers to focus on investigative strategy rather than data collection and documentation.
Scope
This solution automates three focused digital investigation tasks to support law enforcement in gathering evidence and generating legally compliant documentation. It enables officers to extract WhatsApp group names, IDs, and administrator phone numbers from shared invite links, automatically filling and forwarding Section 69(A) legal request forms to nodal officers. It also processes uploaded screenshots of Instagram ads by identifying the advertiser’s username and content, populating the appropriate legal template for further action. Additionally, the system facilitates AI-powered WhatsApp chat interactions with suspected scammers using customizable chatbot personas, allowing investigators to safely collect critical information such as payment details and scam patterns. All outputs, including filled forms and chat logs, are linked to unique case IDs and securely stored for review, audit, or escalation—ensuring consistent, traceable, and efficient handling of digital evidence.
Use Case 4 – Investigation / Intelligence
AI-powered analysis of large volumes of CDR/IPDR
Objective
The primary aim is to build a secure, centralized, and intelligent investigation support platform that empowers police officers and investigation units with unified access to diverse data streams—including telecom, financial, and social intelligence—collected from multiple service providers. The platform is designed to leverage big data processing and advanced machine learning to surface actionable patterns, identify suspect relationships, and map organized crime networks across jurisdictions. By equipping Investigation Officers (IOs) with intuitive dashboards and real-time alerts, the system ensures timely, evidence-driven interventions. Moreover, it enhances coordination between local police stations and state-level units by offering a shared investigative workspace that promotes consistency, transparency, and agility in tackling complex cases. The platform also maintains stringent security controls and ensures full compliance with national legal frameworks and data protection regulations.
Scope
This system will support secure, role-based access via single sign-on and multi-factor authentication, enabling authorized users to initiate, track, and manage data requests through an integrated web and mobile portal. It will handle the ingestion of structured and semi-structured datasets—such as CDRs, IPDRs, and financial logs—via automated ETL pipelines and secure APIs, ensuring data integrity and consistency in a centralized data lake. Advanced analytics capabilities, including batch and real-time processing, anomaly detection, and graph-based link analysis, will be powered by scalable frameworks like Apache Spark. Outputs will be presented through customizable dashboards and interactive maps that facilitate deep-dive investigations. The platform will also interface with external intelligence tools and systems, while maintaining robust encryption, detailed audit trails, and compliance monitoring. Training, documentation, and continuous technical support are included to ensure user adoption and operational sustainability. Functions not directly related to investigation, such as legacy system overhauls or social intelligence platform development, are excluded from this scope.
Use Case 5 – Predictive Policing
AI analysis of Dial 112, FIRs to generate crime hotspots and early warnings for L&O
Objective
The initiative aims to transform unstructured Dial‑100 emergency call data into structured, actionable intelligence that empowers district-level leadership—such as SPs and Commandants—to detect and respond to emerging public safety concerns in near real-time. By automating the ingestion, classification, and analysis of call records through NLP and large language models (LLMs), the system delivers location-specific insights, highlights recurring issue types, and identifies geographic hotspots of criminal or civil unrest activity. The solution bridges operational gaps by eliminating manual review bottlenecks, accelerating situational awareness, and supporting evidence-based decision-making at both police station and battalion levels.
Scope
The system will automatically process incoming Dial‑100 logs every 10 minutes, extracting relevant textual information and associating it with jurisdictional metadata. It will apply AI-based classification to determine issue categories (e.g., domestic violence, theft, land dispute) and extract named entities like persons or landmarks. The processed data will feed into a dynamic dashboard featuring real-time heatmaps, severity scoring, and recurrence analysis to highlight top hotspots across the district. Additional outputs include daily and weekly summaries for senior officers. A role-based review interface allows Power Users to correct low-confidence classifications, enabling post-event model retraining. Robust error handling, retry mechanisms, and flagging of unprocessable records are integrated to ensure resilience. Features such as full jurisdictional tagging, notification systems, and summary report generation are included, while live voice analysis and predictive policing components remain out of scope for the current version.
Use Case 6 – Personnel Management
AI analysis of digital service record books for evaluation of rewards, medals, etc.
Objective
To develop an interactive, metadata-driven document processing system that streamlines the management of police service records. This system enables power users to define document templates specifying key fields and validation rules, while automating document classification and text extraction using OCR and vision AI. By providing an intuitive review interface that displays the original scanned document alongside a dynamic, editable form, the system facilitates rapid and accurate digitization of various records such as promotions, awards, charge memos, transfers, and complaints. The goal is to convert unstructured paper documents into clean, auditable digital records that can be easily stored, searched, and shared, enhancing operational efficiency and compliance.
Scope
The solution supports end-to-end digitization workflows starting with user uploads of scanned documents, which are automatically classified into predefined document types or manually corrected as needed. Text extraction is performed using configurable OCR engines optimized for English documents, and the extracted data is mapped into structured fields based on active templates managed by power users. The system features a two-pane review UI allowing officers or clerks to validate and correct extracted information with real-time feedback and validation rules. Finalized records are serialized into standardized JSON format and securely stored in a MongoDB database, enabling quick retrieval and audit trails. Initial implementation covers key document categories—Promotions, Awards, Charge Memos, Transfers, and Complaints—with flexibility to add more templates and improve OCR performance over time. System administration includes monitoring OCR accuracy, template versioning, and classification effectiveness to ensure continuous enhancement.
Use Case 7 – Productivity
AI analysis of WhatsApp chats and group chat data for flagging key points and early warnings
Objective
The AI-powered WhatsApp Group Assistant is designed to support police personnel by simplifying the management and analysis of extensive group chat conversations. It automatically condenses large volumes of messages into topic-based summaries, making it easier for officers to quickly grasp the main points of discussion. In addition, the assistant extracts and tracks specific tasks assigned or received within the chats, while continuously monitoring their progress and prioritization. This functionality helps law enforcement maintain organized, up-to-date situational awareness and allocate resources more effectively, reducing the time spent on manual information processing and enhancing operational efficiency.
Scope
The solution includes comprehensive capabilities to facilitate seamless integration and effective usage within police workflows. It begins with group onboarding, where key metadata is captured through a structured Group Info Template to provide necessary context for accurate AI analysis. Users can set personalized summary rules to tailor the information presented according to their specific roles and preferences. The system continuously ingests messages from WhatsApp groups, employing advanced language models to generate concise summaries and identify actionable tasks. A personalized dashboard presents these insights in an accessible format, enabling officers to monitor tasks and priorities efficiently. To ensure reliability and maintain trust, a review agent validates the AI-generated outputs before they appear on user dashboards. Together, these features create a robust platform that enhances communication clarity, task tracking, and decision-making within digital policing environments.
Use Case 8 – Productivity
AI analysis of news feeds from newspapers (vernacular and English)
Objective
The aim is to develop a minimal viable product (MVP) that automatically produces a comprehensive, police-relevant daily news digest based on a given date. This digest consolidates and organizes news articles in English, focusing on content that is relevant to specific police districts as defined by senior officers. The system uses AI to filter, group, and cross-reference news topics, providing comparative insights on how different news sources report the same events, and enriching the content with related articles from the previous seven days. The digest is delivered through a simple, user-friendly dashboard or as a PDF report, enabling officers to stay informed efficiently and make well-grounded decisions.
Scope
The solution covers a daily batch process that accepts date-based input from a news API, exclusively handling English-language articles. It leverages GenAI components to filter articles for district relevance, cluster them into coherent topics, and generate comparative summaries aligned with senior officers’ guidelines. Additionally, it attaches historical context by linking the top related articles from the prior week for each topic. The assembled digest is rendered in PDF format for easy consumption. The MVP excludes real-time news alerts, multilingual capabilities, user authentication, extended archival searches, and mobile app interfaces to focus on delivering core functionalities within a limited timeframe.