Background

Land acquisition is a critical component of infrastructure development and public welfare projects in India. It facilitates the implementation of highways, railways, industrial corridors, irrigation projects, urban development, renewable energy initiatives, and other strategic infrastructure. The process involves multiple stakeholders, including land requiring bodies, land acquiring authorities, district administrations, state governments, and central ministries.

Description of the Study

Web-based National Land Acquisition & Management System that digitizes the complete land acquisition lifecycle—from project proposal submission to final possession of land. The proposed platform should provide standardized workflows for different stakeholders and enable seamless coordination among Central Ministries, State Governments, District Authorities, and Project Implementing Agencies. The system should facilitate online submission and approval of proposals, digital scrutiny, document management, automated workflow routing, and status tracking at every stage.The platform should support geo-tagging of acquired land parcels using GIS technology, enabling visualization of project locations on interactive maps. It should also maintain real-time information on key land acquisition parameters such as:

Expected Solution

The solution should incorporate role-based access control, automated alerts and notifications, API-based integration with relevant government systems, customizable dashboards, analytical reports, and predictive analytics to support policy formulation and efficient project execution. The system should provide

Background

CMPDI/CIL subsidiaries play a key role in providing geological and mining information to the Ministry of Coal and responding to parliamentary and high-priority administrative inquiries. These reports require compilation of data from scanned PDFs, digital documents, spreadsheets, images, and historical archives. The current workflow is largely manual, resulting in: High dependence on individual expertise Delay in generating reports and analytics Higher probability of manual errors Limited ability to quickly retrieve insights when required Objectives Deploy an automated platform for AI-assisted geological, mining and any other production figures document processing and reporting. Enhance data validation, consistency, and traceability across historical and contemporary datasets. Build an efficient, scalable foundation for future digital transformation initiatives within each CIL subsidiary and the Ministry of Coal. Desired Outcomes The solution should be implemented in structured phases, including requirement analysis, data digitization and pre-processing, platform development, system testing, integration with CIL subsidiary workflows, training, and continuous enhancement to ensure scalability and long-term adoption.

Background

A significant gap exists between the skills acquired in academic institutions and the competencies expected by industries. Students often struggle to identify the skills required for their desired career paths, while industries face challenges in finding candidates with the right skill sets. Similarly, academicians have limited visibility into industry internship opportunities that could help them gain practical exposure and align teaching with current industry practices. There is a need for a unified platform that connects students, industries, and academicians, enabling seamless collaboration and skill development.

Description

The proposed solution is a centralized Academia–Industry Collaboration Portal that serves as a one-stop platform for students, industries, and academicians.

Key Features include Skill Assessment Students complete a questionnaire to evaluate their technical and soft skills shared by industry. The system generates a skill profile and identifies strengths and skill gaps based on current industry requirements. Skill Mapping Based on the assessment, the platform recommends relevant industries, job roles, and skill development programs aligned with industry requirements. Industry Internship & Job Opportunities: Industries can post internships, projects, apprenticeships, and entry-level job openings with required skills. Students receive recommendations based on their skill profiles and can apply directly. Industry Learning Programs Companies can publish training programs, certification courses, workshops, and mentorship initiatives to help students acquire in-demand skills before applying. Allow students to search, apply, and track internship and placement opportunities through a single platform. Provide a dedicated portal for academicians to explore faculty internships, industrial training, Faculty Development Programs (FDPs), consultancy opportunities, and collaborative research projects. Facilitate industry–academia collaboration through mentorship programs, workshops, guest lectures, innovation challenges, and live industry projects. Enable institutions to monitor student skill development, internship participation, and placement progress through dashboards and analytics. Maintain a digital portfolio for students containing verified skills, certifications, projects, internships, and achievements to improve employability.

Expected Solution

The solution should provide: The solution should provide a secure, scalable, and intelligent platform that supports the complete lifecycle of skill development, internships, and placements. Skill Development- Skill assessment through questionnaires and aptitude tests. Skill profiling and identification of technical and soft skill gaps. Personalized learning recommendations, certification programs, and industry-relevant training. Career guidance based on individual skills, interests, and industry demand. Student digital portfolios showcasing verified skills, certifications, projects, and achievements. Internship- Centralized internship portal where industries can post internship opportunities with required skills. Matching of students to internships based on their skill profiles and career interests. Internship application and tracking system for students. Internship opportunities for academicians, industrial training, and Faculty Development Programs (FDPs). Progress tracking, mentor feedback, and internship completion records. Placement- Industry portal for posting job opportunities with required qualifications and skill sets. Recommendation engine to match students with relevant placement opportunities. Candidate shortlisting based on skill compatibility and eligibility. Application tracking and recruitment management for students and recruiters. Analytics and reporting dashboards for institutions and industries to monitor placement readiness, recruitment outcomes, and skill demand trends. Overall Platform Features- Role-based access for students, academicians, industries, and institutions. Secure document management for resumes, certificates, internship reports, and academic records. Collaboration features for industry mentorship, live projects, workshops, and research partnerships. Integration with learning platforms, certification providers, and institutional databases. Comprehensive analytics to support data-driven decisions for institutions, industries, and policymakers.

Develop an intelligent energy-management system using AI for load forecasting, renewable energy integration and fuel optimization under extreme polar conditions.

Health-conscious users Highlight Air Quality Index (AQI), pollen count, UV index, and humidity levels to help users manage allergies, asthma, or skin sensitivity. Outdoor fitness enthusiasts Show sunrise/sunset times, 'best running hours,' wind speed, and heat alerts to optimize workout planning. Beachgoers & surfers Display sea conditions, tide timings, wave height, and water temperature for safe and enjoyable beach activities. Travelers Provide quick access to saved destinations, severe weather alerts for flights, and packing suggestions (e.g., 'Carry a raincoat in London'). Parents & families Emphasize school commute conditions, rain alerts, and severe weather warnings to plan daily routines. Agriculture & gardeners Show soil moisture, rainfall predictions, frost alerts, and seasonal planting guidance. Commuters Integrate weather with traffic updates, visibility conditions, and alerts for storms or fog that affect travel. Event planners Offer extended forecasts, probability of rain, and 'comfort index' for outdoor gatherings or weddings.

Problem Statement

Different forecasting systems perform differently depending on region, season, lead time and weather situation. Physical NWP models, ensemble forecasts and AI/ML weather models may each have strengths under different conditions. Therefore, there is a need for an intelligent blending system that can dynamically combine multiple forecasts. The challenge is to develop a hybrid AI–NWP blending framework that assigns adaptive weights to different forecast sources based on historical skill, forecast lead time, region, season and weather regime. The final product should provide an optimized forecast for rainfall, temperature, wind and extreme weather indicators.

Expected Outcome

Description

Dynamically blended forecast Best-combined forecast from multiple model sources Model weight maps Indication of which model is more reliable for each region/lead time Improved forecast skill Better performance than individual models Extreme weather guidance Improved signals for heavy rainfall, heat wave and high-wind events Operational workflow Automated script/dashboard for routine forecast blending

The Indian Summer Monsoon dictates the economic livelihood of millions of farmers, particularly during the Kharif sowing season. While macro-scale monsoon forecasts across large meteorological subdivisions have improved, Indian agriculture remains highly vulnerable to the unpredictable nature of intra-seasonal variations. Specifically, the exact dates of monsoon onset,prolonged dry spells (break-monsoon phases), and subsequent revival cycles vary drastically from one district to another. Standard regional forecasts lack the spatial granularity required for localized agricultural planning.If a farmer sows seeds during a false onset just before a major breakthrough pause, entire crops fail due to moisture stress, leading to crushing financial losses. The challenge is to build a hybrid predictive framework capable of delivering a 7-to-30-day probabilistic outlook of monsoon behavior at the Block and Panchayat (Village cluster) scale. The system must bridge the gap between global climate teleconnections and hyper-local weather outcomes. Participants should design a solution that ingests large-scale climate indices—such as the El Niño-Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), and Madden-Julian Oscillation (MJO)—and downscales their signatures using advanced machine learning models to predict localized precipitation behavior, onset thresholds, and active/break durations.Develop a hybrid mathematical or machine learning model that pairs global planetary boundary conditions (ENSO, IOD, MJO phases) with regional atmospheric data to predict local rainfall anomalies. Generate dynamic, color-coded risk maps at the block/panchayat level illustrating the statistical probability percentage of monsoon onset, continuous dry spells (breaks), or heavy downpours 1 to 4 weeks in advance. Build an expert-system engine that translates rainfall probabilities into localized crop-specific agronomic advisories (e.g., advising farmers to delay sowing, prepare irrigation alternatives, or alter crop choices based on upcoming break phases). A mobile-optimized web application or automated SMS/WhatsApp API gateway that pushes clear,actionable text-based advisories in regional Indian languages directly to farmers and local agricultural extension officers.

Background

The government actively supports the socio-economic upliftment of marginalized communities,particularly micro-entrepreneurs, artisans, and weavers. Financial assistance is provided to establish small-scale manufacturing and handicraft units. To help these beneficiaries sell their goods, market exposure is facilitated through periodic physical exhibitions, cluster development programs, and trade fairs (such as Shilp Samagam, Surajkund Mela, and Dilli Haat).While physical exhibitions provide a temporary boost in sales, these micro-entrepreneurs lack continuous, year-round access to broader digital markets. Transitioning to the digital economy is hindered by low digital literacy, language barriers, and a lack of technical skills required to professionally photograph, price, and catalog products for modern e-commerce.

Challenge

There is a critical need to bridge the gap between traditional craftsmanship and modern digital commerce. Beneficiaries struggle to present their products competitively online. They often fail to capture high-quality images, write compelling product descriptions, or understand dynamic market pricing.The challenge is to build an intuitive, AI-driven mobile application that acts as a 'virtual business manager' for these artisans. The app must empower them to seamlessly digitize their inventory, optimize their listings using AI, and connect directly with larger B2B buyers or government e-marketplaces without requiring advanced technical knowledge.

Expected Solution

Participants are expected to develop an AI-powered, cross-platform mobile application supported by a robust, scalable backend architecture. To ensure high adoption among low-literacy users, the application must feature a highly responsive, minimalist UI/UX design (incorporating modern, clean visual hierarchies and accessible layouts). Key

features

description to suggest an optimal, competitive selling price based on current market trends and raw material costs.

Impact Goals

Provide marginalized micro-entrepreneurs with a continuous, year-round digital sales channel,reducing their dependency on periodic physical fairs. Drastically lower the barrier to entry for digital commerce through intuitive AI automation. Improve digital literacy and financial independence, ultimately increasing the average annual income of the target demographic.

Background

Challenge

Expected Solution

Participants are required to develop an NLP-powered, multilingual AI Business Advisory Assistant tailored for rural and semi-urban geographies. The system should take basic inputs from the user Geographic Location (Village/Block/District), Available Margin Capital (e.g., ₹1,00,000), and the Proposed Business Category (e.g., Dairy, Retail, Textiles etc). The application must feature two core modules Module 1: Hyper-Local Business Feasibility Report The AI must dynamically generate a localized strategy encompassing:

output

Impact Goals

Background

Challenge

Expected Solution

Participants are expected to develop a comprehensive platform that includes:

Impact Goals

Enhance financial literacy among the target demographic regarding concessional lending. Improve transparency and efficiency in the channel finance ecosystem, ensuring faster disbursements and better fund utilization.

Problem Statement

Develop a centralized mobile application for real-time monitoring, surprise inspections, CCTV surveillance integration, and random inspection assignment for projects/institutes/NGOs running under DoSJE schemes.

Key Features

Stakeholders

Expected Outcomes

Background

The Members of Parliament Local Area Development Scheme (MPLADS) is a Central Sector Scheme under which Hon'ble Members of Parliament recommend developmental works for creation of durable community assets and provision of basic civic amenities.

The Scheme involves large-scale fund utilization and execution of thousands of works across the country through multiple implementing agencies and administrative authorities.

Given the volume and complexity of financial and project-related data generated under the Scheme, there is a need for an AI-powered solution that can leverage machine learning and advanced analytics to detect trends and anomalies in expenditure patterns, fund utilization, cost estimates, and work execution, thereby enabling early identification of potential fraud, inefficiencies, and non-compliance while enhancing transparency, accountability, and effective monitoring of MPLADS works.

Description

Develop an AI-powered monitoring and analytics platform for MPLADS that leverages Machine Learning (ML) , Artificial Intelligence (AI), and advanced data analytics to identify trend, anomalies, irregularities, and potential fraud in fund utilization and project execution.

The solution should analyze data relating to sanctions,expenditures, cost estimates, work progress, payments, and asset creation to detect unusual patterns, cost overruns, duplicate works, delayed projects, and deviations from established norms.

The system should generate risk-based alerts, predictive insights, and decision-support dashboards for Members of Parliament, State Nodal Authorities, District Authorities, and the Ministry.

The platform should also facilitate automated compliance monitoring, trend analysis, and early warning mechanisms to improve transparency, accountability, and efficiency in the implementation of MPLADS works across the country.

Expected Solution

The proposed solution should be an AI-powered platform that helps monitor MPLADS works and fund utilization in a smarter and more efficient manner.

Background

Recent advancements in generative AI and neural speech synthesis have made high-fidelity voice cloning possible from only a few seconds of recorded audio.

Threat actors are exploiting these capabilities to impersonate CXOs,government officials, and trusted individuals in order to initiate fraudulent financial transactions, manipulate employees,or bypass verification procedures in high-risk workflows.

Conventional call verification methods—such as caller ID, manual call-back, and basic voice familiarity—are no longer sufficient to distinguish genuine callers from AI-generated or manipulated voices, especially in high-pressure social engineering scenarios.

These attacks are increasingly orchestrated over VoIP, mobile networks, and enterprise collaboration platforms,sometimes combined with leaked personal information to create highly convincing narratives.

The absence of automated detection of synthetic or cloned voices in real time significantly increases the likelihood of large-scale financial fraud and reputational damage to institutions that rely heavily on telephonic instructions and approvals.

Problem Statement

Current telephony and communication ecosystems lack a robust, AI-driven mechanism to detect and flag voice cloning or synthetic speech impersonation during live calls.

Existing solutions rarely perform granular analysis of acoustic artifacts, prosody, and speech generation patterns and typically cannot provide an actionable risk score while the conversation is ongoing.There is a need for an end-to-end security framework that can analyze incoming voice streams in near real time,determine the likelihood that the caller is using a cloned or AI-generated voice, and provide timely alerts and recommendations before sensitive actions—such as approval of fund transfers or disclosure of confidential information—are taken.

The solution must be privacy-preserving, scalable across telecom and enterprise environments, and support multilingual contexts with diverse Indian accents and dialects.

Proposed Solution

Develop an AI-powered, real-time voice integrity verification framework that integrates advanced deep learning,digital signal processing, and contextual analysis to detect AI-generated or manipulated voices.

The system should continuously process live or near live audio streams from telephony, VoIP, and collaboration platforms,extract discriminative features, and compute a dynamic impersonation risk score.The framework should expose APIs and SDKs for seamless integration with banking applications, enterprise communication systems, and telecom operator infrastructures, enabling proactive fraud prevention and enhanced cyber resilience in voice channels.

Key Components

Multi-Layer Voice Authenticity Analysis Acoustic and spectral analysis using deep learning models to detect synthesis artifacts, phase inconsistencies, and spectral signatures indicative of cloned or AI-generated audio.

Prosody and behavioral analysis to model speech rhythm, pitch contours, pauses, and microvariations, differentiating natural human speech from neural TTS outputs.

Cross-session consistency checks comparing ongoing call features against historical genuine samples (where available) to detect anomalies in speaker identity.

Real-Time Risk Scoring Engine Continuous computation of a confidence/risk score indicating the probability of impersonation or synthetic speech.

Threshold-based alerting logic configurable for different risk scenarios (for example, high-value transaction calls, privileged access approvals).

Contextual enrichment using metadata such as call origin, known contact information, transaction context, and historical fraud indicators.

Alerting and User Interaction Layer Multi-channel alert mechanisms (UI prompts, SMS/email, in-app notifications) for frontline staff and end users.

Pre-transaction warning prompts recommending secondary verification such as call-back, multifactor authentication, or escalation to supervisors.

Configurable workflows for banks,enterprises, and government agencies to define automated responses when impersonation risk crosses thresholds.

Privacy and Compliance Module Minimal retention of voice recordings with options for on-device or edge inference to reduce central storage of sensitive audio data.

Support for anonymization or feature-only logging to comply with data protection and privacy requirements.

Platform and Integration APIs REST/gRPC APIs and SDKs for integration with core banking systems, contact center platforms, enterprise communication tools, and telecom networks.

Support for multiple Indian languages and regional accents through language-agnostic feature extraction and language-specific acoustic models.

Expected Outcomes

Background

Almost every optimization problem in India's refining, petrochemical, power, logistics, manufacturing and planning sectors ultimately depends on a handful of foreign mathematical optimization solvers such as IBM ILOG CPLEX, Gurobi and FICO Xpress. These engines sit behind refinery scheduling, production planning, supply chain optimization, blending, energy management and many AI-driven decision-support systems. While they are extremely capable, they come with high recurring license costs, restrictive licensing models and limited visibility into the underlying optimization algorithms. Indian developers can formulate optimization problems, but they cannot inspect, modify or tailor the solver internals to suit strategic national requirements. Open-source alternatives such as COIN-OR CBC, HiGHS, GLPK and SCIP exist and have made significant progress, but they still lag behind commercial solvers for several classes of large-scale mixed-integer optimization problems and have not been developed, validated or optimized specifically for Indian industrial use cases. The real challenge is not building the modeling interface; it is developing a numerically robust optimization engine that consistently finds high-quality solutions for large, sparse and highly constrained industrial problems within practical computation times.

Description

The objective is to develop a sovereign mathematical optimization solver core rather than a complete modeling environment. The solver should support Linear Programming (LP), Mixed-Integer Linear Programming (MILP) and Quadratic Programming (QP) as the initial focus, with a modular architecture that can later be extended to Mixed-Integer Quadratic Programming (MIQP), Nonlinear Programming (NLP) and Mixed-Integer Nonlinear Programming (MINLP). Core algorithms may include revised simplex and interior-point methods for continuous optimization, together with branch-and-bound, branch-and-cut, cutting planes, presolve, heuristics and advanced node selection strategies for mixed-integer problems. The solver should exploit sparse matrix techniques, efficient numerical linear algebra and multi-core parallelization, with GPU acceleration considered where it provides measurable benefits. The emphasis is on numerical stability, scalability and reliable convergence across large industrial optimization problems rather than on graphical interfaces or modelling tools. It shall not be built upon any existing open source solver library but shall be built from scratch from mathematical foundation. The scope is to solve optimization problems arising from refinery scheduling, crude blending, process optimization, production planning, logistics, power system dispatch, transportation and supply chain management. The benchmark is that the solver should consistently deliver optimal or near-optimal solutions for industrial-scale problems involving thousands to millions of variables and constraints, including highly degenerate models, ill-conditioned matrices and difficult mixed-integer formulations where weaker implementations exhibit excessive computation times or fail to converge.

Expected Solution

A robust optimization engine with a basic application programming interface (API) or command-line interface is sufficient; a polished graphical user interface is not required. The solver should successfully solve standard benchmark problems from recognised optimization libraries such as MIPLIB, Netlib or Mittelmann benchmark sets, with solution quality and computational performance compared against at least one established commercial or open-source solver. A clear demonstration of numerical robustness should be provided by solving challenging large-scale optimization problems involving degeneracy, weak LP relaxations or ill-conditioned constraint matrices, where simpler implementations struggle to achieve reliable convergence or acceptable solution times. The resulting solver should provide a transparent, extensible and sovereign foundation for future Indian optimization software across industrial, scientific and strategic applications.

Problem Description

Skill-development programmes may be designed using broad or historical occupation categories that do not fully reflect changing technologies, local industry demand, job roles, productivity standards and employer expectations.Course curricula, equipment, trainer capacity and assessment methods may lag emerging requirements. Employers may struggle to identify job-ready candidates, while trainees may complete courses that have limited placement potential. The challenge is to create a continuous, evidence-based mechanism for translating industry demand into course design, capacity planning, trainer development and candidate guidance.

Expected Solution

Outcome A labour-market intelligence and curriculum-alignment platform that combines job-posting signals, employer surveys, industry consultations, sector growth data, placement outcomes and emerging-technology trends to identify demand by role, skill, location and proficiency level. The system should map skill gaps to qualifications and courses,recommend curriculum updates, flag obsolete or oversupplied courses,support employer validation and generate district-level training plans.

Expected Outcomes include stronger placement rates, reduced mismatch, improved employer satisfaction, timely course revision, better equipment and trainer planning, and clearer career pathways for candidates.

Background

The raw data for analysis of signal collected off the air typically range from few Khz to Ghz bands. The analysis is being carried out manually to identify the signal parameters and the resultant data is then utilised for processing signals in the designated sensors. This data is often insufficient for fine grain analysis for parameter extraction such as modulation type, sampling rate, FEC, interleaving, etc. This creates a need for advanced data processing to extract the observation data.

Description

The terrestrial signals received from various sources includes data in HF, VHF and UHF bands. The raw data collected in the form of .wav or .IQ format to retain the characteristics of wave form. The analysis of signals is primarily dependent on the basic characteristics of data points selected during recording of these signals. Since the data point are recorded from different sensors and different locations, the parameters may vary. Therefore, the data available for analysis is often insufficient to clearly identify fine details such as sampling rate, modulation type, interleaving, FEC etc. This limitation reduces the accuracy and confidence of interpretation and data analysis that require detailed information. The data saved as .IQ and .wav have different parameters and therefore they store the raw information in different format. These files have to be processed in different ways for signal analysis to extract signal parameters. The problem can be addressed using advanced models such as GNU Radio, python, C++ to enhance the parameter extraction capability and more information rich inputs. The spectral relationship from training data containing both .IQ and .wav formats can be utilised for identifying signal parameters and carry out deeper analysis. The expected solution should be able to demodulate signals. The GUI based model will have features to take .IQ or .wav file as input data and perform following tasks.

Expected Solution

The expected system should improve feature visibility of signals with the help of GUI, enable automated signal analysis to identify spectral features such as sampling frequency, constellation plot, water fall (time-frequency domain), demodulate signals, carry out de-interleaving and error correction. The output can then be used to carry out correlation of bit stream for identification of header and payload.

Background

Social media platforms are complex ecosystems driven by human emotion, diverse demographics, and interconnected networks. To truly understand an online community, it is required look beneath the surface. This requires understanding how followers feel (Sentiment Analysis), who those followers are (Demographics), what topics are captivating them (Trend Tracking), and how they influence one another (Link Analysis). Combining these four vectors using AI is the key to unlocking true audience intelligence.

Description

Participants will be challenged to design and build an AI-driven Social Media Analytics Framework that processes raw platform data to extract deep, actionable audience insights. The system must leverage advanced Artificial Intelligence and Machine Learning techniques to simultaneously infer follower sentiment, map audience demographics, identify top trending narratives, and perform link/network analysis to uncover how information and influence flow among followers.

Expected Solution

AI solution must address the following five core components:

Background

Modern enterprises generate massive volumes of logs from a wide range of sources, including network devices, servers, operating systems, applications, databases, cloud services, containers, endpoint security tools, identity and access management systems, IoT devices, and other hardware and software platforms. These logs are produced in diverse formats such as Syslog, JSON, XML, CSV, CEF, LEEF, proprietary vendor formats, and application-specific schemas. The diversity of log structures creates significant challenges in centralized monitoring, security operations, compliance reporting, incident investigation, and threat analytics. Security teams often spend substantial effort developing source-specific parsers and normalization rules before the data can be effectively utilized by SIEM, data lake, or machine learning platforms. As organizations adopt hybrid, multi-cloud, and AI-driven environments, the need for a universal and extensible log standard that can accommodate both current and future data sources have become increasingly critical.

Detailed Description

Design and develop a Universal Log Pre-processing Framework (ULPF) capable of ingesting, parsing, normalizing, and standardizing logs and events generated by any hardware or software system. The framework should support diverse event sources while preserving the original event data for forensic and compliance purposes. It should transform heterogeneous logs into a unified schema that enables consistent analytics, correlation, visualization, threat hunting, anomaly detection, and machine learning applications. The framework must be scalable, extensible, vendor-agnostic, and suitable for deployment in Big Data environments handling billions of events per day.

Expected Solutions

This solution should cover universal event schema and processing framework that enables:

Current Scope

Build a framework that converts any perimeter network device-generated log or event—regardless of source, format, vendor, or technology into a standardized, lossless, analytics-ready representation for next-generation SIEM and cybersecurity platforms.

Expected Solution/Deliverables for Evaluation

Source Code Link (GitHub/Drive Link) Readme with Setup Instructions Architecture Document (Max 2 Pages) Demo Video (Max 2 Minutes) Technical Presentation (Max 5 Slides)

Background

The National Critical Information Infrastructure Protection Centre (NCIIPC) assesses the cyber resilience of Critical Sector Entities (CSEs). As part of these assessments, NCIIPC performs manual reviews of samples of security alerts and case-management records generated by Security Operations Centres (SOCs). These reviews have consistently produced valuable supervisory findings that were not evident through policies, audits, self-assessments, management reports, KPI dashboards, or compliance documentation. The purpose of these reviews is not to assess individual alerts. Rather, alert and case-management data are used as operational evidence to assess whether a CSE possesses effective capabilities relating to: (i). Threat Detection (ii). Investigation (iii). Escalation (iv). Incident Response (v). Security Operations (vi). Governance and Oversight (vii). Operational Discipline (viii). Cyber Resilience While effective, manual review is resource-intensive and difficult to scale across a growing number of CSEs and increasing volumes of security data.

Description

NCIIPC seeks a deployable Supervisory Analytics Tool for SOC Assessment (SAT-SA) that assists supervisors in analysing SOC alert and case-management data at scale.The tool should help supervisors: (i). Identify entities requiring supervisory attention. (ii). Prioritise alert samples and investigations for manual review. (iii). Detect operational weaknesses and cyber resilience concerns. (iv). Improve the efficiency, consistency and scalability of supervisory assessments. The tool is intended to support human examiners and supervisory decision-making. It is not intended to replace supervisory judgement. 1. Out of

Scope

The proposed solution is not intended to (i). Function or replace as a Security Operations Centre (SOC) of CSEs. (ii). Perform real-time monitoring. (iii). Act as a SIEM platform. (iv). Act as a centralized SOC for multiple entities. (v). Continuously collect logs or telemetry from CSEs. (vi). Serve as a national cyber monitoring platform. The solution should be viewed as a supervisory analytics capability, not an operational security capability.

Negative Space Situations where expected evidence is absent. Examples (i). Missing telemetry from critical systems. (ii). Absence of expected alert categories. (iii). Missing investigations or escalation records. (iv). Unexpectedly low activity levels. (v). Monitoring blind spots. (vi). Absence of evidence that would normally be expected within comparable environments. The tool should help supervisors identify both known and previously unknown indicators of these conditions.

Illustrative Supervisory Use Cases

The following examples are illustrative and not exhaustive. The tool may assist in identifying (i). High-severity alerts closed unusually quickly. (ii). Repeated alerts on the same asset without evidence of root-cause remediation. (iii). Critical alerts closed without appropriate escalation. (iv). Critical systems generating little or no security telemetry. (v). Significant deviations from peer entities. (vi). Missing monitoring coverage for critical environments. (vii). Repetitive investigation patterns suggesting superficial review. (viii). Operational behaviours that satisfy performance metrics without effectively managing cyber risk. (ix). Investigation or escalation workloads inconsistent with expected activity levels. Participants are encouraged to identify additional supervisory signals beyond these examples.

Deployment Requirements

The solution shall operate within an NCIIPC-controlled environment. The solution must (i). Operate in a fully offline (air-gapped) network. (ii). Require no Internet connectivity. (iii). Have no dependency on cloud services. (iv). Have no dependency on SaaS platforms. (v). Have no dependency on externally hosted AI models or APIs. (vi). Support local deployment and local data processing. Where AI or machine learning is proposed, participants shall specify: (i). Model architecture. (ii). Hardware requirements. (iii). Offline training and inference approach. (iv). Model update mechanism. (v). Explainability controls. (vi). Auditability controls.

Deliverables

Participants should provide (i). Solution architecture. (ii). Functional design. (iii). Analytics methodology. (iv). Data requirements. (v). Tool or Prototype (vi). Infrastructure requirements. (vii). Validation methodology. (viii). Estimated deployment and operational requirements.

Expected Solution/Deliverables

For Evaluation Source Code Link (GitHub/Drive Link) Readme with Setup Instructions Architecture Document (Max 2 Pages) Demo Video (Max 2 Minutes) Technical Presentation (Max 5 Slides)

Background

Industrial facilities generate thermal signatures that can be observed from space, but current satellite-based monitoring systems like NASA FIRMS cannot distinguish between different types of thermal anomalies. To address this, there is a challenge to develop an AI-enabled geospatial system that integrates thermal data, land-cover information, industrial databases, and satellite imagery to automatically identify, classify, and monitor industrial fires and persistent thermal sources.

Description

Industrial facilities such as oil refineries, petrochemical complexes, thermal power plants, steel industries, mining areas, and LNG terminals generate thermal signatures that can be observed from space. In addition, accidental industrial fires, gas leaks, explosions, and abnormal thermal events pose significant risks to critical infrastructure, public safety, and the environment. Current satellite-based fire monitoring systems such as NASA FIRMS provide thermal anomaly detections but do not distinguish between industrial fires, gas flares, agricultural burning, mining activity, and wildfires. The challenge is to develop an AI-enabled geospatial system that can automatically identify, classify, and monitor industrial fires and persistent thermal sources by integrating thermal anomaly data, land-cover information, industrial infrastructure databases, and satellite imagery.

Expected Solution

Deliverables

Background

The world Monitor application is a Web/ Mobile platform that provides users with real-time monitoring, analytics, and reporting features. The application handles user authentication, data visualization, API communication, and role-based access controls. As a security analyst, the task is to evaluate the application's security posture and identify vulnerabilities that could compromise the confidentiality, integrity, or availability of the system.

Description

Conduct an authorized security assessment of the World Monitor application to:

Scope

The assessment should focus on Authentication and session management Authorization and access control Input validation and data handling API security Client-side security controls Secure communication mechanisms Data storage and privacy protections Success Criteria The assessment is considered successful if At least one valid vulnerability is identified and documented. Evidence supports the existence of the vulnerability. Risk and impact are clearly explained. Practical mitigation strategies are provided Expected Solution/Deliverables: For each vulnerability discovered, provide: Vulnerability title Description Affected component Severity rating (e.g., CVSS) Steps to reproduce Proof of concept demonstrating the issue in a safe testing environment Business impact assessment Remediation recommendations constraints Testing must be performed only on authorized systems. No actions should affect production users or data. Exploitation should be limited to proof-of-concept validation. Compliance with applicable laws, policies, and ethical hacking guidelines is required.

Background

OIL collects large volumes of UA/UC observations, near-miss and incident reports through its HSSE platform but these are triaged manually after certain time intervals such as monthly, quarterly etc.However, Global best practice (DEKRA Martin & Black 2015; EEI SIF Precursor model; VelocityEHS 2024 PSIF classifier) has established that low-severity incidents do not share the same causes as fatalities — non-fatal US accidents fell 51% over 15 years while fatalities fell only 25.5%.Leading operators therefore separately flag the ~20–25% of reports carrying genuine fatal potential.

Problem Description

Build a prototype that ingests OIL's free-text safety reports and automatically

Expected Outcome/Solution

A working AI/NLP with an interactive dashboard that ranks sites/activities by SIF-precursor density and auto-maps to Life-Saving Rules, enabling HSE to focus interventions where fatal potential is highest.

Relevant Data Availability

(if any) OIL's UA/UC observations, near-miss and incident reports.

Background

Vehicle logistics, ride-hailing services, quick commerce and emergency responders heavily rely on smartphone-based navigation apps (like Google Maps or MapmyIndia) powered by GNSS (GPS/Galileo/NavIC etc). However, when a vehicle enters a long underground tunnel/ underpass, a multi-level parking lot, a dense forested highway, or a deep urban canyon surrounded by skyscrapers, GNSS connectivity drops entirely. GNSS signals are inherently weak and vulnerable to structural blockage (urban canyons, dense foliage, tunnels, deep valleys) and unintentional electromagnetic interferences from variety of sources such as jamming. This causes navigation apps to freeze, jump erratically, or miscalculate upcoming turns, leading to missed exits, delivery delays, and safety hazards. In these environments, systems must rely on self-contained Inertial Navigation Systems (INS) built from Inertial Measurement Units (IMUs) (accelerometers and gyroscopes) to calculate position via dead reckoning during GNSS outage and switch back to GNSS aided INS after blackout. While INS is immune to external jamming, low-cost tactical or MEMS-grade IMUs suffer from inherent sensor biases, deterministic errors, and thermo-mechanical noise. While modern high-end cars possess factory-fitted, wheel-connected Inertial Navigation Systems (INS), the vast majority of vehicles on Indian roads—including commercial trucks, older cars, and millions of two-wheelers (motorcycles/scooters)—rely solely on the driver’s smartphone mounted on the dashboard or placed in mobile holder. Using a smartphone's internal MEMS IMU (accelerometer and gyroscope) to track vehicle position via dead reckoning during a GNSS blackout is highly challenging. The smartphone is subjected to severe chassis vibrations, engine harmonics, sudden braking, and road potholes. Without an external speedometer feed from the vehicle's OBD-II port, calculating distance and velocity exclusively from consumer-grade smartphone sensors results in exponential error accumulation, causing the estimated location to drift away within seconds. To overcome these challenges, there is a need for AI-ML enhanced dead reckoning and sensor fusion(GNSS+INS) techniques that integrate AI and machine learning models with real-time correction strategies.

Description

The goal is to develop a lightweight, edge-deployable software engine and mobile application that transforms a standalone smartphone into an Intelligent Dead Reckoning (IDR) system with GNSS Fusion. When a GNSS outage occurs, the application must instantly transition to inertial tracking(INS), maintaining lane-level accuracy without requiring any physical connection to the vehicle’s internal computer and seamlessly switch back to GNSS aided INS solution. To bypass the need for an external speedometer, the solution must employ AI/ML models trained on vehicle kinematics to accurately predict vehicle speed and acceleration profiles solely from the smartphone’s noisy accelerometer/gyro inputs. It must dynamically detect and filter out non-navigation motions such as engine idling vibrations, pothole shocks, bumps, and accidental phone misalignments on the mount. Furthermore, the navigation engine should implement a smart Map-Matching Filter. By overlaying the inertial trajectory onto an offline map database (e.g., Open Street Map), the system should use the road layout as a constraint. For instance, it can apply Non-Holonomic Constraints (NHC), assuming a car cannot slide sideways or fly upwards, to dramatically snap the drifting IMU path back onto the actual road grid. Also, the GNSS+INS fusion Algorithm should employ AI/ML techniques to develop an AI based fusion model to mitigate drift errors and provide accurate position. The Final solution and AI/ML models developed should not be constricted to smart phone IMU sensors data alone (Mobile application). These algorithms/models should also work with any other external IMU sensors data (Edge deployable software engine). Dataset Details IO-VNBD: Inertial and Odometry benchmark dataset for ground vehicle positioning. This dataset should be used to train & test the models and submit for screening of proposals. Teams are required to include the preliminary AI models and the results of the position plot inferenced from the subset of IO-VNBD dataset as part of their proposals submitted for evaluation. During the screening process more datasets will be provided for further evaluation of the AI models. The On-Device Workflow Dead reckoning and GNSS fusion algorithms are hybrid. Complex training happens in the cloud/desktop apriori, while inference happens on the smartphone.

Expected Solution

The final deliverable must be a working mobile application and an Edge deployable software engine exhibiting the following technical capabilities: In-Vehicle Alignment & Calibration Engine: An algorithmic module that automatically determines the phone’s pitch, roll, and yaw relative to the vehicle's driving direction, whether the phone is strictly dashboard-mounted or placed in mobile holder. AI Speed & Vibration Filter: A deep-learning or statistical signal-processing model running locally on the phone that filters out high-frequency road noise/potholes and directly estimates vehicle forward velocity from IMU signals. Advanced Map-Matching & Kinematic Constraints: A framework (e.g., AI-ML framework or Unscented Kalman Filter + Hidden Markov Map Matching) that binds the calculated position to known road networks and geometric paths during a dropout. GNSS+INS Fusion Engine: An innovative AI based Sensor Fusion Algorithm that combines GNSS & IMU measurements and provides significant improvement in overall output by eliminating drift errors and providing accurate position and velocity. Seamless GNSS Deficit Handler An instant seamless transition mechanism between GNSS aided INS and Dead reckoning modes within milliseconds of GNSS signal blackout and vice-versa. Real-time Navigation Interface A functional mobile application with UI displaying a smooth, uninterrupted vehicle icon showing seamless navigation. Performance Benchmark Dead Reckoning: The solution must restrict positional drift to less than 10% of the total distance travelled using smartphone IMUs sensors during GNSS signals blackout (for e.g., in case of smartphones IMU, a drift of less than 5 meters is desired over 50m GNSS denied environment in <1 minutes OR less than 100m of drift over a 1km GNSS denied environment at a speed of 60kmph in tunnels/underground metro OR similar simulated environments where GNSS signals are unavailable). GNSS+INS Fusion: Position update rate of 10Hz with processing on smartphones (Mobile application) and higher update rates on Edge deployable software engine using FOG based IMU sensors data (around 200Hz).

Background

Free Space Optical Communication (FSOC) offers unprecedented advantages for next-generation mobile networks, including gigabit-to-terabit data rates, license-free spectrum operation, high immunity to electromagnetic interference, etc. However, deploying FSOC links between mobile platforms (satellites, UAVs presents a severe challenge of pointing, acquisition and tracking (PAT) of highly narrow laser beams. PAT typically happens in two stages coarse alignment and fine alignment. Coarse alignment is one of the key challenges of PAT, where the transmitting terminal must first locate and maintain the remote terminal within its camera Field-of-View (FOV). Developing and testing such algorithms on real hardware requires expensive cameras, pan-tilt mechanisms, and optical components & equipment. A software based virtual camera tracking provides an inexpensive and accessible platform for algorithm development and learning.

Description

Unlike conventional radio-frequency systems, FSOC relies on a highly directional optical beam. Even a small angular error can prevent successful communication. Before fine pointing mechanism can take over, a coarse alignment stage must: Observe the surrounding environment, Acquire and detect the remote terminal or beacon, Estimate the position, and Continuously adjust the pointing direction to maintain visibility. The participants shall develop this coarse alignment process in software, allowing to develop and validate tracking algorithms without specialized hardware and setup. The following section provides reference parameters and performance criteria to be considered for the software development.

Parameters and Specifications

Functional HEADING Objective Develop a software system that autonomously detects, identifies, and continuously tracks a designated moving target within a virtual scene by controlling a virtual camera viewport. Expected Solution Participants shall develop an AI-assisted camera tracking system capable of automatically detecting and continuously tracking a moving optical beacon in a simulated video stream while controlling a virtual pan-tilt camera. The developed software shall be able to Generate a configurable virtual environment, Generate one or more moving targets, Implement a movable virtual camera, Detect the target beacon automatically, Track the beacon continuously using computer vision, Control and reposition the virtual camera, Generate and introduce disturbances due to atmospheric turbulence, platform vibrations, camera motion, noise, etc., in the virtual camera feed, Display tracking performance and statistics in real-time Deliverables Each participating team shall submit the following mandatory deliverables Software Application A standalone executable application implementing the complete virtual camera tracking system. The application shall provide all the mandatory functions and features as described above. Source Code Complete source code with proper documentation. The code shall be modular and adequately commented. Technical Report The technical report (about 10-15 pages) containing problem understanding, system architecture, description of software modules, tracking methods, AI methods (if used), test methodology, performance analysis and future improvements shall be submitted. User Manual The user manual with the description of installation of software, application operation, parameter configuration, GUI description, etc. shall be submitted. A 3–5 minutes video may also be provided as an optional deliverable for demonstration of the application. Performance Log The software should be capable of automatically generating a performance report containing simulation duration, FPS, acquisition time, average and maximum tracking error, lock retention rate, processing time, etc.

Evaluation Method and Criteria

The solutions developed by participating teams will be evaluated using multi-layered evaluation method. The following table describes stages of evaluation, their weightage and methods.

Background

AI agents are becoming omnipresent in the current era and can play an important role in our digital interactions. If an agentic AI pipeline has access to our visual context, screen states, they can assist users in complex workflows and automate many tasks. Most of the agentic AI pipelines are deployed on server side which limits the type to data that a user can share with it. It would open a new dimension of possibilities, if a local agent is deployed on user machine particularly browser which can eliminate the need to share the sensitive data with the server. Local system generally has fewer resources than server and is unable to host a full-fledged pipeline therefore only the non-sensitive data such as structure of the screen, application fields etc can be sent to server for processing. Modern browser APIs (such as WebGPU and WebAssembly) and local inference libraries (like ONNX Runtime Web and Transformers.js) have unlocked the ability to run lightweight machine learning models directly on the client. The aim is to bridge these two environments leveraging the reasoning power of cloud or server based AI while strictly enforcing data privacy at the client side.

Description

Participants are required to build a privacy-preserving vision agent which runs on browser. This involves implementing a client-side architecture where a local Vision Transformer (ViT) or equivalent computer vision model 'reads' the user's screen and takes decision based on that. If it requires the visual context to be sent to server, it shall sanitize the sensitive/PII data using DOM tags or any other method, before any network request is made. It should dynamically detect and redact sensitive elements. For example, blurring faces, blacking out passwords, and masking PII etc. Only this anonymized, unidentifiable data should be transmitted to the central server which should be aware for this redaction scheme and can process data accordingly. The server will then process the sanitized context and return actionable commands for the browser agent to execute. Participants must balance the trade-offs between inference latency and the accuracy.

Expected Solution

A successful submission should include a working prototype consisting of client side extension and server that demonstrates the following: Client-side (extension/JS) running in popular browsers (chrome, Firefox) components: Local Vision Processing Implementation of a client-side vision model running in the browser (e.g., via WebGPU) that evaluates the current screen state. Privacy Preserving Filter A mechanism for sanitizing sensitive or personal visual data. This can be achieved through local bounding-box redaction, semantic obfuscation, masking etc. This should be clearly demonstrated. Server-side implementation components Server Side Integration The transmission of the anonymized visual context to a centralized LLM/VLM, which successfully interprets the sanitized data and returns the response which may be processed data to be again ingested by local client or an UI action (e.g., 'click the submit button,' 'scroll down') that the local client executes. Participants are free to use any offline deployable (open-source/open-weights) model on server side. During SIH they can use cloud hosted version of these. An end-to-end task assisting the user should be demonstrated. Evaluation will be done on the following metrics 1- Accuracy of visual context from screen – 25% 2- Recall and precision for detection of sensitive/PII data – 20% 3- Precision of redaction – 20% 4- Client side resource utilization – 20% 5- Overall end-to-end latency of the provided task 15%

Background

As vocal audio information is very data intensive making it difficult to transmit through low data rate links. In alert and distress based scenarios Transmitting Audio information is critical instead of written message as it will be more inclusive and will cater to everyone even if they are literate or not.

Description

Build an Android App with lightweight, highly accurate STT and TTS models for 10 Indian Languages (Hindi, Gujarati, Marathi, Kannada, Malayalam, Tamil, Telugu, Odia, Bengali, English) that runs locally on a low-power device. The system’s STT module when activated after detecting pauses and stoppages should form the sentences detected and must instantly and efficiently stream the data through wifi/Bluetooth connected embedded device or another phone with same application with minimal latency. The systems TTS module when activated after receiving the Text data should convert it into intelligible speech which will be played as a voice note and alert type messages will be announced at highest volume non-interruptible. To verify the complete loop two phones with same app one in TTS mode and another in STT mode can be connected via wifi or Bluetooth and it should work like a walkie talkie using push to talk feature, if turned off it should work like a phone. Key Metrics for Evaluation Efficiency Model size, App size (RAM/Flash footprint) and CPU usage during idle listening. (20%) Accuracy Low Word Error Rate for STT and High human legibility and flow for TTS. (40%) Latency The Time delay between the Words said and STT completion, Time delay between the text received and audio processed and played for TTS along with RTF (Real Time Factor). The time delta between the sentence said and the same sentence started as audio in another phone. (20%) Software & Framework Restrictions Open-Source Only The use of proprietary, closed-source, or commercial voice-activation SDKs is strictly prohibited. Allowed Frameworks Teams must build their pipelines using open-source machine learning and TinyML frameworks. Recommended tools include TensorFlow Lite for Microcontrollers, PyTorch Mobile or similar. Fully Offline Working Model or pipeline should work fully offline only and no internet hosted API based solutions are expected and encouraged for the STT or TTS.

Expected Solution

Teams are expected to deliver a robust, deployable system architecture. A successful submission must strictly satisfy the following technical boundaries: Hardware & Runtime Environment: The Android application must run smoothly on Low and Mid rage mobile phones.

Background

As humanity aims for space missions such as BAS and lunar missions, real-time ground support becomes impossible due to communication delays. An AI-based HAR system acts as an on-board assistant that supports the execution of scientific experiments, ensuring the success of science beyond Earth's orbit. In the space environment, AI-based HAR system may act as mission-critical support for astronauts. By tracking astronaut movements and activities in real time, HAR ensures scientific experiments and related protocols are executed flawlessly without requiring constant, high-bandwidth communication with mission control.

Description

Challenge is to design and train an AI model that recognizes and validates the sequence of a pre-defined experiment using human activity recognition techniques. Standalone operation Space stations operate on restricted data bandwidth to Earth. Rather than streaming raw video to ground control, data is processed locally at the 'edge.' Inputs are given from fixed-payload cameras. Dataset generation to train model for object detection, pose estimation and hand-object interaction based on the steps of the experiment. Optional Another challenge is that Standard 2D or ground-based 3D posture models fail because astronauts do not have a fixed 'up' or 'down' orientation. The AI model should use orientation-agnostic 3D Human Mesh Recovery (HMR) to track the astronaut’s body relative to the payload rack, not the floor.

Expected Solution

The software should continuously process local video feeds to track the sequence of experiment. At the start or after each step, the model should suggest the next step to be performed. It should alert when a step is skipped or an out of sequence step is added. It should be a voice based alert. Using the live video, it should generate a timestamped and structured lightweight text file of the conducted steps with outcomes/ status. Stream the video of the experiment to specific IP and also store the video locally. A graphical user interface for monitoring the above activities.

Deliverable

A trained AI model that runs on offline standalone system

Background

Accurate Digital Elevation Models (DEMs) and Digital Surface Models (DSMs) are fundamental to urban planning, disaster management, and military reconnaissance. Traditionally, elevation data is acquired through stereo-imaging pairs, LiDAR, or Interferometric Synthetic Aperture Radar (InSAR). These approaches can be cost-prohibitive, dependent on specific sensor availability, and computationally intensive. Single-view height estimation offers an agile alternative, but foundational monocular depth models are trained largely on natural egocentric imagery and predict relative depth. When applied to remote sensing, they face domain gaps, structural variations, and a lack of absolute-scale mapping. Converting relative depth into metric elevation remains a critical challenge, alongside the operational need to transform static elevation profiles into interactive 3D assets that can be navigated in real time.

Description

Develop an end-to-end software pipeline that transforms single-view optical RGB remote-sensing images into high-precision elevation maps. The framework must support both non-georeferenced and georeferenced imagery. Non-Georeferenced RGB Imagery (for example, PNG or JPG): Produce a Relative Digital Surface Model (rDSM) for images without spatial metadata. Georeferenced RGB Imagery (for example, GeoTIFF): Produce an Absolute Digital Surface Model (DSM) with metric height values for images containing coordinate-system metadata. The solution should use a pre-trained monocular depth-estimation backbone to generate initial relative-depth maps. For georeferenced imagery, a lower-resolution DEM source such as SRTM or a limited set of Ground Control Points may be used to map scale-agnostic depth features to absolute metric elevations. For non-georeferenced imagery, relative height may be used directly in the visualization stage. After computing the elevation map, the system should project the original optical image onto a generated 3D terrain mesh and integrate the result with a rendering engine such as Unity, Three.js, or Babylon.js. The interface should support seamless first-person navigation and analysis of structural heights and slopes from arbitrary aerial perspectives. Key Milestones Elevation Extraction Use a robust pre-trained monocular depth model to extract geometric and structural representations from single-view optical imagery. Scale Calibration Develop a module that converts relative depth to absolute height using scene-level statistics, low-resolution DEMs, semantic priors, or minimal Ground Control Points for georeferenced inputs. Visualization Layer Build an immersive, preferably interactive, rendering pipeline that converts the optical texture and derived depth map into a navigable 3D environment deployable as a standalone application. Evaluation Criteria DSM Estimation Accuracy and Validation (50%): Evaluate RMSE, MAE, and correlation against LiDAR or reference data, including performance stability across urban, sparse, hilly, and forested landscapes. Visualization Rendering Quality and User Experience (50%): Assess projection accuracy, visual fidelity, navigability of the 3D flythrough, interface intuitiveness, software stability, and successful standalone deployment.

Expected Solution

Deliver a fully integrated software suite with complete source code and technical documentation. The solution must be deployable as a unified module containing the following components: Elevation Estimation Module Accept single-view optical satellite imagery in PNG, JPG, or TIFF format and output a high-fidelity DSM in a standard geospatial format. Interactive Visualization Platform Provide a user-friendly 3D flythrough experience that lets users upload imagery, visualize reconstructed terrain, and validate estimated height values against reference datasets.

Background

The marine ecosystem plays a vital role in supporting livelihoods, food security, biodiversity, maritime transportation, coastal resilience, and the blue economy. Every day, vast volumes of satellite Earth Observation and oceanographic data, including Sea Surface Temperature (SST), chlorophyll concentration, and weather forecasts, are generated by ISRO and other global agencies. Marine stakeholders such as fishermen, researchers, coastal authorities, disaster management agencies, and maritime operators rely on timely access to oceanographic and meteorological information for operational planning and decision-making. As the volume, diversity, and complexity of marine data continue to increase, there is a growing need for an intelligent conversational platform that enables users to interact naturally with marine information, ask questions, explore scenarios, and receive synthesized, evidence-based recommendations tailored to their context. Advances in Agentic AI, conversational intelligence, and geospatial technologies present an opportunity to fundamentally transform how marine information is accessed and utilized. By integrating satellite Earth Observation data with autonomous AI agents, intelligent conversational decision-support systems should be able to answer questions such as 'Where is the nearest Potential Fishing Zone today?', 'Is it safe to venture into the sea tomorrow morning?', or 'What are the tide, weather, and sea conditions near my fishing location?' and provide explainable, context-aware recommendations.

Description

Develop an Agentic AI-powered conversational platform that enables users to access, analyze, and reason over marine information using natural language. The platform should autonomously interpret user intent, decompose complex requests into executable tasks, coordinate multiple specialized AI agents, retrieve relevant marine and geospatial datasets, perform spatial-temporal reasoning, and synthesize actionable recommendations through a conversational interface. The solution should be capable of integrating information from multiple sources, including satellite Earth Observation products, GIS layers, weather services, oceanographic observations, and marine advisories available in the public domain. Typical user queries include Where is the nearest Potential Fishing Zone (PFZ) today? Is it safe to venture into the sea tomorrow morning? What are the tide, weather, and sea conditions near my fishing location? Are there any lightning or cyclone alerts in my area? Which regions show high chlorophyll concentration and favourable sea surface temperature? What is the safest route for a fishing vessel considering weather and sea-state conditions? Why has fish productivity declined in a particular coastal region? Which fishing zones should be avoided due to hazardous marine conditions or geofencing restrictions? The platform should not merely retrieve information from individual datasets but intelligently correlate observations from multiple sources, explain the reasoning behind its recommendations, and present insights through conversational responses, maps, alerts, and interactive geospatial visualizations.

Expected Solution

Participants are expected to develop an Agentic AI-powered Marine Intelligence Platform that leverages collaborative AI agents, geospatial technologies, and satellite Earth Observation data to provide intelligent conversational decision support. The solution should demonstrate the core principles of Agentic AI, including autonomous planning, reasoning, tool selection, task execution, collaboration among specialized agents, and explainable decision-making. The platform should be capable of Understanding user intent expressed in natural language. Automatically identifying the language of the user's query and responding in the same language, with emphasis on supporting Indian regional languages. Supporting contextual, multi-turn conversations that enable users to refine queries and explore related scenarios. Autonomously discovering, retrieving, and integrating relevant satellite, marine, meteorological, and geospatial datasets. Performing spatial, temporal, and contextual reasoning by correlating observations from multiple heterogeneous data sources. Generating explainable, evidence-based recommendations supported by maps, charts, geospatial visualizations, and marine advisories. Enhancing fishermen safety through proactive alerts for adverse weather, high waves, lightning, cyclones, and other hazardous marine conditions. Providing geofencing-based notifications when approaching international maritime boundaries, restricted waters, marine protected areas, ecologically sensitive zones, or other predefined operational boundaries. Assisting with route optimization, safe navigation, and operational planning based on prevailing and forecast marine conditions. Delivering reliable recommendations together with the supporting evidence and reasoning used to derive each response. Participants are encouraged to design a modular multi-agent architecture comprising specialized AI agents for planning, marine data discovery, weather intelligence, ocean analytics, geospatial reasoning, risk assessment, visualization, reporting, and user interaction. The architecture should demonstrate autonomous collaboration among agents to solve complex marine intelligence problems while providing an intuitive conversational experience.

Background

Common challenges faced at border checkpoints:

Current verification methods rely heavily on human inspection and basic database lookups.

Detailed Description

Border checkpoints process thousands of identity documents every day, including passports, visas, national identity cards, permits, and travel authorizations. Manual verification is time-consuming, prone to human error, and often unable to detect sophisticated forgeries, tampering, or identity fraud. Develop an Al-powered document screening platform that automatically analyzes identity and travel documents, detects signs of tampering or forgery, validates information against rules and databases, and generates a risk score to assist border security personnel in making faster and more accurate decisions.

Expected Solution

Module 1: OCR Extraction

Objective

Automatically extract all relevant information from identity documents.

Inputs:

Extracted Fields:

Module 2: Document Validation

Objective

Verify whether the extracted information follows official document standards.

Module 3: Tampering Detection (Core AI Innovation)

Objective

Detect digitally or physically altered documents.

Use Cases:

Module 4: Face Verification

Objective

Ensure document owner matches the presented individual.

Expected Impact

Possible Project Name

Al-Based Fake Identity & Document Screening System.

Background

Law enforcement agencies, courts, legal departments, and investigative organizations handle vast amounts of sensitive documents throughout the lifecycle of a case. These documents may include:

Many organizations still rely on paper-based systems or fragmented digital storage solutions. This often leads to challenges such as:

As the volume of legal and investigation-related data continues to grow, there is an increasing need for a secure, centralized, and intelligent document management system that ensures data integrity, accessibility, confidentiality, and efficient case management. Modern technologies such as Cloud Computing, Artificial Intelligence (AI), Blockchain, Digital Signatures, and Secure Access Control can significantly improve the management and security of legal and investigative documents.

Description

The objective is to develop a Secure Digital Document Management System (DMS) that enables law enforcement agencies, legal institutions, and investigative departments to securely store, organize, manage, retrieve, and share sensitive legal and investigation documents. The system should:

The challenge is to create a secure, scalable, and intelligent platform that streamlines document handling while preserving legal validity and evidentiary integrity.

Expected Solution

Develop a system to monitor and manage police assets throughout their lifecycle.

Provide ideas in a decentralized and distributed ledger technology used to store digital information that powers cryptocurrencies and NFTs and can radically change multiple sectors.