Background

The North Eastern Region (NER) faces major logistics and accessibility challenges due to difficult terrain, extreme weather conditions, limited transport connectivity, and frequent road disruptions caused by landslides, floods, and infrastructure gaps. Transportation of essential goods such as medicines, food supplies, construction materials, and agricultural produce to remote districts often gets delayed, leading to supply shortages, increased costs, and disruption in public service delivery. Currently, there is no integrated intelligent platform that can provide real-time logistics visibility, route accessibility status, predictive disruption alerts, and optimized transportation planning for the region. To strengthen regional connectivity and support infrastructure-led development initiatives, there is a need for an Al-enabled logistics intelligence system tailored for the unique geographical and operational challenges of NER.

Description

This problem statement seeks the development of an Al-powered Smart and in Logistics Accessibility Intelligence Platform for the North Eastern Region (NER) to address challenges related to difficult terrain, weather-induced disruptions, and limited transport connectivity remote areas. The platform should use Artificial Intelligence (Al), Machine Learning (ML) , GIS mapping, weather data, and real-time field inputs to monitor transportation networks and improve movement of essential goods and services across the region. The platform should

Expected Solution

A scalable Al-based software platform integrated with GIS and real-time analytics featuring: Al-powered route prediction and optimization engine GIS-enabled accessibility monitoring dashboard GPS-based vehicle tracking system Real-time alert and notification mechanism Mobile/web application for field-level reporting and monitoring Integration capability with weather APIs, transport databases, and government monitoring systems Cloud-based infrastructure with secure data management and offline support The platform should improve regional logistics efficiency, reduce supply disruptions, strengthen emergency response capability, and support infrastructure and economic development across the North Eastern Region.

Background

MOIL Limited is the largest producer of Manganese Ore in India. To meet future demand, it is important to accurately identify available reserves and avoid production shortfalls. At present, reserve estimation and production planning are mainly based on manual surveys, drilling results, and production records. These methods are time-consuming and sometimes lead to a mismatch between expected and actual ore production.

Detailed Description

The challenge is to develop an AI/ML-based solution that uses geological data,historical production, equipment performance, and satellite/space technology inputs (such as rainfall, soil moisture, vegetation index, and land temperature) to:

Expected Solution

The expected solution is a user-friendly dashboard that shows predicted reserves, production trends, possible risks of shortfall, and recommended corrective steps. This will help MOIL improve planning, reduce losses, and ensure steady ore supply to customers.

Background

KVIC’s Honey Mission supports rural beekeepers with bee boxes and extraction toolkits for livelihood promotion, but they still face challenges like counterfeit honey, low consumer trust, weak market linkages, and lack of traceability and advanced hive management support. Hence, there is a need for an integrated block chain, AI, and IoT-based digital ecosystem to improve honey authenticity, traceability, productivity, and market credibility.

Description

Develop 'Honey Chain,' a block chain-based honey traceability and smart beekeeping system with QR-code consumer verification, secure batch tracking, and AI-IoT features for disease detection, environmental monitoring, and productivity prediction to enhance authenticity, transparency, and market access for rural beekeepers.

Expected Solution

Develop a prototype block chain-based honey traceability and smart beekeeping system with QR-code consumer authentication. Integrate IoT-enabled hive monitoring and AI analytics for disease detection, colony health tracking, and productivity optimization. Create a scalable deployment framework for implementation across rural beekeeping clusters under KVIC and related institutions.

Background

The Indian coal mining sector involves large-scale operations spread across multiple subsidiaries, mine sites, contractors, regulatory bodies, and field offices. Governance-related activities such as statutory compliance monitoring, inspection tracking, safety observations, production reporting, environmental monitoring, worker attendance, contract management, grievance handling, and regulatory reporting are often managed through fragmented systems, manual documentation, spreadsheets, and delayed reporting mechanisms. This leads to challenges such as data inconsistency, delayed decision-making, limited transparency, compliance gaps, duplication of records, weak monitoring of field-level activities, and difficulty in obtaining real-time operational insights. With increasing focus on transparency, accountability, sustainability, and digital governance, there is a need for an integrated smart governance platform specifically designed for the coal mining ecosystem.

Defining the Problem

Develop a centralized AI-enabled governance and compliance monitoring platform for coal mining operations that can digitally integrate mine-level activities, statutory compliance, inspections, contractor management, and operational reporting. The proposed solution should Digitally track statutory compliance requirements related to safety, environment, production, and labour regulations. Enable real-time monitoring of inspections, observations, violations, and corrective actions. Use AI/analytics to identify high-risk areas, recurring compliance failures, and operational anomalies. Provide geo-tagged and time-stamped field reporting through mobile applications. Integrate dashboards for mine officials, corporate management, and regulatory authorities. Generate automated alerts, reminders, compliance reports, and escalation mechanisms. Minimize manual paperwork and improve transparency, accountability, and decision-making. Be scalable for deployment across multiple mines and subsidiaries. Participants may use AI/ML, mobile applications, GIS mapping, OCR/document digitization, workflow automation, blockchain-based audit trails, or multilingual conversational interfaces as part of the solution. The proposed system is expected to Improve governance efficiency and transparency in coal mining operations. Reduce delays and errors in compliance management and reporting. Enable data-driven monitoring and faster administrative decision-making. Strengthen accountability and real-time tracking of field activities. Support digital transformation and paperless governance in the mining sector. Create a scalable indigenous e-governance framework for Indian coal mines.

Expected Solution

The proposed solution should be a centralized AI-enabled smart governance platform for coal mines that integrates compliance monitoring, inspection management, operational reporting, contractor management, and field activity tracking into a single digital ecosystem. The system should provide real-time visibility, automated workflows, and data-driven insights through web and mobile applications to improve transparency, accountability, and decision-making across multiple mining sites and subsidiaries. Centralized dashboard for mine officials, corporate management, and regulatory authorities with real-time compliance and operational monitoring. AI/analytics engine to detect compliance risks, operational anomalies, recurring violations, and generate predictive alerts. Geo-tagged mobile application for field inspections, safety observations, attendance, and incident reporting with offline support. Automated workflow system for alerts, reminders, escalations, digital approvals, and statutory report generation. GIS mapping, OCR-based document digitization, and secure digital audit trails for transparent and paperless governance.

Background

1. 1 The Clinical History-Taking Bottleneck in Indian Hospitals History taking — the structured elicitation of a patient's presenting complaints, history of present illness, past medical and surgical history, drug and allergy history, family and personal history, and a review of systems — is the single most important diagnostic activity in clinical medicine. Classical teaching holds that a well-conducted history yields the correct diagnosis in 70–80% of cases, even before examination or investigation. Yet in India's overburdened public hospital outpatient departments (OPDs), the time available for this critical interaction has collapsed to unsustainable levels. India operates one of the most patient-dense healthcare systems in the world. Tertiary government hospitals and apex institutions routinely register 4,000–10,000 OPD patients per day, with a doctor-to-patient consultation time frequently reported between 2 and 5 minutes — among the shortest globally (study published in BMJ Open, 2017, across 67 countries placed India's average primary-care consultation at just over 2 minutes). Within this window, the physician must simultaneously elicit history, examine the patient, review prior records, formulate a diagnosis, counsel, and prescribe. The result is systematic under-elicitation of history, missed comorbidities, repeated questioning across visits, and diagnostic error. AYUSH institutions face an additional layer of complexity. Ayurvedic history taking (Trividha, Ashtavidha, and Dashavidha Pariksha) requires detailed assessment of Prakriti (constitution), Vikriti (current imbalance), Agni (digestive capacity), Koshtha (bowel nature), Ahara-Vihara (diet and lifestyle), Nidana (causative factors), and Samprapti (pathogenesis) — a far more extensive history framework than allopathic intake. Capturing this depth manually within OPD time constraints is effectively impossible, forcing practitioners to abbreviate the very assessment that defines personalized Ayurvedic care. 1. 2 The Documentation and Records Fragmentation Problem Compounding the time problem is the fragmentation of patient records. Patients in India typically carry physical paper prescriptions, laboratory reports, discharge summaries, and imaging films from multiple prior providers. During consultation, the physician must manually scan through these unstructured documents — often handwritten, in varying languages, and chronologically disordered — consuming a significant fraction of the already-scarce consultation time. There is no point-of-entry mechanism to digitize, structure, and chronologically organize a patient's prior medical documents before they reach the consultation room. The Ayushman Bharat Digital Mission (ABDM) has established the national digital health infrastructure — ABHA (Ayushman Bharat Health Account) IDs, the Health Information Exchange, and FHIR-based interoperability standards. However, the 'first-mile' problem remains unsolved: there is no efficient, patient-facing software platform that captures structured history and digitizes documents into the ABDM ecosystem before the clinical encounter begins. 1. 3 The Opportunity: AI-Powered Digital Clinical Intake Platform Self-service kiosks have transformed high-throughput service industries — ATMs in banking, self-check-in terminals in aviation, and ordering kiosks in quick-service restaurants — by offloading structured data-entry tasks from human staff to the user, dramatically improving throughput and accuracy. In healthcare, patient check-in kiosks are now widespread in developed-country hospitals, but these are limited to administrative check-in. None perform deep, AI-driven, multimodal clinical history acquisition with medical document digitization. The convergence of mature enabling technologies — robust automatic speech recognition (ASR) for Indian languages and accents (Bhashini / AI4Bharat models), large language models for conversational clinical history elicitation, high-accuracy OCR for handwritten and printed medical documents, and ABDM's FHIR interoperability — now makes it feasible to build an AI-powered clinical history software platform.

Description

2. 1 The Problem in Precise Terms There is no purpose-built, patient-facing software platform that enables patients to independently and comprehensively record their medical history — through both natural spoken conversation and guided touchscreen interaction — and simultaneously digitize their existing physical medical documents, generating a structured, physician-ready clinical history summary that integrates with the hospital information system and the ABDM ecosystem before the patient enters the consultation room. 2. 2 Why Existing Solutions Fall Short Existing hospital registration systems (currently deployed in some Indian hospitals) capture only demographic and appointment data — name, age, department, token number. They do not elicit any clinical history or process medical documents. Mobile health apps and tele-triage chatbots require smartphone literacy, stable connectivity, and patient enrolment ahead of the visit — excluding the large elderly, rural, low-literacy, and first-visit patient populations who form the bulk of government hospital OPD load. Manual nurse-led triage / history desks are themselves human-resource-limited, do not scale to 5,000+ daily patients, and reintroduce the same time and transcription bottleneck the system is trying to eliminate. Generic document scanners digitize images but do not extract, structure, or chronologically organize clinical content, nor link it to a structured history or ABHA record. 2. 3 Specific Challenges a Solution Must Overcome Multilingual, multi-accent voice capture in noisy hospital environments across Hindi, English, and major regional languages, for patients of varying literacy and digital comfort. Accessibility for low-literacy and elderly users through intuitive icon-driven UI, audio prompts, and conversational guidance — the software platform must be usable by a first-time, non-tech-savvy patient with zero training. Accurate clinical history structuring converting free-form patient narration into a standardized, physician-readable history (chief complaint, HPI, past history, drug/allergy, family, personal, review of systems) — and, for AYUSH settings, Dashavidha Pariksha parameters. Reliable medical document digitization OCR of handwritten and printed prescriptions, lab reports, and discharge summaries in multiple languages, with intelligent extraction of diagnoses, medications, and investigation values. Privacy, consent, and data security compliance with the Digital Personal Data Protection Act 2023 and ABDM consent framework — handling sensitive health data within a secure software environment.

Expected Solution

3. 1 Solution Overview — 'MediKiosk' AI Clinical History Software Platform The proposed solution — tentatively designated MediKiosk — a software platform for an AI-powered clinical history software platform that allows any patient to record a comprehensive medical history through natural voice conversation and guided touchscreen interaction, scan and digitize their existing physical medical documents, and generate a structured, physician-ready clinical history summary that is pushed to the hospital information system (HIS) and linked to the patient's ABHA record — all completed before the consultation, with minimal staff assistance required. Insert Table*3.2 3. 3 Software & AI Stack (Integrated) Module A — Conversational Multimodal History Engine A conversational AI engine that conducts a structured clinical history interview through both voice and touch. The patient speaks naturally in their preferred language; the engine asks intelligent follow-up questions (e.g., on stating 'chest pain', it probes onset, character, radiation, aggravating/relieving factors — the SOCRATES framework) and simultaneously offers touch-based multiple-choice options for patients who prefer tapping. Built on Indian-language ASR, a dialogue manager constrained by a clinical history ontology, and text-to-speech for audio prompts. Adaptive questioning dynamically branches based on chief complaint and prior answers, mirroring a physician's clinical reasoning to elicit a complete HPI and review of systems Dual-mode input every question answerable by speaking OR tapping, ensuring usability across literacy and comfort levels AYUSH history mode for Ayurvedic OPDs, an extended interview capturing Dashavidha Pariksha (Prakriti, Vikriti, Sara, Samhanana, Pramana, Satmya, Sattva, Ahara Shakti, Vyayama Shakti, Vaya) and Ahara-Vihara assessment Red-flag detection AI flags emergency symptoms (e.g., acute chest pain with dyspnoea, stroke symptoms) and triggers immediate priority alert to triage staff rather than routine queueing Module B — Medical Document Digitization & Intelligence An integrated scanning and document-AI pipeline that allows the patient to upload prior prescriptions, lab reports, and discharge summaries. The system performs high-accuracy OCR (printed and handwritten, multilingual), then extract and structure clinical entities. Intelligent extraction diagnoses, prescribed medications with dosages, investigation results with values and reference ranges, and procedure/surgery history Chronological organization automatically dates and orders documents into a coherent medical timeline for the physician Abnormal-value highlighting flags out-of-range lab values and potential drug interactions for physician attention Module C — Structured History Summary Generator An AI summarization engine that synthesizes the conversational history and the digitized documents into a single, concise, physician-ready clinical summary in standard format — presented on the consultation screen the moment the patient enters the room. The physician reads a complete, structured history in seconds rather than spending minutes eliciting it, and can edit/confirm before saving. Standard clinical format Chief complaint ? HPI ? Past medical/surgical ? Drug & allergy ? Family ? Personal ? ROS ? Prior investigations summary Editable & verifiable: physician retains full control — the summary is a draft to accept, amend, or reject, never an autonomous diagnosis Bilingual output patient-facing audio confirmation in local language; physician-facing summary in English/Hindi Module D — Consent, Privacy & ABDM Integration A robust consent and security layer compliant with the Digital Personal Data Protection Act 2023 and the ABDM consent framework. The patient authenticates via ABHA ID, grants explicit consent for data capture and sharing, and the structured history is pushed to the hospital HIS/EMR and linked to the ABHA Personal Health Record via FHIR APIs. Secure processing voice and document AI are processed securely within the software platform Session termination temporary session data is cleared immediately after submission Consent-first design granular, revocable consent with audio explanation for low-literacy patients 3. 4 End-to-End Patient Journey Step 1 — Identify: Patient logs into the software platform, enters/scans ABHA ID or Aadhaar details or registers as new; selects language; grants consent (audio-guided) Step 2 — Converse: AI conducts adaptive voice + touch history interview, capturing chief complaint, HPI, and full history; red flags trigger priority triage Step 3 — Scan: Patient uploads prior prescriptions, lab reports, and discharge summaries; AI digitizes, structures, and timelines them Step 4 — Summarize & Route: AI generates structured history summary, links to ABHA, pushes to HIS, updates the patient's digital record; summary appears on physician's screen at consultation Step 5 — Consult: Physician reviews complete history in seconds, edits/confirms, and devotes the full consultation to examination, reasoning, and counselling

Background

India's vast Exclusive Economic Zone (EEZ) and coastline demand continuous, high-resolution monitoring of ocean state variables. INCOIS routinely generates and archives large volumes of ocean model outputs including three-dimensional fields of temperature, salinity, current vectors,chlorophyll, etc. as well as real-time and delayed-mode observations from autonomous instruments such as Argo profiling floats and underwater Gliders. These datasets are stored in NetCDF and ASCII/text formats and span multiple depth levels, spatial grids, and time steps.Despite the richness of this data, no integrated, web-based 3D visualization platform currently exists that can simultaneously render model fields and in-situ instrument observations in a single interactive environment. Existing tools are either desktop-bound, support only 2D plan views, or lack the ability to co-visualize model outputs alongside instrument profiles. Operational oceanographers and forecasters are therefore forced to toggle between disparate software packages,making it difficult to rapidly correlate model predictions with observational evidence. Key gaps identified include ? No web-based, platform-independent 3D rendering of ocean model data (temperature,salinity, currents, etc.) with depth-resolved volumetric views. ? No unified display of Argo float and Glider profile data (latitude, longitude, depth, time,temperature, salinity, chlorophyll) alongside model fields. ? Absence of interactive controls for variable selection, depth-slice navigation, time-step animation, and customizable colorbars. ? Inability to ingest new observational data streams or additional model variables without significant re-engineering. ? Lack of tools to support intuitive, rapid understanding of complex 3D ocean phenomena for operational decision-making.The absence of such a system impedes timely hazard assessment, search-and-rescue support,fishery advisories, climate monitoring, etc. all operational mandates of INCOIS. ?

Expected Solution

The proposed solution is a web-based, browser-native 3D Ocean Data Visualization System that integrates ocean model outputs with observational data on a single interactive platform. Core functional requirements ? 3D Volumetric Rendering: Interactive visualization of ocean model fields (temperature,salinity, current vectors) across the full water column, with support for depth-slice views,isosurface extraction, and time-step animation using WebGL / Three.js or Cesium.js. ? Instrument Data Overlay: Co-display of Argo float, Glider profile, CTD and BGC data using geospatially accurate markers; users can click a float/glider to inspect a depth-vs-variable profile chart with timestamps. ? Multi-format Data Ingestion: Automated parsers for NetCDF (via PyNIO / xarray backend)and delimited text formats, with a modular architecture that allows new variables or data sources to be added with minimal code change. ? Customizable Colorbar & Variable Controls: Dynamic colorbar editor (color palette, min/max range, log/linear scale), variable selector, layer opacity controls, and vertical exaggeration slider for intuitive depth perception. ? Web-based, Scalable Architecture: Frontend built on modern JavaScript frameworks with a lightweight REST/OPeNDAP API backend, enabling Deployable on INCOIS infrastructure without any client-side dependencies. ? Extensible Design: Plugin-style module for future integration of additional sensors (e.g.,CTDs, moorings, HF-radar, Acoustic doppler current profiler (ADCP), etc.), new ocean model variables, and machine-learning derived products. The system will follow open standards (OGC WMS/WCS, CF Conventions for NetCDF), enabling interoperability with national and international ocean data portals. The end product will empower INCOIS forecasters to perform rapid, intuitive analysis of complex 3D ocean phenomena significantly improving the speed and accuracy of operational advisories, in the same way that 3D meteorological visualization has transformed weather forecasting workflows.Public Outreach & Science Communication: Beyond operational use, the platform will serve as a powerful science communication tool. Complex numerical ocean model outputs which are typically inaccessible to non-specialists can be transformed into visually intuitive, interactive 3D experiences. This makes the tool valuable for educating school and college students about ocean dynamics, engaging the general public during awareness campaigns, and supporting policymakers in understanding marine environmental conditions. INCOIS can use the platform for outreach events, exhibitions, and e-learning initiatives, bridging the gap between cutting-edge ocean science and the common person. Insert 2 tables(Acronyms and Dataset Link) here-

Background

Labour Cooperative Federations and Labour Cooperative Societies possess a large pool of skilled workers such as electricians, plumbers, carpenters, painters, domestic helpers, caregivers, drivers, gardeners,cleaners, and technicians. However, they lack a structured digital platform to connect these workers with households and institutions requiring such services.Private platforms currently dominate this market, while cooperative workers often remain underutilized despite having skills and local presence.

Problem Statement

To develop a cooperative-owned digital service marketplace platform that enables Labour Cooperative Federations and Labour Cooperative Societies to provide verified household and community services while ensuring fair wages, worker welfare, and consumer trust.

Expected Solution Features

Service provider registration and verification Worker skill profiling and certification Customer booking and scheduling system Geo-location based service matching Digital payments and invoicing Rating and feedback mechanism Worker welfare and insurance integration Emergency and on-demand service booking Cooperative federation administration dashboard Multilingual mobile application AI-based demand forecasting and workforce allocation

Technology Components

Mobile Applications Artificial Intelligence (AI) Geo-Spatial Technology Digital Payment Systems Cloud Computing

Proposed Mode

Software

Background

Victims and complainants belonging to Scheduled Castes and Scheduled Tribes who approach the National Helpline Against Atrocities (14566), Integrated Portal, chatbot, mobile application, IVRS, or other digital platforms often experience severe emotional distress arising from caste-based discrimination, violence, rape, gang rape, murder of family members, social boycott, displacement, threats, and prolonged legal proceedings.

Presently, there is no standardized mechanism for assessing the psychological condition and vulnerability of victims at the time of first contact with authorities.

Problem Statement

Design and develop an AI-enabled Real-Time Stress and Trauma Assessment Module that can assess the psychological stress, trauma, fear, anxiety, and vulnerability levels of victims/complainants interacting through NHAA (14566), the Integrated Portal, chatbot,IVRS, mobile application, or any other approved digital interface.

Expected Solution

The solution should Analyse voice interactions, speech patterns, pauses, pitch variation, emotional indicators, and textual narratives.

Expected Outcomes

Stakeholders

Background

Central Public Sector Enterprises (CPSEs) operating in sectors such as Oil & Gas, Power, Steel, Mining and Heavy Engineering procure and maintain a large number of similar or functionally equivalent materials.

However, the same material may be assigned different material codes, descriptions, specifications, units of measurement and classification across different CPSEs.

This results in duplication of material masters, inconsistent descriptions, difficulty in identifying equivalent materials, fragmented procurement data, higher inventory levels and limited opportunities for collaborative procurement.

A unified and intelligent approach is therefore required to standardize, harmonize and rationalize material master data across CPSEs.

Description

The proposed solution envisages development of an AI-powered National Unified Material Master Framework capable of analysing material codes, descriptions, specifications, technical parameters and historical procurement data from multiple CPSEs.

The system shall use Artificial Intelligence, Machine Learning and Natural Language Processing (NLP) techniques to identify identical, duplicate, near-duplicate and functionally equivalent materials across different ERP/SAP systems.

The platform should automatically recommend standardized material descriptions, specifications, classifications and a Common National Material Code, while retaining mapping with the respective CPSE's existing material codes.

The system should provide intelligent matching and recommendation capabilities, allowing users to review, validate and approve proposed mappings.

It should also support migration/mapping of legacy material codes and seamless integration with existing SAP/ERP systems.

Expected Solution

An AI-driven Unified Material Master Platform shall be developed with the following capabilities: AI-based matching of material descriptions and specifications across CPSEs.

Key Capabilities

Expected Impact

Background

Government procurement through the Government e-Marketplace (GeM) involves verification of multiple statutory, regulatory and eligibility requirements of bidders.

Procurement officers are required to examine and validate documents and information related to Udyam/MSME registration, GST registration and return filing, PAN and Income Tax compliance, Make in India/local content, EPFO/ESIC compliance, Startup India, NSIC, OEM authorization, DigiLocker, blacklisting/debarment and other applicable statutory requirements.

The verification process is largely document-intensive and requires cross-checking information across multiple government portals and databases.

This results in significant manual effort, longer tender evaluation time and the possibility of inconsistencies or human errors.

Description

The problem statement envisages development of an AI-powered integrated bid compliance verification platform that can automatically verify the eligibility and compliance status of bidders participating in GeM procurement.

The proposed platform shall integrate with relevant Government portals and databases and retrieve/verify bidder information such as Udyam Registration, GSTN, Income Tax, PAN, MCA21, Startup India, NSIC, EPFO, ESIC, DigiLocker, Make in India, BIS/DPIIT and other applicable sources.

An AI Verification Engine shall analyse the submitted bidder documents and portal-derived information, identify missing or inconsistent information, validate applicable compliance requirements and generate an overall compliance assessment.

The system shall provide a Compliance Dashboard displaying the compliance score, risk level, document verification status, pending requirements and AI-generated recommendations.

The final decision regarding qualification/disqualification shall remain with the Procurement Officer.

Expected Solution

An AI-enabled integrated platform shall be developed for automated verification of bidder compliance in GeM procurement. The solution shall

Key Capabilities

Expected Impact

Background

The Infrastructure & Project Monitoring Division (IPMD), Ministry of Statistics and Programme Implementation (MoSPI) monitors the Central Sector Infrastructure Projects costing ₹150 crore and above, across all the infrastructural Ministries/ Departments. The project monitoring was undertaken through the Online Computerised Monitoring System (OCMS) since 2006, which served as the primary repository of project-level information relating to project cost, expenditure, timelines and implementation status. Over nearly two decades, OCMS generated a valuable historical database capturing project implementation trends, cost overruns and time overruns across sectors. Later, OCMS was modernized to Project Assessment, Infrastructure Monitoring and Analytics for Nation-building (PAIMANA) portal, to enable a comprehensive and integrated project-monitoring ecosystem.

PAIMANA Portal and Data Ecosystem

PAIMANA is a web-based integrated project-monitoring platform designed to function as a national repository of infrastructure projects. It captures project-level information relating to approved cost, revised cost, expenditure, implementation timelines, physical progress, milestones, implementing agencies and project status. The information on infrastructure projects is updated on a monthly basis, through role-based access and APIs. As of April 2026 , the PAIMANA project-monitoring framework tracks 1,981 ongoing infrastructure projects across 17 Central Ministries/Departments covering 22 infrastructure sectors

AI Opportunity from PAIMANA Database

The historical project-monitoring database available through OCMS combined with the recent PAIMANA portal provides a unique and comprehensive repository of infrastructure project data spanning nearly two decades. The database encompasses projects of varying sizes, sectors, geographical locations, implementing agencies, expenditure patterns and implementation timelines. The availability of large-scale historical repository of project data together with continuously updated project information received through integrated digital systems provides a strong foundation for the application of Artificial Intelligence (AI), Machine Learning (ML) and Large Language Models (LLMs). These technologies can be leveraged to develop predictive analytics and early warning decision support systems for identifying cost overruns, schedule delays and implementation risks, thereby enabling proactive interventions and evidence-based decision-making in infrastructure project monitoring.

Problem Statement and Scope of Work for Hackathon

Under the broader theme of 'AI for Infrastructure Monitoring', the proposed use-case seeks to develop an AI-powered Predictive Analytics and Early Warning System capable of analysing the large volume of project data available at PAIMANA portal, using Open-Source Tools and Softwares, to identify projects that are likely to experience cost escalation, schedule delays and implementation risks before such issues materialise. The solution should assist policymakers, project administrators and monitoring agencies in prioritising interventions, improving project execution outcomes and enhancing the effectiveness of infrastructure project monitoring. The use-case aims to transform project monitoring from a descriptive reporting framework into a predictive and prescriptive decision-support system capable of generating actionable insights for evidence-based decision-making. In this regard, the proposed solution may address the following technical dimensions:

Possible Expected Outcomes and Evaluation

An indicative solution proposed by the student should comprise of any of the outcomes given below: a. Cost Overrun Prediction Model; b. Time Overrun Prediction Model; c. Project Risk Scoring Framework; d. Early Warning Alert System; e. Benchmarking and Comparative Analytics Module; f. Cost Escalation Driver Analysis Module; g. AI-powered Monitoring Dashboard; h. LLM-enabled Project Intelligence Assistant;

Background

The Bureau of Indian Standards publishes thousands of Indian Standards and provides various services such as product certification, hallmarking, laboratory recognition, Standards Clubs, training, consumer affairs, and conformity assessment.

At present, users often struggle to identify: Applicable Indian Standards for their products, Certification requirements, Relevant BIS schemes, Licensing procedures, Testing requirements, Related standards, and Answers to technical queries.

Searching through multiple documents, portals, and PDFs is time-consuming, particularly MSMEs, startups, students, and consumers.

Description

Develop an Al-powered conversational assistant that enables users to obtain accurate, context-aware, and source-backed information related to Indian Standards and BIS services through natural language interactions.

The assistant should understand user queries in plain language, retrieve relevant information from authorized BIS knowledge sources, and provide responses with references to the documents or clauses which ever are applicable.

Expected Solution

The software solution consists of a Intelligent Assistant or Agent which can Answer questions related to Indian Standards.

Background

Government departments, Public Sector Enterprises (PSES),procurement agencies, and private organizations procure a wide range of products and services through e-procurement portals.

Procurement officials are often required to prepare technical specifications that reference the appropriate Indian Standards (IS).However, identifying the correct standard(s) is challenging due to the large number of published standards, overlapping scopes, frequent revisions, and the need to consider associated or normative reference standards.

Consequently, tender specifications may omit relevant standards, reference outdated versions, or include incomplete technical requirements, leading to ambiguity, reduced product quality, and procurement disputes.

An intelligent system is required that can automatically analyze a product description or technical specification and recommend the most relevant Indian Standard(s), along with allied, cross-referenced, or normative standards that should also be considered.

Description

Develop an Al-powered recommendation engine that integrates with procurement portals and assists procurement officials in identifying the most relevant Indian Standards and related standards while preparing tender specifications.

Expected Features

Accept product descriptions, technical specifications, or tender documents as input.

Recommend the most relevant Indian Standard(s) based on semantic understanding rather than keyword matching.

Identify allied standards, including normative references, test methods, terminology standards, safety standards, installation standards, and related product standards.

Highlight the latest published version and amendments of the recommended standards.

Suggest mandatory certification requirements, where applicable (e.g., BIS Product Certification, CRS, Hallmarking).

Support multilingual input and natural language queries.

Description

Students are tasked with designing a Smart City using Forma Site Design for effective site design for adding details to the buildings in the site.

The Site area selected must have minimum 1 km square area The goal is to design and develop a site including the contextual data (available for free within Forma Site Design) and export Site BIM model suitable for further use in Revit app.

The Site designed must include Site Limits, Landscaping, Buildings, and Transportation elements.

The Site designed must be analyzed using the Analyze functions available within Forma Site Design for Area Metrics, Embodied Carbon, Sun hours, Daylight potential, Wind Analysis, Microclimate analysis, Noise analysis, Solar Energy.

Minimum 2 Site Design proposals must be compared and presented using the Forma Borad available within Forma Site Design.

Objective

Urban populations continue to grow rapidly, creating the need for smarter, more sustainable, and resilient cities.

Planners, architects, and engineers must leverage data-driven design tools to evaluate site performance, optimize land use, and improve the quality of life for future residents.In this project, students are tasked with designing a Smart City Development using Autodesk Forma Site Design for site planning and analysis, and Autodesk Revit for detailed building development.

Participation Guidelines For Idea Submission Each student team should submit Forma Site Design with 2 proposals that are compared for their and a Forma Site Design PowerPoint presentation (5-7 Slides).

Models should be created using only Forma Site Design and not copied or taken from any other source.

AI Generated content is NOT ALLOWED.

For Grand Finale Students must use Forma Site Design to design, and create 3D Model of specific office building within the given time period and present the following to the jury members: PPT explaining the final Site Design proposal Detailed and synced drawing block of a building exported, edited in Revit app and then synced to Forma Site Design.

Presentation of the Proposal Comparison done through Forma Board Rendered images and Walkthrough video (30 secs.) of the final Model.

Note Teams coming with pre-designed files will be disqualified.

Attach Marking Criteria Table here* Faculty (SIH SPOC) Form Teams choosing to submit idea for Autodesk’s problem statement are required to request their faculty (SIH SPOC) to fill this 'Mandatory Form'.

Autodesk Forma Site Design Autodesk Forma Site Design is a complete Site Design and Site Analysis web app built on native Forma AI HUB primarily used by architects, engineers, and construction professionals to design, model, and document the site and present proposal for approvals.

Educators and Students can download 'Autodesk Forma Site Design'.

Essential workflow and learning access Click Here to get FREE access to Forma Site Design.

Background

Refineries, PSUs, defence-linked manufacturing units and government offices generate a lot of routine but sensitive knowledge work. Approval notes, board presentations, engineering calculations, code for internal tools, review of scanned drawings and inspection reports. None of this can go through cloud AI assistants like Claude or Codex because the underlying data is confidential: Piping & Instrument Diagrams, financials, vendor negotiations, unreleased designs, internal correspondence, confidential business strategies etc. Company policy keeps this data on premises, so people either do the work manually resulting in productivity gain, or they quietly paste confidential material into public tools anyway. Open weight large reasoning models have reached a point where a genuinely useful assistant built on them is realistic. But nothing deployable exists today that industrial users can actually work with the way they use Claude or Codex.

Description

The idea is a self-hosted, air gapped AI workbench running entirely on the organization's own GPU server. Nothing leaves the premises. The backend should not be locked to one model. It needs to support multiple open weight models at once and automatically pick the right one for a given task based on what that task needs, a coding request handled differently from a document summary request. New open weight models should be addable later without redesigning the system, since this space is moving fast. The assistant also needs to actually act like an agent. Plan out multi step work, call local tools such as file read and write, code execution in a sandbox, spreadsheet work, internal document search, and iterate on a task instead of answering once and stopping. It needs to handle more than text too scanned PDFs, handwritten notes, engineering drawings, photographs, read through on device OCR and vision models. Output should be real deliverables, approval notes, PPT/Word/Excel files, working code, calculations with steps shown, not just chat replies. And it needs to ground itself in the organization's own manuals, SOPs and past correspondence through a local knowledge base connector, again with nothing going external.

Expected Solution

A working local deployment, demonstrable on a single workstation or server with a mid range GPU (use a smaller open weight model if 120B class hardware isn't available at the venue), that shows model auto selection across at least two different task types. An agentic task carried through end to end, for example reading a scanned inspection report, pulling out key findings and drafting an approval note as a Word file. A coding task run and verified in a sandbox. A multimodal task involving image or scanned document understanding. The system should also show, through logs or a visible network monitor, that no external calls are made at any point. That's the actual proof of the sovereign claim, not just a statement of it.

Background

Baghewala Field in Rajasthan produces heavy crude oil (17–19° API) from the Jodhpur Sandstone reservoir. The reservoir is characterized by High crude viscosity, High asphaltene content, Low reservoir pressure, Low reservoir temperature (46–48°C) and Poor oil mobility under primary recovery. Consequently, artificial lift and thermal enhanced oil recovery are critical for sustained production. At present, CSS cycle design and SRP operation are optimized separately using historical experience. As reservoir temperature declines after steam injection, crude viscosity increases, leading to reduced pump efficiency, higher energy consumption, rod floating issues, rod failures and lower oil recovery. There is a need for an integrated, data-driven system that continuously optimizes both CSS and artificial lift operations.

Problem Description

Current operations face the following challenges CSS parameters (steam volume, injection pressure, soak time and production cut-off) are largely based on historical practices. SRP operating parameters (stroke length, SPM and VFD settings) are adjusted manually and reactively. Heavy crude causes rod floating, impact loading, frequent pump unsetting, rod failures and increased maintenance. Reservoir behaviour, wellbore conditions and SRP performance are not optimized together. Lack of predictive analytics results in higher Steam-Oil Ratio (SOR), increased energy consumption and reduced production efficiency.

Expected Outcome / Solution

Develop an AI-enabled Well-to-Surface Digital Twin that integrates reservoir, wellbore and surface production systems to provide real-time monitoring, prediction and optimization. The solution should Optimize CSS cycle parameters. Predict reservoir heating, cooling and production performance. Continuously optimize SRP operation by adjusting stroke speed and SPM based on well conditions. Detect rod floating and minimize impact loading. Improve pump efficiency and equipment reliability. Optimize steam and energy consumption while reducing operating cost.

Expected Benefits

Increased oil production and recovery. Reduced Steam-Oil Ratio (SOR). Lower energy consumption per barrel. Reduced rod failures and pump unsetting. Improved equipment life and operational reliability. Data-driven and predictive decision making.

Relevant Data Availability

The field has sufficient historical and operational data, including: Production history CSS cycle records Steam injection parameters VFD and SRP operating data Rod failure and pump unsetting history Well completion and reservoir data Fluid properties and pressure data

Background

Oil India Limited has a digital real-time monitoring system (eRTMAC) that provides real-time drilling data, mud logging information, and wellsite analytics across operational areas. However, drilling decisions, particularly in geologically complex formations, require not only real-time data from the active well but also insights from nearby and historical wells drilled in the same reservoir or formation. Historical drilling knowledge currently resides across numerous well completion reports, drilling reports, PDF documents, and individual experience, making retrieval time-consuming and dependent on individual experience & memory. This often results in delays in decision-making and missed opportunities to proactively mitigate drilling risks.

Problem Description

Currently, drilling teams do not have a unified platform that can:

Expected Outcome / Solution

Develop an AI/ML-enabled Nearby Wells Intelligence System (NWIS) that acts as a standalone decision-support platform alongside eRTMAC that has institutional memory. The solution should

Relevant Data Availability

(if any) Potential data sources available within OIL may include:

Background

Infrastructure project schedules cascade from macro milestones (L1) down to micro, executable activities (L5/L6),spanning multiple engineering disciplines civil, piping, static/rotating equipment, electrical, instrumentation, HSE- each executing and reporting in parallel. While the baseline plan is well-structured (Primavera/MS Project), actual execution data flows back through daily progress reports, site diaries, discipline-wise spreadsheets, and verbal supervisor updates, each in its own format and cadence, largely disconnected from the L5/L6 activity IDs in the plan.

Problem Description

There is no reliable, low-friction mechanism to capture actual start/end times of L5/L6 activities across disciplines and auto-link them back to the plan. Input quality varies with manpower skill, reporting discipline, and format.Field execution is often more granular than the planned WBS, and different disciplines describe the same physical progress differently (e.g., 'spool erected' vs. the plan's 'Erect Line 24?-XX').Consequently:

Expected Outcome/Solution

Relevant Data Availability

Anonymized/ sample daily progress report formats, sample L5/L6 schedule extracts, and illustrative discipline-wise (civil/ piping/ electrical) site-diary or spreadsheet templates can be shared under NDA with Institute/ Authorised person. Live project data will not be shared; teams should work with synthetic/sample data of similar structure.

Problem Description

Government departments operate multiple portals, mobile applications,registries, workflow systems and databases that have often been developed independently. Differences in data formats, identifiers, authentication methods, APIs, process definitions and ownership structures can prevent seamless information exchange.Citizens and businesses may be required to submit the same information repeatedly, track applications across different portals, or visit multiple offices.Officials may lack a consolidated view of beneficiaries, applications, approvals,grievances and service outcomes. The challenge is to enable secure,standards-based interoperability without requiring complete replacement of existing systems.

Expected Solution

Outcome An interoperability framework,middleware layer or federated service delivery architecture that supports API based exchange, common data standards, master-data management, consent-based data sharing, single sign on or federated identity, event-driven notifications, unified application tracking and configurable workflow orchestration.The solution should provide reusable connectors for legacy and modern systems, audit logs, role-based access, data-quality checks, exception handling and monitoring dashboards.

Expected Outcomes include fewer duplicate submissions, reduced processing time,consistent records, improved citizen experience, better cross-department coordination, and measurable improvement in service-level compliance.

Problem Description

Entrepreneurs and industrial units may need to obtain multiple registrations,permissions, licences, no-objection certificates, inspections and renewals from different authorities. Requirements may vary by sector, location, project size and stage of operation. Applicants may find it difficult to identify applicable approvals, understand documentation requirements, monitor timelines,respond to queries and access incentives or support schemes. Departments may face incomplete applications, repetitive scrutiny, manual coordination, limited visibility of bottlenecks and inconsistent compliance monitoring. The challenge is to simplify and accelerate the end-to-end journey while maintaining statutory safeguards.

Expected Solution

Outcome A unified, intelligent approval and compliance management solution that can generate a customised approval checklist, guide applicants through documentation, pre-validate submissions, reuse verified data,coordinate parallel departmental workflows, schedule inspections, track service-level timelines, issue alerts, and provide a single dashboard for applications, approvals, renewals and incentives. It may include a regulatory knowledge engine, risk-based scrutiny,common inspection planning, grievance escalation and analytics for identifying delays.

Expected Outcomes include reduced approval time, fewer incomplete applications, improved transparency,lower compliance cost, better utilisation of government schemes and stronger ease of doing business.

Problem Description

Government departments often face operational problems that could benefit from innovative startup solutions, but conventional procurement processes are generally designed for standardised goods and established vendors. Departments may find it difficult to formulate outcome-based problem statements, discover suitable startups, evaluate novel technologies, structure controlled pilots, manage intellectual property and data, measure pilot results, and transition successful pilots into compliant procurement or scale-up.Startups may struggle with prior-turnover or experience requirements, long sales cycles, unclear payment milestones and limited visibility of departmental demand. The challenge is to create a transparent, competitive and legally compliant innovation-procurement pathway.

Expected Solution

Outcome A structured end-to-end mechanism for challenge identification, startup discovery, eligibility screening, expert evaluation, sandbox or pilot design,milestone-based contracting,performance measurement, payment,independent validation and scale-up decisions. The mechanism should provide standard templates for problem statements, evaluation criteria, pilot agreements, data/IP clauses, cybersecurity, risk management and procurement pathways. It may integrate with recognised startup databases and government e-marketplaces.

Expected Outcomes include faster discovery and testing of innovative solutions, higher quality pilots, reduced departmental risk, timely startup payments, evidence-based procurement decisions and successful scaling across departments or districts.

Background

Organisations frequently need to convert information available in different forms such as news articles, reports, advisories, threat intelligence, policy documents, research papers, announcements, incident reports or free-form prompts into specific communication artefacts suitable for various purposes. The process of manually analysing the source content, understanding the desired objective and creating the required output format is time-consuming, resource-intensive and often requires expertise in content creation, communication and domain knowledge. There is a need for an intelligent platform that can transform user-provided content into a desired output format through a simple and configurable interface.

Description

The system shall act as an AI-powered content transformation engine that converts a common source of information into the specific deliverable requested by the operator, thereby reducing manual effort, improving consistency, accelerating content creation and enhancing operational efficiency. The platform shall provide a dashboard through which an operator can submit source content in the form of high quality English language text, documents, articles, reports, prompts, images, videos or contextual information. In addition to providing the source content, the operator shall select one or more desired output types through configurable parameters available on the dashboard. Based on the submitted content and the selected output type(s), the platform shall analyze the input, understand the context and intent, and generate the requested output artefact. The platform should support multiple output formats and allow operators to control generation parameters such as target audience, tone, language, level of detail, communication objective and content style. In summary, platform shall generate output corresponding to the option(s) selected by the operator on the dashboard. Examples include If 'Video' is selected, generate a complete video package including script, storyboard, scene descriptions, narration text, subtitles and visual recommendations. If 'LinkedIn Post' is selected, generate a professional LinkedIn post suitable for publication. If 'Twitter/X Post' is selected, generate platform-optimized tweets or tweet threads. If 'Advisory' is selected, generate a structured advisory document. If 'Infographic' is selected, generate infographic content, layout recommendations and key messaging. If 'Executive Summary' is selected, generate a concise executive briefing. If 'Presentation' is selected, generate presentation slides and speaker notes. If multiple output formats are selected, generate all selected deliverables from the same source content.

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

In high-reliability sectors (like space) electronic components undergo rigorous environmental stress screening (ESS), including Burn-In testing (operating components at elevated temperatures, e.g., 125°C for extended periods). Traditional screening relies on static parametric pass/fail limits. However, 'latent defects'—components that pass the absolute limits but exhibit subtle, anomalous drift over time—often escape into final payloads, leading to catastrophic field failures.

Description

Development of a predictive machine learning model that analyzes time-series parametric data (e.g., standby current Iddq, leakage currents, or propagation delays measured at intervals like 0h, 24h, 96h, and 168h to detect anomalous components.

Expected Solution

Module A The outlier detection system Static limits catch obvious failures. Participants need to develop a 'Dynamic' outlier detection system. If a lot has an average leakage current of 10µA, a part showing 45 µA is a massive anomaly, even if the absolute datasheet maximum limit is 50 µA.

Module B

Time-Series Drift Predictor Build a predictive regression model that takes Value_0h and Value_24h as inputs and forecasts Value_168h. If the predicted 168h drift rate exceeds a calculated safety slope, the system flags the component for early rejection.

Evaluation Metrics

Anomaly Detection Score a False Negative (missing a defective part) is catastrophic, penalizing teams that let bad parts escape. Drift Prediction Accuracy The mean absolute error between the predicted Value_168h and the actual hidden ground-truth values. Explainability Can the model justify its classification to a QA inspector, or is it a complete black box?

Background

Border security forces deploy CCTV cameras at Border Out Posts(BOPs), check posts, border roads, and other strategic locations for surveillance and monitoring. However, conventional CCTV systems primarily provide video recording and live monitoring capabilities, requiring continuous human observation. Advanced surveillance functionalities such as Facial Recognition Systems (FRS), Automatic Number Plate Recognition (ANPR), intrusion detection, and object tracking often require specialized hardware and proprietary solutions, making large-scale deployment costly and difficult, particularly in remote border areas.

Description

The proposed solution aims to develop an AI-driven software platform capable of transforming existing CCTV infrastructure into an intelligent surveillance network without requiring dedicated FRS, ANPR, or smart-camera hardware. The platform shall ingest live video streams from standard IP-based CCTV cameras and perform real-time video analytics using Artificial Intelligence and Computer Vision techniques. The solution should provide capabilities such as:

Expected Solution

The proposed system should leverage Artificial Intelligence, Machine Learning, Computer Vision, and Video Analytics to create a software-defined surveillance platform capable of extracting actionable intelligence from existing CCTV infrastructure. The solution should:

The final solution should be cost-effective, scalable, and suitable for deployment across remote border locations and strategic installations.

Possible Project Name

IBVAP – Intelligent Border Video Analytics Platform