• 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 . These projects have an aggregate original cost of approximately ?37.13 lakh crore, revised cost of approximately ?42.78 lakh crore and cumulative expenditure of approximately ?20.36 lakh crore. The monitored portfolio covers major sectors including Transport & Logistics, Energy, Water & Sanitation, Communication, Social Infrastructure, Coal, Steel and Mining.
Despite the availability of comprehensive project-monitoring data, infrastructure projects frequently encounter challenges such as cost overruns, time overruns, delays in milestone achievement, contractual and implementation bottlenecks, resource constraints and execution risks. These challenges often result in significant escalation of project costs and delays in the creation of public infrastructure assets.
While the existing PAIMANA framework provides robust capabilities for monitoring and reporting project progress, there is a growing need to move beyond descriptive monitoring towards predictive and prescriptive monitoring. The scale, diversity and continuous availability of project data provide an opportunity to strengthen infrastructure project monitoring through data-driven analytical and decision-support systems.
• 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:
a)Development and evaluation of statistical analysis and predictive models using open-source tools and methodologies for analysing project performance and forecasting cost overruns, time overruns and implementation risks.
b)Assessment of whether Artificial Intelligence (AI) and Machine Learning (ML) techniques provide significant gains over conventional statistical methods in terms of prediction accuracy, early warning capabilities and decision-support for infrastructure project monitoring.
c)Development of prediction and analytical models based on the existing Common Upload Form (CUF) fields available in the project-monitoring framework, along with an assessment of the extent to which predictive performance is attributable to the current CUF fields vis-Ã -vis additional variables that are not presently captured in the CUF.
It is suggested/ desirable that the proposed solution may leverage any of the following (using Open-Source Tools/ Softwares); (a) Artificial Intelligence (AI), (b) Machine Learning (ML), (c) Big Data Analytics, (d) Forecast Modelling and (e) Large Language Models (LLMs); to predict cost and time overruns, generate project-level risk scores, identify emerging implementation challenges, and provide early warning signals and decision-support mechanisms for timely interventions. However, these suggested techniques are only indicative and non-exhaustive. The students may adopt alternative or additional methodologies, tools, frameworks, or analytical approaches, as deemed appropriate, to achieve the stated objectives.
• 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;
i. Documentation and deployment framework.
The above outcomes are indicative and non-exhaustive. Students may propose alternative outputs, features or solution components that effectively address the problem statement. It is desirable that only open-source tools/ software be used to achieve the stated objectives. The selection and application of such methods shall remain at the discretion of the student, subject to demonstrating their suitability, effectiveness, and alignment with the project requirements.