Signal Detection from Global Adverse Event Databases
Challenge: A US-based client needed to detect safety signals from massive databases such as FAERS, VAERS, and EMA. Existing market-leading software products were slow and often missed critical signals, limiting confidence in regulatory submissions.
Solution: We developed state-of-the-art algorithms that combined statistical rigor with AI-based anomaly detection. The system achieved far greater sensitivity and specificity than leading commercial tools while maintaining speed and scalability.
Outcome: The client was able to detect critical signals more reliably, securing large contracts from global MNCs and earning recognition from the World Health Organization (WHO) for their advanced pharmacovigilance practices.
Predicting Adverse Events in Drug Design
Challenge: A leading US-based client wanted to improve drug development by predicting adverse events earlier in the pipeline. Traditional methods identified risks only during trials, leading to costly redesigns and delays.
Solution: We developed an AI-driven predictive model that simulated potential adverse events during drug design. Beyond simple predictions, the system highlighted specific molecular structures likely to trigger adverse effects, enabling early redesign.
Outcome: The model helped the client save significant time and money by reducing failed trial rates and accelerating go-to-market timelines. This early risk detection also gave them a competitive advantage in innovation-driven markets.
AI Literature Mining for Signal Validation
Challenge: Pharma safety scientists often spent 3–4 weeks evaluating whether a signal was real, manually reviewing literature and relying on human judgment. This created long delays in safety decision-making.
Solution: We designed an AI-based solution that automatically scanned medical literature databases, retrieved relevant studies, and extracted causal sentences most likely to influence safety decisions. By blending NLP with domain ontologies, it mimicked expert reasoning.
Outcome: The system reduced decision-making time from weeks to just hours with an accuracy of 87%. This allowed safety scientists to focus on strategic insights while improving compliance with regulatory timelines.
Manufacturing & Supply Chain Excellence
Safety Stock Optimization for a Newspaper Giant
Challenge: India’s largest newspaper publisher struggled with excessive safety stock that locked up working capital. Rule-of-thumb inventory norms led to inflated costs across its nationwide supply chain.
Solution: We modeled demand and lead-time using log-normal distributions and Monte Carlo simulations, replacing intuition-driven buffers with data-driven optimization.
Outcome: Safety stock was reduced by 17%, delivering quarterly savings of ₹7.7 crore without stockouts. The optimization improved liquidity and allowed reinvestment into digital initiatives.
Steel Price Forecasting for Mega Projects
Challenge: A global engineering consultancy faced major cost overruns in ₹45,000+ crore infrastructure projects due to unpredictable steel prices.
Solution: We built hybrid models combining ARIMA and simultaneous equation modeling of input drivers like iron ore and coal. The system generated baseline forecasts and scenario simulations.
Outcome: Forecasts became significantly more accurate, improving cost estimates and reducing risk premiums. This strengthened the consultancy’s credibility and competitive bidding capability.
Telecom Demand Forecasting
Challenge: A leading telecom company faced frequent supply chain disruptions due to poor demand forecasts.
Solution: We developed predictive simulations using decision trees and Monte Carlo methods to capture variability in demand drivers.
Outcome: Forecast accuracy improved by 35%, reducing supply chain costs by 10% and improving product availability.
Loom Width Optimization for Textile Manufacturer
Challenge: A textile manufacturer faced 17% cutting loss in weaving processes, leading to significant cost inefficiencies.
Solution: We applied a modified knapsack optimization algorithm to redesign cutting patterns and minimize wastage.
Outcome: Cutting loss dropped to 10.5%, unlocking major cost savings and improving competitiveness.
Financial Services & Risk Analytics
Credit Risk Prediction for Microfinance
Challenge: A microfinance company was struggling with rising defaults, threatening portfolio health.
Solution: We tested multiple predictive models and found logistic regression offered the best predictive power for repayment behavior.
Outcome: The model achieved 97% accuracy in predictions, helping the client reduce delinquency rates and strengthen portfolio quality.
Health Insurance Fraud Detection
Challenge: A leading health insurer was incurring significant losses due to fraudulent claims, many of which bypassed existing rule-based checks.
Solution: We built a hybrid AI model using recursive partitioning and Naïve Bayes applied to both structured and unstructured claims data.
Outcome: The system detected 11 of 12 fraudulent claims in test data with 97% accuracy, substantially reducing financial losses.
Public Health & Social Impact
Malaria Alert System for Public Health
Challenge: : A state government needed an early-warning system to predict malaria outbreaks.
Solution: We combined weather and historical health data to model outbreak likelihoods and built a heatmap-based alert app.
Outcome: A district-level alert system was successfully deployed, with plans for state-wide expansion.
Vulnerability Index for Climate & Health Risks
Challenge: Policymakers required a composite measure of vulnerability to climate and health risks.
Solution: We developed a PCA-based methodology to build a scalable vulnerability index and deployed it through an interactive web application.
Outcome: The index was adopted at the state level and extended nationally, enabling evidence-based planning.
Respiratory Illness Prediction in New Delhi
Challenge: Rising air pollution in Delhi was driving an increase in respiratory illnesses, overwhelming public health systems.
Solution: We used Generalized Additive Models with pollution and weather data to predict spikes in respiratory illnesses, integrated into a real-time web app.
Outcome: Health authorities gained actionable early warnings, improving resource allocation and response readiness.
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