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As per our latest analysis, we have identified that two fund categories provide us with the highest returns compared to other categories.

Each month we will recommend one Mutual Fund in both of these categories. Here are the latest recommendations.

Related case studies

Data Science

AI-Powered Customer Onboarding Agent for EdTech Mentorship Platform

An education technology company providing mentorship programs for universities and corporations faced a 6–8 week manual onboarding cycle that relied on unscalable tribal knowledge and synchronous CSM labor for each partner configuration. A conversational AI onboarding agent was deployed using a LangGraph-based multi-agent system on AWS Bedrock with RAG-powered recommendations and human-in-the-loop approval, reducing infrastructure costs to approximately $5.32 per new partner onboarding while enabling partners to self-serve 68+ program design decisions asynchronously.

Data Science

AI Automation for Industrial Aftermarket Maintenance

A heavy industrial power generation client faced an unsustainable maintenance documentation challenge: manually extracting preventive maintenance tasks from thousands of pages of fragmented OEM manuals, vendor handbooks, and site procedures required up to 480 hours per 12-asset plant and systematically missed hidden safety-critical tasks embedded in appendices and commissioning guides. By deploying a Google Gemini-powered semantic extraction platform with parallel processing, cross-document knowledge graph unification, and a RYGB confidence validation framework, the solution achieved 160x faster processing (40 hours to 15 minutes per asset), a 469% expansion in discovered maintenance activities over human baselines, and a 94–96% reduction in extraction costs—transforming static documentation into living operational intelligence at enterprise scale.

Data Science

Clinical Application for Tracking and Managing Autoimmune Diseases

A digital health startup is building the intelligence layer for patients living with MS, RA, and Lupus — an alpha-stage platform focused on the data collection phase. It integrates Apple Watch and Fitbit biometrics with structured daily check-ins and flare reports, all stored in database, and enriches onboarding with Claude AI–powered community insights derived from real patient. The system delivers a production-ready REST API with 30+ endpoints covering onboarding, daily tracking, flare reporting, wearable ingestion, drift detection, home dashboard, and account management. It also includes personalized z-score–based anomaly detection, statistically rigorous community intelligence built from 50+ patient cohorts, and foundational infrastructure for proactive flare prediction in future releases.