Powerful Solutions for Modern Enterprises

Problem

Organizations lose critical intellectual capital when employees leave. Without effective knowledge transfer, operations become inefficient and business continuity is at risk.

Solution

A deployment-ready, multi-modal RAG (Retrieval Augmented Generation) application that delivers accurate, domain-specific answers by combining semantic search with large language models - in three steps: query, retrieve, respond.

Problem

ALS is highly heterogeneous - patients vary widely in symptoms, disease progression, and subtypes. This makes it extremely difficult to identify representative patient populations for clinical trials, slowing drug development.

Solution

A multi-model ML framework combining:

• Supervised learning (Random Forest, SVM) for patient classification
• Unsupervised learning (K-means, hierarchical clustering) for disease subtype discovery
• Survival analysis (Cox model, Random Survival Forest) for progression modeling
• Deep learning (CNN, LSTM) for complex pattern recognition across longitudinal data

Paired with biomarker analysis to improve early diagnosis and predict personalized treatment responses.

Problem

Querying databases requires technical expertise in SQL or MongoDB syntax, creating a bottleneck for non-technical users who need data insights. Manual query writing is time-consuming, error-prone, and slows down decision-making.

Solution

A conversational AI assistant that allows users to interact with SQL and MongoDB databases using plain natural language. The agentic system interprets user intent, auto-generates and executes the appropriate queries, and returns results in a clear, readable format - no coding required.