Existing Solutions
Data Science Assistant - coming soon
Custom AI Solutions
10 Machine Learning Exploration
The 10-day machine learning initial exploration project aims to conduct a thorough analysis of the provided dataset, perform data processing, feature engineering, and build an initial ML model. The project will focus on gaining insights from the data, training and evaluating a baseline ML model, and providing recommendations for future steps. Additionally, deployment options will be explored, and all code and documentation will be delivered to ensure seamless knowledge transfer. This includes: 1. Data Processing and Feature Engineering: The project will begin with data cleaning, handling missing values, and converting data into a suitable format for analysis. Feature engineering techniques will be applied to extract meaningful insights from the dataset and create new relevant features for the ML model. 2. Data Insights Report: A comprehensive data insights report will be generated, highlighting key patterns, trends, and correlations within the data. Visualizations and summary statistics will be used to communicate important findings to stakeholders. 3. ML Model Training and Evaluation: An initial ML model will be selected and trained using the pre-processed data. The model's performance will be evaluated using appropriate metrics and cross-validation techniques to ensure robustness. 4. Recommendation and Next Steps: Based on the model performance and data insights, recommendations for further improvements and potential areas for optimization will be provided. This will serve as a guide for future iterations of the ML model. 5. Deployment Options: Various deployment options will be explored, and a recommendation for the most suitable deployment method will be included in the project deliverables. This may include cloud-based deployment, containerization, or any other relevant approach. 6. Code and Documentation: All code developed during the exploration phase, including data processing scripts and ML model training code, will be provided. The code will be well-documented, enabling easy understanding and future modifications.
Cloud Deployment of AI Models
Our project aims to deploy an existing Machine Learning (ML) model into a scalable and efficient prediction pipeline. We will convert the existing Jupyter notebook code into usable scripts and package the entire solution into a Docker container to ensure consistency across environments. The main goal is to create ML endpoints that can be triggered on-demand or scheduled for execution. This is done in following steps: 1. Review of Existing Code: We will utilize the provided existing code to process input data and make predictions using the pre-trained ML model. The code will be reviewed and optimized for efficiency. 2. Converting Jupyter Notebook to Usable Scripts: If your code is in the form of Jupyter notebooks, we will convert it into modular and reusable Python scripts. This transformation will enhance maintainability and readability. 3. Creating a Docker Environment: To ensure a consistent and reproducible run environment, we will create a Docker container that includes all necessary dependencies and packages required for executing the ML prediction pipeline. 4. Building the Machine Learning Prediction Pipeline: The ML prediction pipeline will be designed and developed to accept input data, process it through the existing ML model, and provide predictions as output. The pipeline will be thoroughly tested for reliability and scalability. 5. Creating ML Endpoints for Execution: RESTful APIs or suitable endpoints will be developed to interact with the ML prediction pipeline. These endpoints will be designed to either trigger predictions on-demand or schedule automated executions.
GenAI Agents
We specialize in designing and deploying Generative AI Agents that deliver real business value by combining cutting-edge AI models with domain-specific knowledge. Our solutions go beyond using pre-trained Large Language Models (LLMs) as-is—we enrich them with tailored data, intelligent retrieval systems, and seamless integration capabilities to create highly effective, context-aware agents. Key capabilities include: 1. Domain-Enriched AI – Extending pre-trained LLMs with custom knowledge bases, ensuring the AI understands and adapts to specific business needs. 2. Retrieval-Augmented Generation (RAG) – Building robust RAG pipelines that connect LLMs to curated data sources for accurate, reliable, and up-to-date responses. 3. System & Data Integration – Designing AI agents that integrate smoothly with existing enterprise systems, APIs, and diverse data sources to fit seamlessly into business workflows. 4. Custom AI Agents – Creating specialized AI agents for customer engagement, recommendation systems, process automation, and decision support. 5. End-to-End Deployment – Delivering production-ready solutions, from model design and integration to cloud deployment, ensuring scalability, security, and reliability. With this expertise, we help organizations unlock the full potential of generative AI by building agents that are not only intelligent but also fully connected to their data, workflows, and technology ecosystem.