Unleash the power of AI with our differentiated offerings, leveraging the award-winning QpiAI-Pro™, an end-to-end No-Code/Low-Code Auto ML and MLOps platform, recognized for its capability of developing and deploying models across a wide variety of domains and sub-domains. Our dedicated team of scientists bring the best of AI to deliver unparalleled solutions to unique customer problems. We specialize in Generative AI, LLMs, Neural Architecture Search, Auto ML, ML Ops, Done Intelligence and Latency optimized AI solutions deployment. Our state of the art QpiAI-Enterprise™ AI solution provides drag and drop solutions for custom AI workflows. Join forces with QpiAI to unlock unlimited possibilities in AI and Quantum computing, and propel your organization towards unparalleled success.

AI Solutions features

Unlock the future of AI with our cutting-edge solutions. Our solutions encompass state-of-the-art AI techniques including Machine Learning, Deep Learning, Reinforcement Learning, Generative AI, LLMs, and more.

• With QpiAI be an early adopter to state of the art computer vision algorithms to achieve your business goals with unparalleled efficiency and optimized compute.
• Make the most of Generative AI with our industry proven innovators and scientists working round the clock to stay ahead of the disruptions within AI community
• Harness the power of our patented domain and subdomain tagging, enabling clients to explore new models with minimal computing resources.
• With our advanced model monitoring solutions monitor and improve the performance of your deployed AI solutions and stay ahead.
• We have proven expertise for deploying advanced AI solutions for world leaders in smart cities, drone imagery analytics, healthcare, logistics, production line inspection, and predictive maintenance.
• Make the most of CUDA and GPUs with our advanced accelerators and our patented sparsification techniques and search space reduction algorithms.
• Centralized data manageent: Bring your existing databases from any of the AWS database services.

Project Objectives

• Develop and Deploy Robust AI Models: The objective is to develop and deploy AI models that deliver accurate and reliable predictions or classifications for a specific problem or task.
• Optimize Model Performance and Scalability: The goal is to optimize the performance of AI models by improving accuracy, reducing latency, and ensuring scalability to handle large datasets and real-time predictions. Make the most of computing resources from AWS optimizing your trainng and deployment costs for Amazon Sagemaker.
• Streamline Model Deployment and Management: The objective is to establish a streamlined process for deploying and managing AI models in production environments, ensuring efficient updates, version control, and monitoring. Manage cloud deployment with integrated streaming services relying on AWS trusted platforms like Kinesis.
• Enable Continuous Model Monitoring and Improvement: The objective is to establish a continuous monitoring process to track model performance, detect anomalies, and trigger retraining or updates when necessary, ensuring models remain accurate and up-to-date.

Project Deliverables

Scope of client deliverables are customizable as per project requirements and may include the following details that are delivered in phases as provided in list below,

• Data Analysis Report: A comprehensive report detailing the analysis performed on the client's data, including data cleaning, preprocessing techniques applied, and insights gained from exploratory data analysis.
• Feature Engineering and Selection Report: A report outlining the engineered features, selection techniques used, and the rationale behind their inclusion in the final model.
• Trained AI Models: The final trained AI models along with their corresponding weights or parameters, ready for deployment in production environments.
• Model Evaluation Report: A report presenting the evaluation results of the AI models, including metrics such as accuracy, precision, recall, F1-score, and any other relevant performance indicators.
• Model Validation Results: Results from the validation process, demonstrating the model's performance on unseen or validation datasets to ensure its generalization capabilities.
• Model Interpretability and Explainability Analysis: A report or documentation highlighting the interpretability and explainability analysis performed on the models, including insights into feature importance, model decision paths, or explanations for individual predictions.
• Knowledge Transfer, Training and User Guides: Materials and resources to facilitate knowledge transfer and enable the client's team to maintain and operate the deployed ML models effectively with detailed documentation on API documentation, data input specifications, and troubleshooting guidelines.