AI Beyond Limits: Shaping a Future of Infinite Possibilities

This isn’t just about innovation — it’s about transformation

Artificial Intelligence is the catalyst redefining how businesses innovate, adapt, expand, and lead. In every industry, AI is fueling the drive to connect with new markets, outpace competitors, and create solutions that not only disrupt the status quo but leave a lasting impact on the world.

Organizations today need more than technology; they need a visionary partner who sees opportunities where others see obstacles. That’s the essence of AI Beyond Limits. It’s about embracing the unknown, leveraging collective intelligence, and tackling the most complex challenges with bold, purpose-driven solutions.

The journey starts here. Together, we’ll push boundaries, unlock new dimensions of growth, and shape a future that’s driven by imagination and innovation. Let’s create the extraordinary. Explore how our expertise can help your enterprise lead in an AI-powered world.

Introduction to Oriva.AI Platform

By Ashok Bhatia, President & CEO

Mentis Oriva.AI Platform: Redefining Enterprise AI for Intelligent Solutions

ORIVA.AI is an advanced AI platform that brings together different types of data, custom AI models, and legacy systems with seamless integration in a cloud-native architecture. It streamlines AI development from data preparation to deployment, enabling organizations to harness AI for predictive analytics, automation, and strategic decision-making. The result is an enterprise AI solution that help businesses optimize operations, mitigate business challenges, and accelerate ROI by reducing complexity, ensuring compliance, and enhancing scalability.

Our AI Services

We provide customized AI solutions and services designed to drive growth and foster innovation.

Use case selection and gathering data

Model review and validation

Model monitoring & governance

Model training and tuning

Model deployment & system integration

(1/5)

AI Orchestrator

The AI Orchestrator serves as the central hub for deploying, integrating, and managing the interaction of AI workloads across nodes within a cluster. It facilitates seamless orchestration of AI models, optimizes data flow management, and ensures efficient allocation and monitoring of computational resources.

Cloud-First Solution

The Oriva.AI Platform is designed as a cloud-native solution, leveraging a microservices architecture, containerized workloads, and CI/CD pipelines to ensure scalability, flexibility, and agility. This robust design allows the platform to efficiently handle containerized AI workloads across diverse environments.

Solution Elements

The platform comprises a suite of specialized components:

ML Orchestrator: Manages the end-to-end lifecycle of machine learning models.
Training Server: Dedicated to training AI models at scale.
Inference Server: Powers real-time or batch predictions.
Data Pipeline Engine: Ensures smooth data processing and flow.
Repository Management: Handles storage and version control for models and artifacts.
Data Stores: Includes structured, columnar, and vector-based storage solutions to support diverse AI workloads.

Model Lifecycle Management

The platform fully supports AI model lifecycle management, encompassing:

Data Gathering & Preparation: Simplifies data ingestion and preprocessing.
Model Training: Provides tools for efficient and scalable training.
Model Evaluation: Enables rigorous testing and validation.
Model Deployment: Streamlines the process of bringing models into production.
Model Governance: Ensures compliance, traceability, and robust monitoring.

This comprehensive lifecycle management empowers users to explore, refine, and deploy multiple AI models with ease, driving innovation and operational excellence.

(2/5)

Distributed Machine Learning

Parallelism Approaches

The solution supports a variety of parallelism techniques, including Data Parallelism, Pipelined Model Parallelism, and Tensor Model Parallelism, to optimize the distribution of tasks across multiple resources.

Distributed ML Training

Training large models demands substantial resources over extended periods, and in many cases, deploying multiple parallel resources is essential to reduce training time. This solution enables the scheduling and coordination of distributed ML training workloads, with dynamic resource allocation to optimize performance.

Distributed ML Serving

When a model is too large to fit into a single GPU’s memory, distributed ML serving becomes necessary. The solution facilitates the distribution of inference workloads across multiple nodes, enabling efficient handling of resource-intensive tasks such as large language models (LLMs).

As machine learning (ML) algorithms continue to grow in complexity, their training and inference processes now require multiple GPUs. Distributed ML leverages various parallelism techniques to efficiently utilize distributed computing resources.

(3/5)

Intelligent Data Federation

Dynamic Data Federation enables seamless integration of data from diverse sources, supporting pipelines that ingest, process, and store data across a variety of formats and storage systems, including structured, columnar, vector, and file-based stores.

Data Pipelines

The solution supports both batch and streaming data pipelines for the efficient ingestion, transformation, and storage of data into the appropriate federation components. These pipelines streamline the flow of data, ensuring it is processed and stored in the most effective manner.

Data Connectors

With robust support for data ingestion from a wide range of sources—including FTP/SFTP servers, S3 Buckets, Google Drive, web pages, and more—the solution ensures that data in various formats is converted into a unified format. Once transformed, the data is stored in the respective data repositories for easy access and analysis.

Data Processing

Data extracted during the pipeline ingestion process undergoes thorough pre-processing, cleaning, and transformation to ensure it is ready for analysis. The solution also supports repository management for distributed file storage and retrieval, providing flexibility and scalability for large datasets.

Data Storage

The solution leverages a data lake architecture, supporting various storage options such as structured, columnar, vector, and file/object storage. These diverse storage categories are optimized for different types of applications, ensuring that data is stored in the most appropriate format for each use case.

(4/5)

Mega Model Store

Supervised Models

Supervised learning algorithms leverage labeled data to make predictions. The solution supports a comprehensive set of supervised models, including both balanced and unbalanced models such as Decision Trees, Logistic Regression, and Neural Networks, among others.

Time Series Models

Time series algorithms are designed to handle data that varies over time. This solution supports both statistical and machine learning-based time series models, including ARIMA, Prophet, and Deep AR, for effective forecasting and trend analysis.

Reinforcement Models

Reinforcement learning algorithms focus on an agent’s continuous learning process within an environment to maximize cumulative rewards. The solution offers support for various reinforcement learning models, including Deep QN, Soft Actor-Critic, and others, to optimize decision-making in dynamic environments.

Other Models

In addition to the above, the solution also supports a variety of other model categories, such as Causal Inference, Model Interpretation, Anomaly Detection, Text-Based Large Language Models (LLMs), Vision Models, and more, enabling a wide array of use cases across industries.

Machine learning algorithms are diverse and expansive, each tailored to address specific challenges. Some algorithms excel with large datasets featuring many variables, while others perform better with smaller or less structured data. This broad array of algorithms provides the flexibility to solve a wide range of real-world problems, from predictions and pattern recognition to enabling autonomous decision-making.

(5/5)

Gen AI RAG Engine

Generative AI refers to algorithms capable of creating new content such as text, images, music, or code. These algorithms are built on foundation models, like Large Language Models (LLMs), which are trained using public data. Retrieval-Augmented Generation (RAG) enhances the capabilities of foundation models by integrating external or proprietary context information, allowing for more relevant and dynamic content generation.

Production-Ready RAG Pipeline

Our solution provides a production-ready RAG pipeline, designed to facilitate the development and deployment of LLM applications. The pipeline includes key components such as the Query Rewriter, Chunk Reranker, Retriever, Response Generator, and Response Synthesizer, ensuring efficient and accurate content generation.

Open Source LLM Models

For cost control and enterprise confidentiality, the solution supports the local deployment of open-source LLM models. This allows organizations to leverage the power of generative AI while maintaining full control over their data and infrastructure.

Vector DB Integration

Our solution integrates seamlessly with vector databases, enabling chunk storage and retrieval through semantic, keyword, and hybrid search methods. Pre-integration with leading vector databases and data pipelines ensures smooth document ingestion and retrieval for enhanced AI performance.

Guardrail Support

As LLM applications become more critical, ensuring their security and reliability is paramount. Our solution supports configurable guardrails, which provide text validation and safeguard against model hallucinations, helping maintain the integrity and quality of generated content.