Platform Based Approach to Enterprise AI

Diverse Content Generation

AI, particularly Large Language Models (LLMs) such as OpenAI's GPTx, are capable of creating a variety of content forms, including text, voice, images, videos, and computer code.

Prompt-Driven Task Management

Effective prompt engineering simplifies machine learning tasks. Users can accomplish a range of ML tasks with just a few instructions to a single LLM, removing the need for fine-tuning specific to each use case or task.

Integrating Multiple Tools for Complex Goals

LLMs can handle tasks with multi-step objectives, such as identifying issues in Kubernetes clusters using simple English prompts. This is part of a new category known as agent frameworks, which have broad applications in areas like IT operations automation and multimodal chatbots.

Business-Specific Fine-tuning

Recent advancements in fine-tuning techniques, such as reducing trainable parameters for subsequent tasks, adapter-based fine-tuning, reinforcement learning with human feedback (RLHF), and supervised fine-tuning (SFT), allow for the customization of large models to fit organizational data and experiences.

1

Cloud-native Features

Utilizes a micro-services architecture for independent deployment and scalability.

Employs containerization for enhanced efficiency, speed, and scalable performance.

Ensures inherent scalability of technology components.

Facilitates service composability for orchestrating new capabilities and applications.

2

Facilitating Rapid Adoption

Enables swift experimentation with new frameworks and models, backed by standardized frameworks for validation and deployment.

Supports testing and proof-of-concept development with emerging technologies.

Simplifies the integration of new features.

Allows for the continuous addition of new AI technologies and components.

3

Self-governance and Control

Implements policy-based orchestration for models and infrastructure.

Incorporates responsible AI practices, such as de-biasing.

Optimizes AI components and infrastructure on an enterprise level.

Simplifies the application of AI governance at a large scale.

4

Modular Architecture

Comprises separate, loosely connected modules, each with clear, specific responsibilities.

Features clearly defined boundary objects to delineate one module from another, ensuring responsibilities are aligned with distinct organizational roles.

Ensures that any future modifications predominantly affect only the relevant module.

Prevents dependency on a single vendor, thereby avoiding vendor lock-in.

5

Data Availability

Offers access to data tools designed for working with both structured and unstructured data.

Supports data privacy and includes features for data synthesis, tracking lineage and provenance, managing versions, maintaining quality, and cataloging datasets for training and validation purposes.

Enables the exploration and utilization of data across enterprise systems.

Facilitates data observability through established methods of sampling and snapshot monitoring, employing standard metrics to assess recency, quality, and distribution of data.

6

Embedding AI

Incorporate AI functionalities into current enterprise applications.

Enable role-based access control for different capabilities and information within the system.

Integrate knowledge retrieval systems throughout various enterprise applications.

Facilitates the deployment of specialized activity agents designed to carry out particular productive tasks.

Offers the capability to track and evaluate the performance of models, utilizing this feedback to enhance future experiences and functionalities.