Multi-agent orchestration platform for scaling, sustaining, and growing GEN AI across enterprises

Orchestrate across LLMs to cut costs

Orchestrating LLMs boosts performance, efficiency, and scalability, reducing costs by coordinating multiple models and optimizing resources.

Get Started

Our Multi-agent Orchestration Platform

Coordinates LLM operations for optimal performance and efficiency, handling monitoring, scheduling, and resource allocation.

Enables smooth interaction between language models, boosting communication and task coordination. Ensures efficient teamwork and system integration.

Focuses on specific functions, optimizing specialized tasks within language models. Its responsibilities include executing targeted actions and ensuring precise, efficient outcomes.

Enterprise AI : Building Multi-Agent Systems

This strategic guide to enterprise AI adoption covers essential concepts and terminology, explores core technology building blocks and architectural frameworks, and delivers actionable implementation strategies. An essential resource for leaders integrating AI into organizations.

  • What is an agent?
  • What are the different types of agents?
  • What is the difference between an agent and an LLM?
  • What kind of intelligence does an agent provide to a business?
  • What is the role of agents in GEN AI?
  • How do agents interact with LLM?
  • What are the Agent market trends and business demand?
  • Discuss about the Business benefits of an agent
  • How does an agent fit into a complex legacy system environment?
  • Discuss how the GEN AI Solutions are at risk without an agent
  • GEN AI Solution with agent and without agent

  • Agent enabling open source technologies
  • Agent enabling platform
  • Comparative analysis of technologies and platforms
  • Which technology is most effective for implementing Agent at scale?
  • Architectural components of the platform and technology
  • How Bedrock agents enable secure and safe interactions with business processes?

  • Case Study -1- Pubic Services
  • Case Study -2- Energy
  • Solution architecture
  • Summary of the implementation step
  • Detailed implementation steps
  • Source code organization

Department of Motor Vehicles

Discover the collaboration of human feedback and an LLM, serving as a judging team at the DMV.
Learn how they combine to enhance decision-making.

Try it Now Examine the interaction between human feedback and an LLM serving as a judge at the DMV.

Key Components of the Agentic Approach

We excel in designing autonomous agents with advanced decision-making and adaptive interactions, optimizing actions through reinforcement learning for intelligent solutions.

Components and Frameworks

Agents operate independently, making decisions without direct human intervention.

Ability to adjust to environmental changes dynamically.

Utilizing logic and algorithms to choose optimal actions.

Often through reinforcement learning, agents improve over time based on experiences.

Interaction with other agents or systems to collaborate and achieve goals.

Dynamic AI: Bridging Traditional and Agentic Approaches

We bridge traditional and agentic approaches by empowering agents with autonomy and adaptability. Unlike static scripts, our agents learn and make dynamic decisions. Trust us for intelligent, evolving solutions.

  • Relies on predefined rules and scripts.
  • Limited adaptability and flexibility.
  • Requires manual updates for changes.

  • Empowers agents with autonomy and decision-making.
  • Adapts dynamically to environmental changes.
  • Learns and evolves through experience and interaction.

Energy agents cut waste and boost efficiency

Energy agents minimize waste and enhance efficiency, ensuring optimal resource use.

Let's innovate together

Agentic Innovation in Energy Management

We specialize in developing autonomous systems that improve energy efficiency using real-time data. Our innovative solutions promote sustainability and reduce waste. Trust us for smart and efficient energy management.

Analyzes and reorganizes layouts for maximum efficiency and utility.

Designs adaptive floor plans to improve workflow and accessibility.

Monitors stock levels and predicts needs to minimize waste and optimize storage.

Continuously tracks assets to ensure optimal utilization and maintenance.

Allocates resources effectively to balance demand and capacity.

Anticipates equipment failures to prevent energy loss.

Begin agent development

Agent-Driven Efficiency and Agility in Business

By optimizing development efforts, time, and resources, this approach facilitates quick adaptation to changes, streamlines processes, reduces costs, and accelerates innovation, enhancing competitive advantage.

Key Benefits

Reduces manual tasks, enhances productivity, and accelerates processes.

Improved accuracy and responsiveness through data-driven insights.

Streamlined operations and optimized resource use.

Minimized waste and operational expenses.

Quick response to market and environmental changes.

Uncover Your Advantages

Open Source Agent Ecosystems: Building Intelligent Automation

Open Source Agent Ecosystems enable sophisticated automation without proprietary constraints, leveraging foundation models to create transparent AI solutions while maintaining governance standards.

(Llama, Mistral, Falcon) deliver enterprise capabilities without vendor lock-in

(LangChain, AutoGPT) enable custom reasoning systems with minimal code

Coordinate complex multi-agent workflows with transparent governance

Collaboration platforms ensure appropriate oversight and continuous improvement