Transform Your IT Operations with the Next-Generation Enterprise AI System

99% of enterprises don’t want their sensitive data exposed to public AI models.

At UnityOne, we believe you shouldn’t have to compromise. Our platform delivers a Private LLM (Large Language Model) as a Service-fine-tuned exclusively on your data, securely hosted on dedicated GPU infrastructure, and seamlessly embedded within your multi-cloud management suite.

Absolute Data Privacy

Your information stays within your private AI environment.

Always Upgradable

Enterprise SaaS simplicity with powerful, future-proof architecture.

Enhances Insights

Analyze vast amounts of operational data, AI identifies patterns and predicts issues.

Customized Private LLM

Get private LLM for each customer as per their unique requirements

How the Enterprise AI System Works?

Connect & Ingest

Effortlessly integrate your operational, cloud, and business data sources.

Private LLM Provisioning

Receive a secure, dedicated LLM, fine-tuned to your organization’s unique data and needs. 

Deploy Agentic AI

Activate SuperAgents and Virtual Agents to automate analysis, identify risks, and deliver actionable recommendations across your IT environment.

Conversational Insight

Ask questions, get answers, and automate actions-all in natural language.

Optimize & Evolve

The system continually improves, adapting to your evolving infrastructure and business priorities.

Why Choose Our Enterprise AI System?

Deliver a secure, customizable, and highly capable enterprise AI platform for multi-cloud management and IT operations. By embedding private LLMs as a service, leveraging agentic AI, and integrating both internal and external data sources, organizations can automate complex analyses, enhance compliance, and drive operational excellence-all while maintaining full control over their data .

Private AI, Maximum Security

Private LLMs for Every Customer: Your data never leaves your control. Each organization receives a dedicated, fine-tuned Large Language Model (LLM) hosted securely in a private cloud or on-premises, ensuring absolute privacy and regulatory compliance.

Enterprise-Grade Security: Powered by dedicated GPU infrastructure, your AI environment is isolated, robust, and always protected.

Intelligent Automation with Agentic AI

Domain-Specific AI Agents: SuperAgents and Virtual Agents are pre-trained on industry-leading operational knowledge-networking, storage, compliance, and more-delivering expert-level analysis and recommendations.

Conversational AI Interface: Interact with your infrastructure in plain English. Instantly surface insights, automate tasks, and resolve issues with natural language queries.

Unified Data for Smarter Decisions

Seamless Integration: Our unified data model ingests and harmonizes information from your infrastructure, DevOps tools, contracts, and external sources, providing a holistic view for AI-driven decision-making.

Continuous Learning: The system evolves with your business, leveraging feedback and human-in-the-loop processes to deliver ever-more relevant insights.

Multi-LLM Framework for Comprehensive Intelligence

Best-in-Class Knowledge: Your private LLM is augmented by public LLMs for up-to-date product information, best practices, and industry benchmarks-never exposing your sensitive data.

Customize different LLMs as per different cloud scenarios and required AI capabilities.

Unified Data Model: The Foundation of Intelligent Enterprise Operations

  • UnityOne’s Unified Data Model (Data Fusion) seamlessly integrates, harmonizes, and contextualizes data from every corner of your enterprise IT and business landscape, creating a single, trusted source for AI-driven insights, automation, and confident decision-making at scale.

Comprehensive Data Ingestion
  • UnityOne connects to all your critical data sources—across infrastructure, cloud platforms, business systems, and external feeds. This ensures that every relevant data point, whether structured or unstructured, is captured and brought into a centralized environment for further processing.
Harmonization and Standardization
  • The platform standardizes and normalizes incoming data, resolving inconsistencies, duplicates, and format differences. By applying a common schema and taxonomy, UnityOne creates a consistent dataset that is easy to analyze, share, and use across all teams and AI agents.
Business Context and Semantic Layer
  • UnityOne enriches raw data with business meaning by mapping it to relevant taxonomies, ontologies, and metadata. This semantic layer allows AI models and agents to understand relationships and context, ensuring that insights and automations are always aligned with your business objectives.
Single Source of Truth for AI and Automation

  • With all data unified and contextualized, UnityOne provides a single, trusted source for analytics, AI, and automation. This eliminates silos, accelerates data-driven innovation, and ensures that every insight, action, and decision is based on the most accurate and up-to-date information available.

Multi-LLM Framework: The Next Evolution in Enterprise AI

Harness the Power of Multiple Language Models for Unmatched Intelligence

Seamless Integration of Private and Public LLMs

The Multi-LLM Framework from UnityOne brings together the best of private and public large language models, creating a unified AI ecosystem tailored for enterprise needs. Sensitive organizational data is processed securely within your private LLM environment, while public LLMs provide access to the latest industry benchmarks, best practices, and global knowledge—ensuring your data privacy is never compromised.

Intelligent Agent-Orchestrated Workflows

Our modular agent-based architecture allows specialized AI agents to interact with the most suitable LLM for every task. Whether it’s network management, security, cost analysis, or IT service management, each agent leverages the strengths of different models. A master agent oversees the entire process, enabling advanced use cases like conversational AI, anomaly detection, and intelligent automation.

Unified Data for Smarter Decisions

With a harmonized data model, the framework ensures all LLMs and agents operate on consistent, high-quality information. This unified approach breaks down silos, enabling comprehensive insights and actionable recommendations across your entire IT and business landscape.

Scalable, Secure, and Future-Ready

The Multi-LLM Framework is designed for flexibility and growth. Easily add new LLMs or agents as your business evolves, while real-time monitoring and robust security keep your operations resilient. Experience the future of enterprise AI—scalable, secure, and always aligned with your unique requirements

Multi-Agent Framework: Powering Collaborative Enterprise AI

Experience the future of enterprise AI with a Multi-Agent Framework—where intelligent collaboration, modular architecture, and orchestrated automation transform complexity into competitive advantage

Orchestrated Intelligence for Complex Workflows

A Multi-Agent Framework brings together multiple autonomous AI agents, each with distinct expertise, to collectively solve challenges that are too complex for any single agent. These agents collaborate, coordinate, and share information—enabling your enterprise to automate multi-step workflows, streamline decision-making, and drive operational excellence.

Modular, Scalable, and Adaptive by Design

At the core of this framework are specialized agents—such as those for networking, security, cost analysis, or ITSM—each powered by advanced AI and, often, large language models. The system is highly modular: agents can be added, updated, or removed as business needs evolve, making the framework inherently scalable and adaptable to changing environments.

Master Agent Orchestration for Seamless Execution

A master or orchestrator agent supervises the collaboration, passing context, enforcing governance, and ensuring that agents interact efficiently. This orchestration enables agents to divide labor, resolve conflicts, and chain together tools and actions, automating processes end-to-end—from data retrieval to strategic reporting—without human intervention.

Enterprise-Ready Automation and Resilience

The Multi-Agent Framework operates as a cohesive ecosystem, integrating with your existing enterprise systems. Agents perceive their environment, reason about data, communicate with each other, and coordinate actions—all while maintaining alignment with your business goals and policies. The result is robust, resilient, and real-time automation that scales with your organization’s ambitions.

How UnityOne AI Transforms Enterprise IT Operations

Conversational Inference

Agents and LLMs enable users to interact with IT and cloud systems in natural language. The master agent routes queries to specialized agents, which use LLMs for understanding and context, delivering clear answers or automating actions through a conversational interface.

Intelligent Automation

The master agent coordinates domain-specific agents, powered by LLMs, to automate workflows like provisioning, incident response, and cost optimization. This reduces manual effort and ensures efficient, adaptive operations.

Anomaly Detection

Security and Network Agents, enhanced by LLMs, monitor data streams and identify abnormal patterns. When anomalies are detected, agents can escalate, recommend fixes, or trigger automated responses for rapid resolution.

Knowledge Discovery

The KB Agent and LLMs synthesize insights from internal and external sources. Users receive summarized best practices, compliance guidelines, and strategic recommendations, all orchestrated by the master agent.

Multicloud FinOps Planning

FinOps Agents, leveraging LLMs, analyze multi-cloud spend and usage, forecast costs, and recommend optimizations. The master agent automates governance and drives savings across cloud environments.

Autonomous Cloud Operations

Orchestration and CloudOps Agents, guided by LLMs, automate cloud resource management and adapt configurations in real time. The master agent ensures operations remain resilient and aligned with business goals.

Predictions and Insights

Prediction Agents use LLMs to analyze data, detect trends, and generate forecasts for performance and costs. The master agent aggregates these insights, enabling proactive, data-driven decisions across the enterprise.

FAQs

How can DCIM data be used in enterprise AI system?

Data Generated by DCIM can be Asset inventory (servers, PDUs, cabinets), Real-time power consumption metrics ,Temperature/thermal logs, Network topology mappings, Compliance audit trails, Capacity utilization reports or AI Integration data.

This data feeds AI models for predictive maintenance, energy optimization, and capacity planning. For example, thermal logs can train ML models to preempt cooling failures.

Data Generated by HCMP can be Resource allocation metrics (CPU, memory, storage), Cross-cloud workflow execution logs, Auto-scaling event histories, Private LLM performance metrics, Alert prioritization patterns or AI Integration data

HCMP data enables AI-driven resource optimization, such as predicting peak workloads for auto-scaling or identifying inefficient workflows. The platform’s orchestration engine uses this data for autonomous decision-making.

Data Generated by Multicloud AI Ops can be Incident/alert logs with root cause analysis (RCA), Anomaly detection patterns, Auto-remediation action histories, Application dependency mappings, Performance monitoring telemetry or AI Integration data.

AIOps data trains ML models for predictive incident management and self-healing automation. For example, historical RCA data improves anomaly detection accuracy in private cloud environments.

Data Generated by cost management can be Cross-cloud cost breakdowns (AWS, Azure, GCP), Budget threshold alerts, Cost anomaly flags, Resource rightsizing recommendations, Forecasted expense trends or AI Integration data.

Financial data powers AI-driven cost optimization, such as identifying underutilized resources or predicting future spending. Showback/chargeback models align with FinOps frameworks.

Data Generated by sustainability can be Carbon footprint metrics, Energy consumption per device/rack, ESG compliance reports, End-of-life (EOL) device statuses, Power optimization recommendations or AI Integration data

Sustainability data trains models to balance performance with environmental goals, such as prioritizing energy-efficient servers or automating green energy procurement.

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