Serenity Star provides a governed enterprise AI platform that enables organizations to build, deploy, and orchestrate generative AI agents and assistants across their operations. The platform offers centralized security, compliance, and cost controls with support for multiple LLMs, no-code/low-code tools, and flexible deployment options including SaaS, on-premise, and hybrid cloud. It includes pre-built agent configurations for systems like SAP and Microsoft to accelerate adoption.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises struggle to adopt generative AI safely and at scale, facing challenges around data security, regulatory compliance, and uncontrolled spending. Without centralized governance, AI deployments become fragmented, difficult to audit, and risky for organizations handling sensitive data.
Solution
Serenity Star provides a unified platform for creating, deploying, and governing production-ready generative AI agents and services. The platform orchestrates multiple large language models with centralized policies, security controls, auditing, and cost management, allowing organizations to deploy AI assistants, integrations, and automations without vendor lock-in. It includes a corporate chat tool trained on internal knowledge with SSO and data sovereignty, plus no-code and low-code tools for building workflows. Deployment options range from shared SaaS to private cloud, on-premise, and hybrid configurations, all under the same governance layer.
Target Audience
Primary customers are enterprises of any size and sector that need to deploy generative AI across employee productivity, customer service, process automation, and back-office operations while maintaining security and compliance.
Features
- Centralized governance with policy management, data obfuscation, versioning, and full audit trails
- Multi-LLM orchestration supporting hundreds of models or custom models without vendor lock-in
- Pre-built agent configurations for enterprise systems such as SAP and Microsoft
- No-code, low-code, and API/SDK integration paths for building AI workflows
- Corporate chat with SSO, internal knowledge base integration, and data control
- Flexible deployment options: shared SaaS, dedicated cloud, on-premise, and hybrid cloud
- Cost control and analytics for tracking AI spending and decisions