CloosphereAll Your AI, Securely Managed in One Place. Build, operate, and govern enterprise AI on one platform
Cloosphere ties together connecting internal data, building AI agents, verifying answer quality, and controlling security and access in a single operating model. It cuts the complexity of building and maintaining separate tools, and keeps every run under consistent policies.
Three challenges to solve when scaling AI across the organization
Pilot results are hard to scale company-wide
As users and departments grow, features alone can't keep things running reliably. You need an operating model that manages permissions, usage limits, and execution logs together.
Connecting internal data means you need controls
Linking documents and databases improves the answers, but you need a way to know who saw what, and whether personal information ever reached the model.
When answer quality slips, you need to pinpoint the cause
Continuous improvement depends on pinpointing which step went wrong — document retrieval, SQL execution, or the model's response.
Four areas, connected from build to operations
Data, agents, evaluation and observability, and governance work together across every step of Connect, Build, and Operate.
Data
Answers grounded in your documents and databases
Key features Knowledge Base · Data analysis (natural language → SQL) · Glossary · Knowledge Graph More about Data → BUILDAgents & flows
Works with your data and tools, and asks before writing
Key features Agents · flows · multi-agent · skills · MCP/OpenAPI tools More about Agents & flows → OPERATEEvaluation · observability
Every answer leaves a trail, and quality shows up as numbers
Key features Traces · automatic quality evaluation · Arena · usage and cost estimation More about Evaluation · observability → OPERATEGovernance
Applies your organization's policies and permissions consistently across all AI use
Key features Guardrails · audit logs · 3-level permission hierarchy · token limits · Code Gateway More about Governance →Every step from question to answer and record
- 1 A user asks a question
From web chat, Microsoft Teams, or an embed widget in an internal system.
- 2 Guardrails see it first
Personal information like Korean resident registration numbers or bank account numbers, and banned words, are blocked, deleted, or masked per policy.
- 3 The agent picks what it needs
It combines Knowledge Base search, DB lookups (SQL), the glossary, and external tools as needed. Anything that changes data requires human approval before it runs.
- 4 The LLM in your account writes the answer
The model is called from an organization-approved list, within per-user, per-group, and per-organization token limits.
- 5 The whole process is recorded
Call order, time taken, and tokens are captured in a trace; configuration changes are captured in the audit log; and agents with automatic quality evaluation turned on build up quality scores for their answers.
Self-hosted, with features chosen by tier
Deployed in your cloud (Azure · Google Cloud · AWS) or an air-gapped network. See security and deployment
Connects directly to your organization's own LLM account; usage is billed under the terms of your contract with the provider.
Available in 4 tiers: Basic · Standard · Professional · Enterprise. Audit logs and guardrails start at Standard; data analysis, flows, traces, and automatic evaluation start at Professional; Code Gateway, global guardrails, encryption (KMS), and the Knowledge Graph are included in Enterprise. See what each tier includes
Monthly scheduled releases. See release notes
See how the four capability areas come together in one operating flow
We set up a demo environment based on your deployment method, integration scope, and governance requirements.