Unique AI Documentation
Entry — Browser
Users & administrators
Sign in and work in the Unique AI application
Entry — API / SDK
Customer applications
Use the platform headless through the Public API & SDK
Identity & Tenancy
Enterprise Identity Providers
Customer-managed identity
Entra IDOkta
IAM
SSO · SCIM · user & group lifecycle
Organisation Structure
Multiple isolated organisations per instance
Org AOrg BOrg C
Every request is authenticated and mapped to an organisation here. Identity context travels with every Space, tool call and knowledge query.
Encryption at rest · permission propagation · audited access.
authenticated, organisation-scoped requests
Connectors
Two kinds of connectors: tool connectors plug into the platform’s MCP Hub, knowledge sources feed the Knowledge Base — each aligned with where it lands.
Tool Connectors
MCP Servers
Enterprise · partner · custom
Web
Public internet sources
Knowledge Sources
SharePoint / OneDrive
Confluence
Google Drive
Salesforce
Teams / Outlook
User uploads
Chat & AI Platform
Access Managementcontrols every Space · users & groups · use · manage · upload
Configuration Service
Branding & application shell
Chat Service
Chats · Spaces · streaming · API & SDK
A Space runs an agent
Conduct
Full agent harness · filesystem · code execution · skills · sub-agents
Claude CodeOpenAI CodexPi
Security boundary: isolated per-user sandbox · workspace isolation · approval policies · audited egress
Unique AI Chat
Tool-using agent, no filesystem or code execution
Agentic Table
Excel questionnaires filled row by row
Translator
Document and text translation
Custom
Customer experiences on API & SDK
the agent picks its tools
Tools
MCP Hub
connects the MCP servers from the connector column
Virtual MCP Servers
Combines tools from different MCP servers into a single virtual MCP server
Web Search
Built-in internet research
Internal Search
Permission-aware retrieval
Operations
Benchmarking
Model and prompt evaluation against reference sets
Audit Logs
User · model · tool activity
Usage & Observability
Quality · latency · cost · adoption · analytics and reporting
PostgreSQL — chat foundation
Organisations · Spaces · access · chats · tables · MCP configuration · encrypted at rest
Internal Search queries the knowledge base
every model call routes through the gateway
Knowledge & RAG
Virtual File System
Files are stored inside the platform
Mirrors the source folder structure
Access rights copied from the source, e.g. SharePoint permissions
Re-synced on change · every read is permission-checked
Indexingwrite path · asynchronous workers
Ingest & sync
Pull from connectors · delta detection · fetch permissions
Parse & understand
Layout · tables · OCR · image understanding
Chunking
Context-aware · tuned for financial documents: sections, tables, footnotes
Embedding
Dense vectors produced through the Model Gateway
Index write
Sparse and dense indices · metadata and access rights kept per chunk
Retrievalread path · one pass per query, called by Internal Search
Query
Scoped to the user’s permissions
Hybrid search
Sparse
Elasticsearch · keyword
Dense
Qdrant · vectors
Fusion
Both result sets merged and ranked
Return
Chunks with metadata retained, citations point back to the source file
Storage Layer
Object Storage
Files & extracted assets
PostgreSQL
Metadata · permissions · state
Qdrant
Dense vector indices
Elasticsearch
Sparse / full-text indices
Model Gateway & Models
Model Gateway & Policy
Routes every request · model-agnostic · configurable per organisation and Space
Language Models
OpenAI
Anthropic
Mistral
Qwen
Llama
···
others
Embedding models
Serves Spaces, tools and the knowledge plane, where it produces embeddings and document understanding.
Customer-owned model endpoints supported · no prompt or completion data leaves the deployment.
Hosting
Everything above runs on one deployment target per customer.
Azure
AWS
Google Cloud
Private cloud
On-premises
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