One Platform.
Infinite AI Possibilities.
One Platform.
Infinite AI Possibilities.
One Platform.
Infinite AI Possibilities.
From intelligent conversations to autonomous multi-agent networks — CCG NEXUS AI is the only platform where every AI artifact you build becomes a composable, versioned tool that powers the next layer of intelligence. Build once. Compose everywhere.
MCP-Native Architecture
Enterprise-Grade Security
Cloud-Agnostic Deployment
Multi-LLM Orchestration
No-Code RAG Builder
Autonomous Agent Networks
The AI Platform That Thinks in Networks
The AI Platform That Thinks in Networks
Most AI platforms give you a chatbot or a workflow engine. CCG Nexus AI gives you the entire stack — from a single intelligent conversation to a self-orchestrating network of specialist AI agents — unified under one coherent, composable architecture where every artifact you build becomes a tool the next module can call.
CCG Nexus AI is a Full-stack enterprise Generative AI platform that runs entirely within your organisation's cloud boundary. Every conversation, every knowledge base, every agent, every automated workflow — it all lives in your environment, governed by your rules, secured with your keys.
CCG Nexus AI is a Full-stack enterprise Generative AI platform that runs entirely within your organisation's cloud boundary. Every conversation, every knowledge base, every agent, every automated workflow — it all lives in your environment, governed by your rules, secured with your keys.
Most AI platforms give you a chatbot or a workflow engine. CCG Nexus AI gives you the entire stack — from a single intelligent conversation to a self-orchestrating network of specialist AI agents — unified under one coherent, composable architecture where every artifact you build becomes a tool the next module can call.
At its core is a radical design principle borrowed from the Model Context Protocol (MCP): every capability the platform produces — a RAG bot, an agent, a workflow — is automatically exposed as a versioned, callable tool that any other module can consume. Build a knowledge base in RAG Studio and it instantly becomes a tool your agents can reason over. Build an agent and it immediately becomes a node your workflows can orchestrate. The composability is structural, not stitched together.
At its core is a radical design principle borrowed from the Model Context Protocol (MCP): every capability the platform produces — a RAG bot, an agent, a workflow — is automatically exposed as a versioned, callable tool that any other module can consume. Build a knowledge base in RAG Studio and it instantly becomes a tool your agents can reason over. Build an agent and it immediately becomes a node your workflows can orchestrate. The composability is structural, not stitched together.
The result is a platform that starts delivering value from day one with intelligent chat and knowledge retrieval, and grows into a fully autonomous multi-agent intelligence layer without changing architecture. One platform. One mental model. Unlimited capability.
The result is a platform that starts delivering value from day one with intelligent chat and knowledge retrieval, and grows into a fully autonomous multi-agent intelligence layer without changing architecture. One platform. One mental model. Unlimited capability.
Composable by Design
Every artifact — RAG bots, agents, workflows — automatically becomes a versioned MCP tool callable by any other module. No integration code. No data pipelines. Pure composition.
Sovereign Deployment
Deployed entirely within your cloud subscription. Your data, your keys, your infrastructure. Zero egress to CCG. Complete organisational sovereignty over every byte.
No-Code to Pro-Code
Business users build production RAG bots in minutes. Developers extend with custom MCP tools and agents. Everyone operates in the same governed, observable platform.
Model-Agnostic
Works with Claude, Chat GPT, Gemini, Grok, Llama, and any model on Azure AI Foundry, AWS Bedrock, or Google Vertex — switch models without changing architecture.
Seven Layers. One Unified INTELLIGENCE.
Seven Layers. One Unified INTELLIGENCE.
Seven Layers. One Unified INTELLIGENCE.
CCG NEXUS AI is architected in seven distinct, independently scalable layers. Each layer has a clear responsibility, a clean interface,
and is replaceable without affecting the layers above or below it.
CCG NEXUS AI is architected in seven distinct, independently scalable layers. Each layer has a clear responsibility, a clean interface, and is replaceable without affecting the layers above or below it.
CCG NEXUS AI is architected in seven distinct, independently scalable layers. Each layer has a clear responsibility, a clean interface, and is replaceable without affecting the layers above or below it.
Multi-LLM Chat
Talk to every model. See every thought . Every major language model - in one intelligent, observable interface
that shows you exactly how AI reasons through your questions.
Multi-LLM Chat
Talk to every model. See every thought . Every major language model - in one intelligent, observable interface
that shows you exactly how AI reasons through your questions.
Multi-LLM Chat
Talk to every model. See every thought . Every major language model - in one intelligent, observable interface that shows you exactly how AI reasons through your questions.
Universal Model Access
Real-Time Trace Panel
MCP Tool Attachment
Streaming SSE
Slash-Command Prompts
Capability-Aware Gating
Image Generation
Universal Model Access
MCP Tool Attachment
Slash-Command Prompts
Image Generation
Real-Time Trace Panel
Streaming SSE
Capability-Aware Gating
Universal Model Access
MCP Tool Attachment
Slash-Command Prompts
Image Generation
Real-Time Trace Panel
Streaming SSE
Capability-Aware Gating
Supply Chain Risk Analysis
A procurement analyst asks: "Summarise the top 5 supply chain risks in our Q3 supplier reports and flag any with delivery impact over 14 days."
User types query - model is Claude, web_search and vector_search tools attached.
Trace panel shows : LLM decides to call vector_search("Q3 supplier reports delivery risk").
MCP Hub routes to RAG Studio knowledge base - retrieves 8 relevant chunks with citations.
Model receives chunks, calls web_search("current logistics disruptions 2025").
Model synthesises both sources - streams ranked risk list with citations back to user.
Trace panel shows full call graph, 1,847 tokens, 3.2s end-to-end, trace ID for audit.
Supply Chain Risk Analysis
User types query - model is Claude, web_search and vector_search tools attached
User types query - model is Claude, web_search and vector_search tools attached.
Trace panel shows : LLM decides to call vector_search("Q3 supplier reports delivery risk").
MCP Hub routes to RAG Studio knowledge base - retrieves 8 relevant chunks with citations.
Model receives chunks, calls web_search("current logistics disruptions 2025").
Model synthesises both sources - streams ranked risk list with citations back to user.
Trace panel shows full call graph, 1,847 tokens, 3.2s end-to-end, trace ID for audit.
Supply Chain Risk Analysis
User types query - model is Claude, web_search and vector_search tools attached
User types query - model is Claude, web_search and vector_search tools attached.
Trace panel shows : LLM decides to call vector_search("Q3 supplier reports delivery risk").
MCP Hub routes to RAG Studio knowledge base - retrieves 8 relevant chunks with citations.
Model receives chunks, calls web_search("current logistics disruptions 2025").
Model synthesises both sources - streams ranked risk list with citations back to user.
Trace panel shows full call graph, 1,847 tokens, 3.2s end-to-end, trace ID for audit.
MCP Hub
The nervous system of your AI infrastructure. Aliving, dynamic registry where every capability the platform produces and
every external tool you connect becomes a versioned, authenticated, observable MCP tool available to everything else.
MCP Hub
The nervous system of your AI infrastructure. A living, dynamic registry where every capability the platform produces and
every external tool you connect becomes a versioned, authenticated, observable MCP tool available to everything else.
MCP Hub
The nervous system of your AI infrastructure. A living, dynamic registry where every capability the platform produces and every external tool you connect becomes a versioned, authenticated, observable MCP tool available to everything else.
Dynamic Tool Registry
Dynamic Tool Registry
External MCP Server Federation
External MCP Server Federation
JWT-Scoped Authentication
JWT-Scoped Authentication
Testing Console
Testing Console
Three Foundational Tools
Three Foundational Tools
Semver Pinning
Semver Pinning
Schema Validation
Schema Validation
Streamable HTTP Transport
Streamable HTTP Transport
A RAG Bot Becoming a Tool
A Builder publishes "Product Catalogue Bot v2.1.0" in RAG Studio. Within seconds, the MCP Hub automatically creates a callable tool.
Builder hits "Publish" on Product Catalogue Bot — eval gates pass (94% accuracy)
MCP Hub receives publish event — validates schema, assigns URI: mcp://rag/product-catalogue@2.1.0
Tool is immediately available in the registry — visible to Chat, Agents, Workflows
Procurement agent calls it: product_catalogue_bot(query="aluminium grade 6061 current stock")
Returns: {answer, citations, confidence: 0.97, trace_id, latency_ms: 1240, tokens_used}
Agent uses the answer, audit log records the full call chain with user identity
A RAG Bot Becoming a Tool
A Builder publishes "Product Catalogue Bot v2.1.0" in RAG Studio. Within seconds,
the MCP Hub automatically creates a callable tool.
Builder hits "Publish" on Product Catalogue Bot — eval gates pass (94% accuracy)
MCP Hub receives publish event — validates schema, assigns URI: mcp://rag/product-catalogue@2.1.0
Tool is immediately available in the registry — visible to Chat, Agents, Workflows
Procurement agent calls it: product_catalogue_bot(query="aluminium grade 6061 current stock")
Returns: {answer, citations, confidence: 0.97, trace_id, latency_ms: 1240, tokens_used}
Agent uses the answer, audit log records the full call chain with user identity
A RAG Bot Becoming a Tool
A Builder publishes "Product Catalogue Bot v2.1.0" in RAG Studio. Within seconds, the MCP Hub automatically creates a callable tool.
Builder hits "Publish" on Product Catalogue Bot — eval gates pass (94% accuracy)
MCP Hub receives publish event — validates schema, assigns URI: mcp://rag/product-catalogue@2.1.0
Tool is immediately available in the registry — visible to Chat, Agents, Workflows
Procurement agent calls it: product_catalogue_bot(query="aluminium grade 6061 current stock")
Returns: {answer, citations, confidence: 0.97, trace_id, latency_ms: 1240, tokens_used}
Agent uses the answer, audit log records the full call chain with user identity
RAG Studio
Ground every answer in your reality. Connect any data source, build a production - grade knowledge base, and
deploy an intelligent retrieval assistant -all without writing a line of code. Then watch it become a toll every module can call.
RAG Studio
Ground every answer in your reality. Connect any data source, build a production - grade knowledge base, and
deploy an intelligent retrieval assistant -all without writing a line of code. Then watch it become a toll every module can call.
RAG STUDIO
Ground every answer in your reality. Connect any data source, build a production - grade knowledge base, and deploy an intelligent retrieval assistant -all without writing a line of code. Then watch it become a toll every module can call.
No-Code Knowledge Base Builder
No-Code Knowledge Base Builder
Two Knowledge Base Modes
Two Knowledge Base Modes
Mandatory Eval Gates
Mandatory Eval Gates
Retrieval Transparency
Retrieval Transparency
Broad Source Connectivity
Broad Source Connectivity
ACL Propagation
ACL Propagation
Semver Lifecycle
Semver Lifecycle
Smart Refresh
Smart Refresh
Engineering Standards Knowledge Base
An engineer asks the Engineering Standards Bot: "What are the approved weld specifications
\for ASTM A36 steel in load-bearing assemblies?"
Query hits RAG Studio query engine — user identity checked against Permission-Aware ACL
Vector search retrieves top-12 chunks from engineering standards corpus (pgvector)
ACL filter removes 3 chunks from restricted project files user lacks access to
Re-ranker scores remaining 9 chunks by semantic relevance to the query
Claude generates answer grounded in top-4 chunks with inline citations and standard codes
Engineering Standards Knowledge Base
An engineer asks the Engineering Standards Bot: "What are the approved weld specifications for ASTM A36 steel in load-bearing assemblies?"
Query hits RAG Studio query engine — user identity checked against Permission-Aware ACL
Vector search retrieves top-12 chunks from engineering standards corpus (pgvector)
ACL filter removes 3 chunks from restricted project files user lacks access to
Re-ranker scores remaining 9 chunks by semantic relevance to the query
Claude generates answer grounded in top-4 chunks with inline citations and standard codes
Engineering Standards Knowledge Base
An engineer asks the Engineering Standards Bot: "What are the approved weld specifications for ASTM A36 steel in load-bearing assemblies?"
Query hits RAG Studio query engine — user identity checked against Permission-Aware ACL
Vector search retrieves top-12 chunks from engineering standards corpus (pgvector)
ACL filter removes 3 chunks from restricted project files user lacks access to
Re-ranker scores remaining 9 chunks by semantic relevance to the query
Claude generates answer grounded in top-4 chunks with inline citations and standard codes
AI Agent Builder
Define the goal. Trust the intelligence. Build autonomous AI agents that observe results, re-evaluate context, and
act in loops until they achieve their objective - using your RAG knowledge bases and MCP tolls as their capabilities.
AI Agent Builder
Define the goal. Trust the intelligence. Build autonomous AI agents that observe results, re-evaluate context, and
act in loops until they achieve their objective - using your RAG knowledge bases and MCP tolls as their capabilities.
AI AGENT BUILDER
Define the goal. Trust the intelligence. Build autonomous AI agents that observe results,
re-evaluate context, and act in loops until they achieve their objective - using your RAG knowledge bases and MCP tolls as their capabilities.
Goal-Driven Reasoning
Goal-Driven Reasoning
RAG Bots as Knowledge Tools
RAG Bots as Knowledge Tools
Human-in-the-Loop Checkpoints
Human-in-the-Loop Checkpoints
Eval & Observability
Eval & Observability
Full MCP Tool Access
Full MCP Tool Access
Observation Loop
Observation Loop
Published as MCP Tools
Published as MCP Tools
Procurement Compliance Agent
Goal: "Review all purchase orders over $50,000 submitted in the last 7 days and flag any that deviate from our approved supplier list or payment terms."
Agent calls vector_search("approved supplier list current") via MCP — gets supplier registry from RAG bot
Agent calls database_query("SELECT * FROM purchase_orders WHERE amount > 50000 AND date > NOW()-7d")
Agent cross-references each PO against supplier list — identifies 3 anomalies
Human-in-the-loop checkpoint: "3 POs flagged. Review before sending alerts?" — User approves
Agent calls send_alert tool — generates detailed compliance report with references and recommended actions
Full reasoning trace logged — every decision, every tool call, every observation — for audit
Procurement Compliance Agent
Goal: "Review all purchase orders over $50,000 submitted in the last 7 days and flag any that deviate from our approved supplier list or payment terms."
Agent calls vector_search("approved supplier list current") via MCP — gets supplier registry from RAG bot
Agent calls database_query("SELECT * FROM purchase_orders WHERE amount > 50000 AND date > NOW()-7d")
Agent cross-references each PO against supplier list — identifies 3 anomalies
Human-in-the-loop checkpoint: "3 POs flagged. Review before sending alerts?" — User approves
Agent calls send_alert tool — generates detailed compliance report with references and recommended actions
Full reasoning trace logged — every decision, every tool call, every observation — for audit
Procurement Compliance Agent
Goal: "Review all purchase orders over $50,000 submitted in the last 7 days and flag any that deviate fromour approved supplier list or payment terms."
Agent calls vector_search("approved supplier list current") via MCP — gets supplier registry from RAG bot
Agent calls database_query("SELECT * FROM purchase_orders WHERE amount > 50000 AND date > NOW()-7d")
Agent cross-references each PO against supplier list — identifies 3 anomalies
Human-in-the-loop checkpoint: "3 POs flagged. Review before sending alerts?" — User approves
Agent calls send_alert tool — generates detailed compliance report with references and recommended actions
Full reasoning trace logged — every decision, every tool call, every observation — for audit
WORKFLOW BUILDER
Precious meets intelligence at scale, Design production workflows that combine deterministic business logic with
AI agents and knowledge retrieval -backed by durable execution that survives outages, retries failure , and honours schedules without exception.
Workflow Builder
Precious meets intelligence at scale, Design production workflows that combine deterministic business logic with
AI agents and knowledge retrieval -backed by durable execution that survives outages, retries failure , and honours
schedules without exception.
WORKFLOW BUILDER
Precious meets intelligence at scale, Design production workflows that combine deterministic business logic with
AI agents and knowledge retrieval -backed by durable execution that survives outages, retries failure , and honours schedules without exception.
Durable Execution
Durable Execution
Heterogeneous Nodes
Heterogeneous Nodes
Visual No-Code Canvas
Visual No-Code Canvas
Published as MCP Tools
Published as MCP Tools
Flexible Triggers
Flexible Triggers
Deterministic + AI in One Flow
Deterministic + AI in One Flow
Retry & Error Handling
Retry & Error Handling
Automated Monthly Quality Report
"Trigger: 1st of every month at 06:00 UTC. Goal: Compile quality metrics, analyse trendS generate executive summary, distribute to stakeholders.
Temporal triggers workflow — fetches quality metrics from ERP database(deterministic MCP tool call)
Parallel fork: simultaneously queries defect trends (DB) and quality standards RAG bot
Both branches complete — results merged and passed to Quality Analysis Agent
Agent analyses trends, identifies root causes, generates recommendations with citations
Conditional: if critical issue flagged → immediate alert → human review gate before distribution
Formatted report emailed to distribution list — OTel trace logged for full auditability
Automated Monthly Quality Report
"Trigger: 1st of every month at 06:00 UTC. Goal: Compile quality metrics, analyse trends,
generate executive summary, distribute to stakeholders.
Temporal triggers workflow — fetches quality metrics from ERP database (deterministic MCP tool call)
Parallel fork: simultaneously queries defect trends (DB) and quality standards RAG bot
Both branches complete — results merged and passed to Quality Analysis Agent
Agent analyses trends, identifies root causes, generates recommendations with citations
Conditional: if critical issue flagged → immediate alert → human review gate before distribution
Formatted report emailed to distribution list — OTel trace logged for full auditability
Automated Monthly Quality Report
"Trigger: 1st of every month at 06:00 UTC. Goal: Compile quality metrics, analyse trends, generate executive summary, distribute to stakeholders.
Temporal triggers workflow — fetches quality metrics from ERP database (deterministic MCP tool call)
Parallel fork: simultaneously queries defect trends (DB) and quality standards RAG bot
Both branches complete — results merged and passed to Quality Analysis Agent
Agent analyses trends, identifies root causes, generates recommendations with citations
Conditional: if critical issue flagged → immediate alert → human review gate before distribution
Formatted report emailed to distribution list — OTel trace logged for full auditability
MULTI AGENT NETWORK
Describe the problem. The network self-assembles. The most advanced AI capability on the market - a platform that translates a natural language
problem description into a coordinated network of specialist agents, each equipped with its own tools and knowledge, working in concert toward a shared objective.
Multi Agent Network
Describe the problem. The network self-assembles. The most advanced AI capability on the market - a platform that translates a natural language
problem description into a coordinated network of specialist agents, each equipped with its own tools and knowledge, working in concert toward a shared objective.
MULTI AGENT NETWORK
Describe the problem. The network self-assembles. The most advanced AI capability on the market - a platform that translates a natural language
problem description into a coordinated network of specialist agents, each equipped with its own tools and knowledge, working in concert toward a shared objective.
Natural Language Network Design
Natural Language Network Design
Specialist Agents as Nodes
Specialist Agents as Nodes
Dynamic Task Routing
Dynamic Task Routing
Custom Graph Runtime
Custom Graph Runtime
Visual Graph Composer
Visual Graph Composer
Supervisor-Specialist Pattern
Supervisor-Specialist Pattern
Shared Memory & State
Shared Memory & State
End-to-End New Supplier Onboarding
User types: "I need to onboard a new steel supplier from South Korea — handle compliance checks, financial risk assessment, and contract preparation."
NL Network Designer proposes: Supervisor + Compliance Agent + Financial Risk Agent + Contract Agent
User reviews graph, adds Regulatory Agent for import compliance — publishes network
Supervisor routes: Compliance checks → Compliance Agent (uses Supplier Standards RAG + web search)
Parallel: Financial Risk Agent queries credit bureau API + analyses financial RAG knowledge base
Contract Agent drafts supplier agreement using Contract Templates RAG + all prior agent findings
Human approval gate — legal reviews draft — approved — supplier onboarding package delivered
End-to-End New Supplier Onboarding
User types: "I need to onboard a new steel supplier from South Korea — handle compliance checks, financial risk assessment, and contract preparation."
NL Network Designer proposes: Supervisor + Compliance Agent + Financial Risk Agent + Contract Agent
User reviews graph, adds Regulatory Agent for import compliance — publishes network
Supervisor routes: Compliance checks → Compliance Agent (uses Supplier Standards RAG + web search)
Parallel: Financial Risk Agent queries credit bureau API + analyses financial RAG knowledge base
Contract Agent drafts supplier agreement using Contract Templates RAG + all prior agent findings
Human approval gate — legal reviews draft — approved — supplier onboarding package delivered
End-to-End New Supplier Onboarding
User types: "I need to onboard a new steel supplier from South Korea — handle compliance checks, financial risk assessment, and contract preparation."
NL Network Designer proposes: Supervisor + Compliance Agent + Financial Risk Agent + Contract Agent
User reviews graph, adds Regulatory Agent for import compliance — publishes network
Supervisor routes: Compliance checks → Compliance Agent (uses Supplier Standards RAG + web search)
Parallel: Financial Risk Agent queries credit bureau API + analyses financial RAG knowledge base
Contract Agent drafts supplier agreement using Contract Templates RAG + all prior agent findings
Human approval gate — legal reviews draft — approved — supplier onboarding package delivered
THE ONLY PLATFORM
THE ONLY PLATFORM That Does All Of It
The Only Platform That Does All Of It
That Does All Of It
Every competitor has picked a wedge. A chat interface. A RAG builder. An agent framework. A workflow tool. CCG AI Playground is the only
platform in the market that unifies all six capabilities under one MCP-native, enterprise-governed architecture - deployed in your cloud, not ours.
Every competitor has picked a wedge. A chat interface. A RAG builder. An agent framework. A workflow tool. CCG AI Playground is the only platform in the market that unifies all six capabilities under one MCP-native, enterprise-governed architecture - deployed in your cloud, not ours.
Every competitor has picked a wedge. A chat interface. A RAG builder. An agent framework. A workflow tool. CCG AI Playground is the only platform in the market that unifies all six capabilities under one MCP-native, enterprise-governed architecture - deployed in your cloud, not ours.
SECURITY ISN'T A FEATURE.
SECURITY ISN'T A FEATURE.
Security Isn't A Feature. It's The Foundation!
IT'S THE FOUNDATION!
IT'S THE FOUNDATION!
CCG NEXUS AI is engineered for enterprise environments where security, compliance, and data sovereignty are non-negotiable.
Every design decision — from how we store secrets to how we log tool calls — was made with an enterprise security team's requirements as the primary constraint.
CCG NEXUS AI is engineered for enterprise environments where security, compliance, and data sovereignty are non-negotiable. Every design decision — from how we store secrets to how we log tool calls — was made with an enterprise security team's requirements as the primary constraint.
CCG NEXUS AI is engineered for enterprise environments where security, compliance, and data sovereignty are non-negotiable. Every design decision — from how we store secrets to how we log tool calls — was made with an enterprise security team's requirements as the primary constraint.
Swipe Right to see Before & After
Swipe Right to see Before & After
Swipe Right to see Before & After
Build Once. Compose Everywhere. Deploy in Your Cloud.
The only AI platform where every capability compounds — where a knowledge base becomes an agent tool, an agent becomes a workflow node, and a workflow becomes part of a self-orchestrating intelligence network. All governed, all observable, all yours.
Build Once. Compose Everywhere. Deploy in Your Cloud.
The only AI platform where every capability compounds — where a knowledge base becomes an agent tool, an agent becomes a workflow node, and a workflow becomes part of a self-orchestrating intelligence network. All governed, all observable, all yours.
©2026 BY CRESCENZA CONSULTING GROUP | ALL RIGHTS RESERVED
sales@crescenzaconsulting.ca
The AI Platform That Thinks in Networks
Most AI platforms give you a chatbot or a workflow engine. CCG Nexus AI gives you the entire stack — from a single intelligent conversation to a self-orchestrating network of specialist AI agents — unified under one coherent, composable architecture where every artifact you build becomes a tool the next module can call.
CCG Nexus AI is a Full-stack enterprise Generative AI platform that runs entirely within your organisation's cloud boundary. Every conversation, every knowledge base, every agent, every automated workflow — it all lives in your environment, governed by your rules, secured with your keys.
At its core is a radical design principle borrowed from the Model Context Protocol (MCP): every capability the platform produces — a RAG bot, an agent, a workflow — is automatically exposed as a versioned, callable tool that any other module can consume. Build a knowledge base in RAG Studio and it instantly becomes a tool your agents can reason over. Build an agent and it immediately becomes a node your workflows can orchestrate. The composability is structural, not stitched together.
The result is a platform that starts delivering value from day one with intelligent chat and knowledge retrieval, and grows into a fully autonomous multi-agent intelligence layer without changing architecture. One platform. One mental model. Unlimited capability.
Composable by Design
Every artifact — RAG bots, agents, workflows — automatically becomes a versioned MCP tool callable by any other module. No integration code. No data pipelines. Pure composition.
Sovereign Deployment
Deployed entirely within your cloud subscription. Your data, your keys, your infrastructure. Zero egress to CCG. Complete organisational sovereignty over every byte.
No-Code to Pro-Code
Business users build production RAG bots in minutes. Developers extend with custom MCP tools and agents. Everyone operates in the same governed, observable platform.
Model-Agnostic
Works with Claude, Chat GPT, Gemini, Grok, Llama, and any model on Azure AI Foundry, AWS Bedrock, or Google Vertex — switch models without changing architecture.