The **Client Communication Management Application** is a multi-tenant platform designed to help businesses manage customer interactions through AI-powered communication channels.

The application provides:

- A dedicated **client dashboard** for project setup, knowledge base upload, and communication channel configuration.

- A **customer-facing AI interface** (chat page, Telegram bot, or email integration) where end-users can interact with the AI assistant.

- An **admin dashboard** to monitor, review, and override AI responses when necessary.

The platform supports multiple AI connectors, enabling flexibility for businesses to choose the best model for their needs. Clients can seamlessly manage communication, while administrators maintain oversight and control over all conversations.

- **Customer-facing AI bot interface**: chat page embedded in client’s site, Telegram, email, etc.

- **API layer: Standard** Go HTTP server with Gin for routing.

- Ollama.

- Handles authentication, routing, and configuration for both **local** and **remote** models.

- Decouples application logic from model APIs, enabling **dynamic model switching**.

- Standardizes inference requests and responses for consistent performance.

- Forms the foundation for future **multi-model orchestration** and **context-aware AI pipelines**.

- Customer submits query → AI pipeline:

1. Normalize input.

2. Retrieve context from Knowledge Base.

3. Feed context + query to chosen AI model.

4. Return AI response.

- Option for admin override & monitoring.

- View all conversations per client/project.

- Override/edit AI responses.

- Track usage per channel/model.

- Analytics & logs.

- **`accounts`**

Stores client/organization information.

**Fields:**

- `id` (string, PK)

- `name` (varchar(150))

- `domain` (varchar(100))

- `description` (text)

- **`workspaces`**

Stores project metadata linked to an account.

**Fields:**

- `id` (string, PK)

- `account_id` (string, FK → accounts.id)

- `name` (varchar(150))

- `description` (text)

- **`users`**

Stores basic user profile information.

**Fields:**

- `id` (string, PK)

- `username` (varchar(50))

- `full_name` (varchar(150))

- `email` (varchar(150))

- `phone_number` (varchar(20))

- `password_hash` (varchar(255))

- `auth_token` (varchar(500))

- **`user_accounts`**

Maps users to accounts and optionally workspaces; stores roles and status.

**Fields:**

- `id` (string, PK)

- `user_id` (string, FK → users.id)

- `account_id` (string, FK → accounts.id)

- `workspace_id` (string, nullable, FK → workspaces.id)

- `role` (varchar(50))

- `status` (varchar(50))

- **`workspace_users`**

Maps users to workspaces for workspace-specific roles/status.

**Fields:**

- `id` (string, PK)

- `user_id` (string, FK → users.id)

- `workspace_id` (string, FK → workspaces.id)

- `role` (varchar(50))

- `status` (varchar(50))

- **`groups`**

Represents functional teams or user groups within a workspace.

**Fields:**

- `id` (string, PK)

- `workspace_id` (string, FK → workspaces.id)

- `name` (varchar(100))

- `description` (text)

- **`group_users`**

Maps users to groups.

**Fields:**

- `id` (string, PK)

- `group_id` (string, FK → groups.id)

- `user_id` (string, FK → users.id)

- `role` (varchar(50))

- **`channels`**

Configured communication channels within a workspace.

**Fields:**

- `id` (string, PK)

- `workspace_id` (string, FK → workspaces.id)

- `type` (varchar(50))

- `config` (jsonb)

- `description` (text)

- **`ai_models`**

Available AI model connectors for accounts.

**Fields:**

- `id` (string, PK)

- `account_id` (string, FK → accounts.id)

- `name` (varchar(100))

- `ai_type` (varchar(50))

- `version` (varchar(50))

- `config` (jsonb)

- `description` (text)

- **`ai_agents`**

Agents linked to a workspace and a single channel; connect to AI models.

**Fields:**

- `id` (string, PK)

- `workspace_id` (string, FK → workspaces.id)

- `channel_id` (string, unique, FK → channels.id)

- `ai_model_id` (string, FK → ai_models.id)

- `name` (varchar(100))

- `status` (varchar(50))

- `description` (text)

- **`mcp_connections`**

Stores Message Coordination Processor connection configuration.

**Fields:**

- `id` (string, PK)

- `workspace_id` (string, FK → workspaces.id)

- `type` (varchar(50))

- `config` (jsonb)

- `description` (text)

- **`mcp_logs`**

Stores request and response logs for MCP connections.

**Fields:**

- `id` (string, PK)

- `mcp_connection_id` (string, FK → mcp_connections.id)

- `request_data` (jsonb)

- `response_data` (jsonb)

- `status` (varchar(50))

- `log_timestamp` (timestamp)

- **`knowledge_bases`**

Stores knowledge base entries linked to workspaces.

**Fields:**

- `id` (string, PK)

- `workspace_id` (string, FK → workspaces.id)

- `title` (varchar(200))

- `description` (text)

- **`kb_files`**

Stores files uploaded to a knowledge base.

**Fields:**

- `id` (string, PK)

- `knowledge_base_id` (string, FK → knowledge_bases.id)

- `workspace_id` (string, FK → workspaces.id)

- `user_id` (string, FK → users.id)

- `file_name` (varchar(255))

- `file_path` (text)

- `mime_type` (varchar(100))

- `file_size` (bigint)

- **`kb_file_chunks`**

Stores content chunks for each knowledge base file, along with vector embeddings.

**Fields:**

- `id` (string, PK)

- `kb_file_id` (string, FK → kb_files.id)

- `chunk_index` (int)

- `content` (text)

- `vector_embedding` (text)

- **`tasks`**

Tracks tasks created in a workspace (optionally linked to a conversation).

**Fields:**

- `id` (string, PK)

- `workspace_id` (string, FK → workspaces.id)

- `agent_id` (string, FK → ai_agents.id)

- `user_id` (string, FK → users.id)

- `conversation_id` (string, nullable, FK → conversations.id)

- `title` (varchar(200))

- `description` (text)

- `status` (varchar(50))

- **`task_activity`**

Logs actions and updates performed on tasks.

**Fields:**

- `id` (string, PK)

- `task_id` (string, FK → tasks.id)

- `action` (varchar(100))

- `performed_by` (string)

- `details` (jsonb)

- `activity_timestamp` (timestamp)

- **`conversations`**

Stores conversation metadata within a workspace.

**Fields:**

- `id` (string, PK)

- `workspace_id` (string, FK → workspaces.id)

- `channel_id` (string, FK → channels.id)

- `agent_id` (string, FK → ai_agents.id)

- `title` (varchar(200))

- `status` (varchar(50))

- **`messages`**

Stores messages in a conversation, including prompts and memory/archive info.

**Fields:**

- `id` (string, PK)

- `conversation_id` (string, FK → conversations.id)

- `user_id` (string, FK → users.id)

- `sender_type` (varchar(50))

- `prompt_id` (string, nullable)

- `content` (text)

- `is_memory_archived` (bool)

- `metadata` (jsonb)

- **`memories`**

Stores memory entries for agents within a conversation.

**Fields:**

- `id` (string, PK)

- `agent_id` (string, FK → ai_agents.id)

- `conversation_id` (string, FK → conversations.id)

- `memory_type` (varchar(50))

- `content` (text)

- **`invites`**

Stores workspace invitation records for users.

**Fields:**

- `id` (string, PK)

- `workspace_id` (string, FK → workspaces.id)

- `email` (varchar(150))

- `role` (varchar(50))

- `token` (varchar(255))

- `status` (varchar(50))

- `invited_by` (string, FK → users.id)

- `expires_at` (timestamp)

- **AI APIs** → OpenAI, Gemini, etc.

The platform is designed with scalability in mind, allowing integration of new features and channels to enhance client communication and management. Planned future improvements include:

- Linking AI conversations to tasks and workflows.

- Syncing client issues directly into project management tools.

- **Internal Ticketing System**

- Native ticket creation and tracking within the platform.

- Escalation workflows and admin assignment for unresolved queries.

- **Sentiment & Intent Analysis**

- Understanding customer tone and urgency for better prioritization.

- **Personalized AI Responses**

- AI models trained on client-specific style guides and communication tone.

- Advanced reporting on client queries, response times, and AI performance.

- Predictive analytics for identifying recurring issues or potential churn risks.

- Exportable insights for stakeholders.

The first demo of **CommNet** establishes the **core multi-tenant communication foundation** and **essential management modules**.

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- Multi-tenant user and organization setup.

- Support for pluggable AI model configurations.

- Link AI models to specific workspaces.

- Store, validate, and monitor MCP configurations.

- Webhook-based message handling integrated with MCP and AI pipelines.

- Configure model bindings, activation states, and AI routing logic.

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- Store embeddings using **pgvector** for contextual **RAG** responses.

- Support integration with **Google Drive** and other data sources.

- Store conversation history, enable admin override, and manage AI memory.

- Assign and track tasks within workspace groups.

- Provide activity logs and notifications.

- Standardized message orchestration across all channels.