The Next-Gen AI-Enhanced Customer Engagement Platform empowers companies to create and deploy their own AI agents using generative AI systems like ChatGPT, Gemini, or LLaMA3. These AI agents can be embedded into various interfaces on the companies' platforms, providing dynamic and intelligent customer support, sales assistance, and other communication services.
- User Management: Admins can manage companies.
- Agent Management: Admins can monitor and control AI agents created by companies and manage end-users who interact with the agents.
- Analytics: Admins can view usage statistics, performance metrics, and other relevant data.
- Agent Configuration: Companies can create and configure their AI agents, feeding them specific data and knowledge.
- Agent Management: Companies can manage their AI agents, including updating the knowledge base and setting interaction protocols.
- User Management: Companies can view and manage end-users who interact with their agents.
- Analytics: Companies can access detailed analytics about their agents' performance and user interactions.
- JavaScript Widget: An embeddable JavaScript widget that companies can integrate into their platforms.
- Visual Interfaces: Various user-friendly interfaces for end-users to interact with the AI agent, such as chat boxes and Q/A formats similar to Zendesk.
- Real-time Interaction: The AI agent responds to user queries in real-time using the knowledge fed by the company.
- Framework: React.js
- Components: User management, agent management, analytics dashboards
- APIs: Interact with backend services for managing companies, agents, and end-users
- Framework: React.js
- Components: Agent configuration, user management, analytics dashboards
- APIs: Interact with backend services for managing agents and viewing analytics
- Framework: Vanilla JavaScript/React.js
- Components: Various interaction UIs (chat box, Q/A format, etc.)
- APIs: Interact with backend AI services to fetch responses
- User Management Service: Handles CRUD operations for admin and company accounts.
- Agent Management Service: Manages the creation, configuration, and deployment of AI agents.
- Knowledge Base Service: Stores and retrieves knowledge fed by companies.
- AI Integration Service: Interfaces with generative AI models like ChatGPT, Gemini, or LLaMA3 to process user queries and generate responses.
- Retrieval-Augmented Generation (RAG) Service: Utilizes LLaMA Index or LangChain to enhance the AI agent's responses with relevant information from the knowledge base.
- Analytics Service: Collects and processes data for usage statistics and performance metrics.
- User Database: Stores information about admins and companies.
- Agent Database: Stores configuration details and metadata of AI agents.
- Knowledge Base: Stores the data and knowledge fed by companies.
- Analytics Database: Stores interaction logs, usage statistics, and performance metrics.
- Short-term Memory Store: Redis, used for storing short-term memory of each end-user of each agent with a TTL of 1 day.
- Cloud Provider: AWS/GCP/Azure
- Services: EC2/VMs, S3/Blob Storage, RDS/Cloud SQL, Redis for short-term memory storage
- Authentication: OAuth2.0, JWT
- Authorization: Role-based access control (RBAC)
- Data Encryption: TLS/SSL for data in transit, AES for data at rest
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Endpoints:
POST /admin/login: Authenticate an admin.GET /admin/companies: Retrieve a list of all companies.POST /admin/companies: Create a new company.DELETE /admin/companies/{companyId}: Delete a company.GET /admin/agents: Retrieve a list of all AI agents.GET /admin/agents/{agentId}/users: Retrieve a list of all end-users for a specific AI agent.GET /admin/analytics: Retrieve platform-wide analytics.
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Authentication: Admin-level access tokens
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Endpoints:
POST /company/login: Authenticate a company user.GET /company/agents: Retrieve a list of all AI agents for the company.POST /company/agents: Create a new AI agent.PUT /company/agents/{agentId}: Update an AI agent.DELETE /company/agents/{agentId}: Delete an AI agent.POST /company/agents/{agentId}/knowledge: Update the knowledge base for an AI agent.GET /company/agents/{agentId}/users: Retrieve a list of all end-users for a specific AI agent.GET /company/analytics: Retrieve analytics for the company's agents.
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Authentication: Company-level access tokens
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Endpoints:
POST /rag/agents/{agentId}/index: Index new knowledge for the AI agent using LLaMA Index or LangChain.GET /rag/agents/{agentId}/search: Perform a search query on the knowledge base to retrieve relevant information.PUT /rag/agents/{agentId}/knowledge: Update the indexed knowledge base for an AI agent.
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Authentication: Company-level access tokens
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Endpoints:
POST /agent/query: Process a user query and generate a response.POST /agent/events: Log interaction events for analytics.
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Authentication: API keys for each embedded agent
- Admin logs into the portal.
- Admin manages companies and their agents, and end-users through the portal.
- Admin views analytics and usage statistics.
- Company signs up and logs into the portal.
- Company creates and configures their AI agent, feeding it specific data and knowledge.
- Company embeds the AI agent on their landing page using the provided JavaScript widget.
- Company monitors and manages the AI agent and views analytics.
- End-user visits the company's landing page.
- The embedded AI agent visual interface (chat box, Q/A format, etc.) appears.
- End-user interacts with the AI agent, asking questions.
- The AI agent processes the queries and responds in real-time using the knowledge fed by the company.
- The short-term memory of the end-user's interaction is stored in Redis with a TTL of 1 day.
This system design outlines a scalable and efficient next-gen platform providing SaaS products related to customer support, sales, and other communications using generative AI. By offering dedicated portals for admins and companies, and versatile AI agent interfaces for end-users, the platform aims to enhance user engagement and streamline business processes.
"Empowering Customer Engagement with Intelligent AI Solutions"