Multi-Tenant Generative AI Routing for Tenant-Specific Queries
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Solution Overview
Problem
Existing multi-tenant database systems and generative AI systems struggle to accommodate diverse tenant-specific considerations, leading to inefficient query handling and response across multiple tenants.
Innovation Solution
A unified multi-tenant generative AI system that adapts to tenant-specific configurations and training data, enabling customized query handling and response through a centralized platform with tenant-specific AI models and communication services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a unified multi-tenant generative AI system is implemented, then adaptability across multiple tenants is improved, but device complexity increases
Solution Approach 1:
The system segments tenant data and configurations through multi-tenant database architecture, isolating each tenant's training data, model parameters, and communication channels while maintaining a unified AI infrastructure. This allows the system to serve multiple tenants with different requirements without requiring separate systems for each tenant.
Solution Approach 2:
The generative AI system is designed as a universal platform that can accommodate multiple tenants with different industries, data types, and operational requirements through a single unified architecture. The system provides multi-functional capabilities including query processing, response generation, and tenant-specific customization within one integrated framework.
2Measurement precision
If tenant-specific training data is used, then response accuracy for each tenant is improved, but data management complexity increases
Solution Approach 1:
The system implements separate data storage and processing segments for each tenant, ensuring that training data for one tenant does not interfere with another tenant's data. Tenant-specific training data is isolated in dedicated database tables and processed through separate model instances, maintaining data security while enabling personalized accuracy.
Solution Approach 2:
The system creates and maintains copies of tenant-specific training data and model parameters within the unified platform. Each tenant has their own copied training data that is processed independently through the generative AI model, allowing accurate responses tailored to each tenant without requiring direct access to other tenants' data.
3Ease of operation
If multiple communication channels are supported, then ease of operation is improved, but device complexity increases
Solution Approach 1:
The system implements a universal communication interface that supports multiple communication channels (e.g., REST API, gRPC, WebSocket) through a single unified architecture. The same generative AI model and processing logic serve all communication channels, allowing tenants to access the system through their preferred channel without requiring separate implementations for each channel type.
Data Source
AI summary
A system may receive a configuration associated with a tenant of a multi-tenant generative artificial intelligence (AI) system and tenant-specific training data, where the configuration includes a first indication of a first communication channel over which a tenant-specific conversational agent is to communicate with users and where the tenant-specific training data includes context information associated with the tenant that is expressed in natural language. The system may determine an intent of a query received from the tenant based at least in part on an analysis of the query. The system may transmit the query to a first generative AI model of a plurality of generative AI models, wherein the first generative AI model is selected based at least in part on the determined intent. The system may transmit, to the tenant over the first communication channel, a response to the query generated by the first generative AI model.