Overview
Portkey is a production stack designed for GenAI builders, centralizing LLM access, governance, observability, prompt management, caching, and cost controls through a unified API and developer SDKs. It aims to streamline the development and management of GenAI applications by providing a comprehensive platform for LLM orchestration and governance.
Key Features:
- Unified AI Gateway for accessing over 1,600 LLMs.
- Real-time observability dashboard for monitoring LLM behavior and managing usage.
- Automated PII redaction for sensitive data.
- Real-time performance and cost monitoring with budget limits.
- Comprehensive platform including AI Gateway, Guardrails, Governance, and Prompt Management.
- MCP Gateway for managing MCP servers.
Use Cases:
- Building and deploying GenAI applications.
- Monitoring and managing LLM performance and behavior.
- Optimizing AI infrastructure costs.
- Ensuring data privacy and security in LLM interactions.
Benefits:
- Focus on building by abstracting LLM management.
- Proactive management and early anomaly detection.
- Significant cost savings and optimized resource allocation.
- Rapid integration with existing stacks using minimal code.
Capabilities
- Automates responses to frequently asked customer questions
- Resolves customer inquiries, achieving up to 70% resolution without human intervention
- Provides instant answers to customer questions in under 6 seconds
- Performs task automation such as checking order statuses and creating support tickets
- Recommends products directly from integrated Shopify inventories
- Manages customer support across multiple live communication channels
- Responds to customer queries in multiple languages, including English, Spanish, French, German, and Portuguese
- Integrates seamlessly with existing support solutions through Lyro Connect
- Continuously learns and updates its knowledge base as business data grows
- Notifies support agents when human assistance is specifically requested
- Analyzes customer queries to deliver human-like responses
- Adapts and improves response accuracy through interaction-based learning
- Monitors and evaluates AI agent performance in real-time for optimization
- Customizes behavior and responses to align with brand guidelines
- Automatically builds a knowledge base by scraping and analyzing support content
- Generates support tickets for inquiries beyond its knowledge base
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