Deploy intelligent AI agents with advanced retrieval, multi-LLM support, and enterprise-grade features. All in one powerful platform.
Production-grade capabilities designed for real-world AI applications
Fine-tune every aspect of your RAG system through our API. Adjust chunking strategies, embedding models, reranking algorithms, and retrieval parameters to optimize for your specific use case.
await skald.chat({
query: 'What are our Q4 goals?',
rag_config: {
llmProvider: 'anthropic',
queryRewrite: { enabled: true },
vectorSearch: {
topK: 20,
similarityThreshold: 0.5
},
reranking: {
enabled: true,
topK: 10
},
references: { enabled: true }
}
});We handle the entire ingestion pipeline for you. From extracting text, tables, and structure from PDFs and Word documents to chunking, embedding, and indexing. Everything is processed and ready for retrieval automatically.
Every answer includes automatic source tracking and references to original documents. Build trust with transparent, verifiable AI responses.
Based on the Q4 financial report, revenue increased by 23% compared to Q3, primarily driven by enterprise subscriptions.
Everything you need to build, deploy, and scale production AI applications
Intelligent parsing powered by Docling for PDFs, Word documents, and more. Extract text, tables, and structure with precision for optimal RAG performance.
Bring your own LLM provider or run inference locally. Support for OpenAI, Anthropic, and any custom LLM server. Complete flexibility without vendor lock-in.
Automatic source tracking and citation generation. Every answer includes references to the original documents, ensuring transparency and trustworthiness.
Store and organize memos with automatic metadata extraction. Support for notes, documents, code, and any text-based content with powerful tagging capabilities.
Powerful vector search with configurable embeddings. Find relevant context instantly with query rewriting and intelligent ranking.
Production-ready chat API with built-in RAG. Chat history management, context retrieval, and response generation in one unified endpoint.
Fine-tune every aspect: chunking strategies, reranking algorithms, vector search parameters, and system prompts. Adapt to your exact requirements.
Restrict accessible knowledge per query with powerful filtering. Improve accuracy and performance by scoping context to relevant documents.
Experiment with configurations and measure performance. A/B test different RAG strategies and optimize for your specific use case.
Production-ready SDKs for Node.js, Python, Ruby, Go, PHP, and .NET. Consistent APIs across languages with comprehensive documentation.
Connect your AI agents directly to Skald using the official Model Context Protocol server. Seamless integration with agent frameworks.
Deploy on your infrastructure with full data control or use our managed cloud. MIT licensed with no vendor lock-in.
Get started in minutes with sensible defaults. Push context and chat out-of-the-box, then fine-tune as you scale.