Skald provides a model‑agnostic Retrieval‑Augmented Generation platform that centralizes documents, databases, APIs, and conversation logs into a searchable knowledge layer. It offers a unified API for embedding generation, vector indexing, semantic search, reranking, caching, and automatic source attribution, with secure on‑prem or cloud deployment and configurable pipelines for enterprise AI applications.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprise AI applications often rely on large language models that cannot directly access proprietary documents, databases, or internal knowledge bases. Building and maintaining a Retrieval‑Augmented Generation (RAG) pipeline requires stitching together multiple services—embedding models, vector stores, reranking engines, and citation layers—resulting in high engineering overhead, inconsistent source attribution, and limited scalability.
Solution
Skald delivers a production‑ready RAG context layer that can be deployed inside a customer’s own cloud or on‑premises environment. The platform abstracts the entire retrieval stack—embedding generation, vector indexing, semantic search, reranking, caching, and citation—behind a single, model‑agnostic API, allowing developers to plug in any LLM (OpenAI, Anthropic, self‑hosted, etc.). By centralizing knowledge from documents, databases, APIs, and conversational histories, Skald provides a unified source of truth and automatic source attribution for every response. Built‑in security controls (VPC networking, data residency, end‑to‑end encryption) ensure full data sovereignty, while configurable pipelines let teams fine‑tune chunking, ranking, and retrieval parameters without writing custom plumbing. The result is faster time‑to‑market, reduced operational cost, and trustworthy AI outputs that scale with organizational growth.
Target Audience
Primary customers are enterprise development teams building AI agents, chatbots, or search applications in regulated sectors such as financial services, legal, healthcare, and enterprise SaaS, as well as any organization that requires private, auditable RAG capabilities.
Features
- Centralized knowledge graph that aggregates documents, databases, APIs, and conversation logs into a single searchable context layer.
- Model‑agnostic retrieval service supporting OpenAI, Anthropic, open‑source, or self‑hosted LLMs via a unified API.
- Production‑grade embedding generation and vector storage with automatic scaling, caching, and fault‑tolerant indexing.
- Fully configurable RAG pipeline: custom chunking strategies, selectable embedding models, reranking algorithms, and retrieval parameters (top‑K, similarity thresholds).
- Advanced document parsing that extracts text, tables, and structural metadata from PDFs, Word, and Office formats, followed by automatic chunking and indexing.
- Automatic source attribution with inline citations, page‑level references, and audit trails for compliance‑heavy industries.
- Secure deployment options (cloud, on‑prem, VPC) with data residency controls, role‑based access, and end‑to‑end encryption.
- Multi‑language SDKs (Python, Node.js, Go, C#, Ruby, PHP) and MCP integration for seamless connection to AI agents and developer tools.
- Built‑in evaluation dashboard that captures latency, relevance metrics, and A/B test results for continuous RAG optimization.