Creates deterministic, auditable vector databases from any content source with deployable RAG applications. Supports multiple embedding providers and vector databases with fine-grained pipeline control or project-based workflows.
Enables document Q&A and knowledge retrieval through hybrid semantic and keyword search, with tools for document ingestion, chunking, summarization, PII redaction, and RAGAS-based evaluation.
Enables ingestion and semantic search over text documents using PostgreSQL + pgvector and OpenAI-compatible embeddings, allowing any LLM agent to retrieve relevant chunks for grounded answers.
Enables LLM evaluation and observability by uploading documents, building test sets, running RAG pipelines, and automatically scoring answers for groundedness, hallucination risk, retrieval quality, latency, and cost, with tools exposed to MCP-compatible clients.
Transforms local Git repositories into queryable, context-rich knowledge bases via AST-aware chunking and Git metadata, enabling AI assistants to search and understand codebases with semantic precision.
