Aidena

— AI STACK RECOMMENDATION

Enterprise RAG Knowledge Base Assistant

Scalable RAG system for searching and retrieving internal documentation with semantic understanding, built for enterprise knowledge management with production-grade observability.

Stays alive for 365 days after the last visit.

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Enterprise RAG Knowledge Base Assistant

Scalable RAG system for searching and retrieving internal documentation with semantic understanding, built for enterprise knowledge management with production-grade observability.

high confidence

Core Stack ℹ︎

Claude Sonnet 4

Primary

Best-in-class LLM for RAG with low hallucination, strong reasoning for complex documentation queries, and native tool use for retrieval augmentation.

$50-200/month

Cohere Embed API

Primary

Multimodal embeddings supporting text, tables, and images in enterprise docs. 100+ language support and superior semantic search quality for documentation retrieval.

$0-100/month

Pinecone

Primary

Managed vector database optimized for production RAG at scale. Serverless, auto-scaling, metadata filtering for document organization, and built-in hybrid search.

$100-500/month

Complete the Stack ℹ︎

Airbyte

Alternative

Automate ingestion of documentation from multiple sources (Confluence, SharePoint, S3, databases) into your RAG pipeline with 300+ connectors.

$0-300/month

Arize Phoenix

Alternative

Open-source observability for RAG pipelines. Monitor retrieval quality, LLM latency, hallucination detection, and debug retrieval failures in production.

$0/month

LangChain

Alternative

Framework for orchestrating RAG workflows: document loading, chunking, retrieval, and LLM integration with built-in memory and chain management.

$0/month

Getting started

  1. 1Set up Pinecone serverless index with metadata schema for document organization (source, date, department).
  2. 2Configure Airbyte to sync documentation sources (Confluence, SharePoint, local files) on a schedule.
  3. 3Use LangChain to build document ingestion pipeline: load docs → chunk with overlap → embed via Cohere → upsert to Pinecone.
  4. 4Create RAG chain: user query → retrieve top-k docs from Pinecone → rerank with Cohere Rerank → pass to Claude Sonnet with context.
  5. 5Integrate Arize Phoenix for tracing: log all retrieval steps, embedding quality, and LLM outputs.
  6. 6Deploy via FastAPI or LangServe for REST endpoint.
  7. 7Add user authentication (Clerk) and audit logging for enterprise compliance.
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