Vector Index Builder
Turn scattered docs, SOPs, and tribal knowledge into a RAG-ready vector pack — without building chunking, embedding, and indexing infrastructure from scratch.
Who this is for
Internal AI teams building RAG assistants for company knowledge. Product teams who need an LLM to answer questions using internal docs, playbooks, and support macros. Agencies building AI-powered search for clients who have knowledge scattered across Notion, Google Docs, and Slack.
What problem this solves
LLMs don't know your internal knowledge unless you provide it at query time. RAG (Retrieval-Augmented Generation) retrieves relevant chunks from your docs and injects them into the prompt. But building RAG infrastructure requires: intelligent chunking (keeping context intact), embedding generation, vector database setup, and retrieval testing.
Most teams either: (1) dump entire docs into prompts (hitting token limits and wasting context), (2) manually chunk content without metadata (poor retrieval quality), or (3) spend weeks building custom RAG pipelines that break when doc structures change.
Why you can't do this yourself
Building production-grade RAG requires: semantic chunking algorithms, embedding model integration, vector database management (Chroma, Pinecone, Weaviate), metadata schema design, and retrieval testing infrastructure. Most teams don't have the AI engineering resources to build and maintain this stack just to make their docs searchable by an LLM.
What breaks without this
Knowledge retrieval inefficiency. LLMs hallucinate because they can't find the right information. Team members ask questions that are answered in docs but buried across multiple sources. AI assistants return irrelevant chunks because metadata is missing. Hours wasted manually answering the same questions over and over.
What this tool does
- Paste content from docs, policies, FAQs, SOPs, or support notes
- Intelligent chunking that preserves context and keeps sections coherent
- Metadata attachment (title, source, category, tags) for better retrieval
- Embedding generation and local Chroma vector index creation
- Test retrieval with queries and inspect which chunks are returned
- Download portable bundle (manifest + chunks + Chroma index folder)
What you get
A zip file with your chunked content, metadata manifest, and a Chroma vector index — ready to plug into a RAG assistant, server-side search endpoint, or AI-powered knowledge base. No custom infrastructure required.
What it does NOT do
This is not a hosted RAG platform. It doesn't auto-sync with your CMS or update when docs change. It's a one-time extraction and indexing tool for building RAG-ready knowledge packs. For production RAG deployments with auto-sync and API endpoints, you'll need additional infrastructure.