Custom RAG Chatbots (Retrieval-Augmented Generation)

Empower your team and customers with instant, verifiable answers grounded strictly in your company’s proprietary documents. Our Retrieval-Augmented Generation (RAG) systems vectorize your manuals, policy guidelines, and inventory data to deliver citation-backed answers without hallucination.

Category: AI & Agents. Typical timeline: 2 - 3 Weeks. Ideal for: Law practices, manufacturing firms with heavy SOPs, medical organizations, and customer support databases.

Benefits

  • 100% citation-backed answers referencing exact document page numbers
  • Strict zero-hallucination guardrails: admits when data is unavailable
  • Enterprise role-based access control protecting confidential documents
  • Support for PDFs, Word files, spreadsheets, Notion, and Confluence

Features

  • Vector database chunking & semantic search indexing
  • Hybrid keyword + semantic dense retrieval
  • Continuous index updating when documents are edited
  • Source citation previews directly inside chat interface

How we deliver it

  1. Document Ingestion & Cleaning: Extract and sanitize proprietary files, catalogs, and policies.
  2. Vector Indexing & Embedding: Chunk content hierarchically and generate high-dimensional embeddings.
  3. Retrieval & Reranker Pipeline: Tune similarity thresholds and integrate top-k rerankers.
  4. Testing & Citation UI: Deploy clean web UI with direct page citations and feedback ratings.

Deliverables

  • Custom RAG application with embedding pipeline
  • Document upload & re-indexing admin panel
  • Embeddable chat widget with source citations
  • Cloud vector database configuration & security keys

Technologies

Pinecone, Qdrant, ChromaDB, LlamaIndex, Gemini Embeddings, Next.js

Frequently asked questions

What is Custom RAG Chatbots (Retrieval-Augmented Generation)?
Empower your team and customers with instant, verifiable answers grounded strictly in your company’s proprietary documents. Our Retrieval-Augmented Generation (RAG) systems vectorize your manuals, policy guidelines, and inventory data to deliver citation-backed answers without hallucination.
How long does Custom RAG Chatbots (Retrieval-Augmented Generation) take to deliver?
The typical timeline for Custom RAG Chatbots (Retrieval-Augmented Generation) is 2 - 3 Weeks, depending on scope and the systems that need to be connected.
Who is Custom RAG Chatbots (Retrieval-Augmented Generation) for?
Law practices, manufacturing firms with heavy SOPs, medical organizations, and customer support databases.
What do I receive with Custom RAG Chatbots (Retrieval-Augmented Generation)?
Custom RAG application with embedding pipeline; Document upload & re-indexing admin panel; Embeddable chat widget with source citations; Cloud vector database configuration & security keys.
Which technologies does NextCore BD use for Custom RAG Chatbots (Retrieval-Augmented Generation)?
Pinecone, Qdrant, ChromaDB, LlamaIndex, Gemini Embeddings, Next.js.