RAG Orchestration

RAG orchestration for grounded, production-grade AI answers.

Design end-to-end RAG pipelines by connecting VectorDB, AI Function Tools, and Custom LLMs into stateful workflows — grounded answers, live data, and full control.

Get Started For Free Chat on WhatsApp

Or start with $150 of credit, enough for a first month of Growth

Nova Electronics AI assistant · online

My battery stopped charging after 14 months. Is that still covered?

10:18

Source: Warranty policy

Section 3.2 · Batteries

Order 6120 · delivered 14 months ago

Yes. Batteries are covered for 24 months from the delivery date, so your order is still under warranty.

10:18

Great, how do I claim it?

10:19

I opened a warranty request for you. Our service team will contact you here.

10:19

Build Blocks for Custom RAG

Connect your data, tools, and models — then orchestrate retrieval end-to-end.

How It Works

A simple path to production-grade RAG — without vendor lock-in.

  1. Connect VectorDB

    Use embedded vector stores or connect your own by pasting credentials. Data stays where you choose.

  2. Add Tools

    Build AI Function Tools to fetch live data, call APIs, or run business actions. Structured outputs & validation.

  3. Route Models

    Choose the best LLM per step. Use fallbacks to balance cost and quality. Fast + accurate workflows.

  4. Orchestrate Pipeline

    Chain steps: rewrite → retrieve → rerank → cite → respond — with stateful logic. Hybrid retrieval patterns.

Typical Pipelines

Common RAG blueprints you can ship quickly — then customize forever.

  • Support RAG

    Ground answers in policies, manuals, and tickets — escalate to agents with full context.

    • Example: Rewrite → retrieve KB → tool: check order → cite policy → respond

  • Sales RAG

    Answer product questions with catalog + pricing + CRM tools, and convert in-chat.

    • Example: Retrieve catalog → tool: pricing → tool: CRM lead → summarize & CTA

  • Operations RAG

    Turn SOP documents into guided actions with approvals, dashboards, and automation.

    • Example: Retrieve SOP → tool: validate → tool: create task → confirm status

FAQ

RAG orchestration questions

Quick answers to the most common questions.

What is RAG (retrieval-augmented generation)?

RAG is a technique where the AI first retrieves the relevant passages from your own content and then writes its answer from them. Answers are grounded in your documents instead of the model’s memory, which reduces hallucinations.

What does RAG orchestration add?

Orchestration chains the steps of a pipeline in one stateful flow: query rewrite, retrieval, reranking, tool calls, citations and the final response. You decide the order, the logic between steps and which model runs each one.

Do I need my own vector database?

No. You can start with the vector database embedded in MonoChat, or connect your own VectorDB with credentials when you want the embeddings to stay in your infrastructure.

Can a RAG answer include live data?

Yes. AI function tools let a pipeline fetch live data such as an order status, a price or a CRM record, and combine it with the retrieved documents in one answer.

Which LLMs can I use in a RAG pipeline?

Any provider you connect as a custom LLM. You can select a model per step, for example a fast model for query rewriting and a stronger one for the final answer, with fallbacks and cost guardrails.

Keep exploring

First month of Growth on us

Build Your First RAG Pipeline

Connect your VectorDB, add AI tools, route models, and ship reliable retrieval workflows in MonoChat.

Start with $150 credit Chat on WhatsApp

One code per business • 30 days to redeem • No card needed

$150 to get started.

Claim