RAG without the separate system
What is Backboard RAG?
Backboard RAG lets your apps answer questions using your own data: docs, PDFs, images, code, and more. Instead of standing up a vector DB, parsers, and custom retrieval logic, you:
RAG
Why engineers use Backboard for RAG
From unified model access to production‑ready ingestion — everything built into one stateful API.
Production‑ready from day one
Ingestion pipelines, vector search, and context injection are all managed infrastructure. No self-hosted vector DB, no maintenance overhead.
HOW IT WORKS
How Backboard RAG works
You treat RAG as a tool on the same message endpoint:
USE CASES
RAG patterns you can implement
Common retrieval architectures teams build on Backboard — from doc Q&A to multimodal pipelines.
Documentation copilots
Let users query your docs, API references, and guides in natural language. Backboard retrieves the right sections and keeps answers grounded in your content.
Internal knowledge bots
Index your wikis, runbooks, and policy docs. Employees get instant answers from internal knowledge with full citations, without hallucinations from general training data.
Developer assistants
Give coding agents access to your codebase, architecture docs, and PR history. RAG + memory means the assistant knows your project context across every session.
Multimodal RAG
Mix text, images, and structured data in a single retrieval pipeline. Vision-capable models can retrieve and reason over charts, screenshots, and diagrams alongside written content.
COMPARISON
Why not just build your own RAG stack?
Rolling your own means choosing infra and stitching systems. Backboard bundles all of it.
Rolling your own usually means:
Choosing and running a vector DB
Implementing parsers and chunkers for various file types
Writing retrieval heuristics, ranking, and dedup logic
Backboard bundles:
Built-in retrieval
RAG that speaks to your real data formats — PDFs, Office docs, images, and code files.
Unified stateful platform
State management + Adaptive Context Management, memory, and web search in the same unified, stateful API. You use one platform instead of a patchwork of services.
PLATFORM
Included in Backboard, not a separate product
RAG is a built‑in part of the Backboard platform. No separate product, no add-on pricing for RAG itself.