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:
Models stay interchangeable. Your retrieval layer stays consistent.
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, chunking, indexing, and relevance tuning are handled for you so you can focus on quality and guardrails, not infra.
HOW IT WORKS
How Backboard RAG works
You treat RAG as a tool on the same message endpoint:
1. Ingest data sources
Ingest data sources (repos, buckets, knowledge bases, etc.) into Backboard.
2. Parse, chunk, and index
Backboard parses, chunks, and indexes content (including images and embedded files).
3. Retrieve on each request
On each request, enable RAG; Backboard retrieves relevant context for the user's query.
4. Merge context and respond
Adaptive Context Management merges retrieved context with state and memory into the model's window.
USE CASES
RAG patterns you can implement
Common retrieval architectures teams build on Backboard — from doc Q&A to multimodal pipelines.
Documentation copilots
Answer questions about product docs, changelogs, and FAQs with citations.
Internal knowledge bots
Connect to wikis, drives, and repositories for company‑wide Q&A.
Developer assistants
Combine code repos, runbooks, and tickets to help debug and implement features.
Multimodal RAG
Ask questions about PDFs with embedded images, slides, and screenshots in one flow.
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.