Answers grounded in your documents, not the model's imagination
Upload your pricing sheets, policies, FAQs and product guides. ReplySetter chunks and embeds them into the vector store you choose, and agents search it with a tool whenever a question needs facts, so replies quote your truth.
A closer look at Knowledge bases
Chunked, embedded and indexed automatically
Add a document and it's split into overlapping chunks, embedded with the model you choose, and written to the store. Embeddings come from OpenAI, OpenRouter or Cloudflare Workers AI, and dimensions are detected on first use, so there's nothing to configure.
- Automatic chunking and embedding
- OpenAI, OpenRouter or Workers AI embeddings
- Dimension detection on first use
- Re-index a document or a whole base in one click
Bring the vector store you already trust
Start with the built-in pgvector store in the app's own Postgres, or point a knowledge base at your own pgvector or Supabase database, a Pinecone index or Cloudflare Vectorize. Each store keeps knowledge bases cleanly separated.
- Built-in pgvector: nothing to set up
- pgvector / Supabase: one table per knowledge base
- Pinecone: one namespace per knowledge base
- Cloudflare Vectorize: separated by metadata
The agent searches when it needs to, and only then
Retrieval is a tool, not a blob pasted into every prompt. The agent calls search_knowledge with a focused query, reads the best-matching passages and answers from them. You can see every query and result in the debugger.
- search_knowledge is called on demand
- Focused queries, relevant passages
- Every search visible in the debugger
- Diagnose can suggest knowledge fixes
The details
Built-in
pgvector in the app's own Postgres. No setup required.
pgvector / Supabase
Any Postgres with pgvector; one table per knowledge base.
Pinecone
One index, with a namespace per knowledge base.
Cloudflare Vectorize
One index; knowledge bases told apart by a metadata field.
Embeddings
text-embedding-3-small, bge-m3 and more via OpenAI, OpenRouter or Workers AI.
Search API
Search any knowledge base from the app or over MCP to check what agents will find.
Who it's for
Support
Warranty, returns and shipping answers straight from your policy docs.
Sales
Pricing tiers and package details quoted accurately every time.
Onboarding
Product guides that answer “how do I…” questions over chat and email.
Knowledge bases: common questions
Do I need my own vector database?
No. The built-in store uses pgvector inside the app's own Postgres with zero setup.
Can I use Pinecone or Supabase?
Yes, along with any pgvector database and Cloudflare Vectorize.
Is my whole knowledge base sent with every message?
No. The agent searches with a tool and only reads the passages it needs.
How do I check what the agent found?
Open the debugger on any reply to see each knowledge search and its results.
Works hand in hand with
Test & Debug
Sandbox conversations, a full debugger for every reply, and an AI diagnose-and-fix loop.
Explore BuildVisual job flows
Design agents on a canvas: steps with objectives, AI conditions, switches, scenarios and exits.
Explore ChannelsWebsite chat widget
An embeddable AI chat bubble with pre-chat forms, CRM sync and serious abuse protection.
ExplorePut it to work on your own inbox
Connect an inbox, describe the job, test it in a sandbox and go live with exactly the autonomy you're comfortable with.