BeforeQuery vs Chatbase
Both let you build an AI chatbot that answers from your content. Chatbase is a general-purpose chatbot platform across many channels; BeforeQuery is purpose-built for docs and support with hybrid retrieval, citations, abstention, and evals — the pieces that stop the assistant guessing when it doesn't know.
Feature comparison
Information about Chatbase is drawn from their public website.
Where BeforeQuery focuses differently
Built for docs, not for everything
Chatbase spans support, sales, voice, and WhatsApp — it's a general platform. BeforeQuery is deliberately narrower: it ingests your docs, wikis, tickets, and code, and answers with citations. That focus is why the retrieval stack (hybrid + reranker + abstention) exists and why the docs-platform installers (Mintlify, Docusaurus, GitBook, ReadMe, VitePress) are first-class.
The assistant refuses to guess
An abstention gate blocks generation when retrieval confidence is below threshold — the answer becomes 'I don't have that in the docs' rather than a plausible-sounding invention. Combined with grounded citations, this is what makes BeforeQuery deployable on a customer-facing widget without a support team fielding complaints.
More surfaces bundled
Slack, Discord, and Microsoft Teams native bots, an MCP server for AI IDEs, and a form deflector for support intake — all included on Pro. No add-on fees.
Full API and SDK openness
Web, React, Widget, and Agents SDKs plus MCP + A2A protocols, on every plan. Build custom experiences without waiting for a native integration.
Frequently asked questions
Is BeforeQuery a good replacement for Chatbase?
If your use case is docs + support with a widget, Slack/Discord/Teams bots, or an MCP server for AI IDEs, BeforeQuery is purpose-built for it and includes retrieval quality and evals that Chatbase's general platform doesn't. If you need WhatsApp or voice channels, Chatbase covers channels BeforeQuery doesn't.
How does retrieval quality compare?
BeforeQuery combines vector (pgvector) with full-text (BM25), fuses them with reciprocal rank fusion, rewrites the query with an LLM before search, and reranks with a cross-encoder. Vector-only retrieval misses exact-match queries (error codes, SKUs). Automated LLM-as-judge evals score every answer.
Does every answer include citations?
Yes. Citations link to the exact source passages the answer was grounded in. When retrieval confidence is too low, the assistant abstains instead of generating a plausible-sounding answer.
Can I migrate from Chatbase?
Yes. Reconnect the same sources (URLs, files, Notion, Google Drive), point the widget embed at BeforeQuery, and you're running. Most teams are live in an hour.
How does pricing compare?
Both offer self-serve pricing. BeforeQuery: Free (100 questions/mo), Pro $99/mo (1,000 questions, 5 KBs), Enterprise custom. See our pricing page for current details.
See for yourself
Connect your docs and get your first AI-powered answer in under 5 minutes. No credit card required.