Agents that answer from
your data
Connect your documentation, FAQs, and internal knowledge. Your AI agents retrieve real answers from your actual data using RAG — no hallucinations, no guessing.
Your docs become
agent intelligence.
Document Ingestion
Upload PDFs, web pages, or text documents. Chanl chunks, embeds, and indexes them automatically for fast retrieval.
Semantic Retrieval
Agents find the right answer even when customers use different words. Vector search matches meaning, not just keywords.
Grounded Answers
Agents cite sources and stay grounded in your data. No more confidently wrong answers from training data.
From documents to agent-ready answers
Upload your docs and Chanl handles the rest — chunking, embedding, indexing, and retrieval. Your agents get accurate, sourced answers in every conversation.
- Automatic chunking and embedding on upload
- Support for PDF, HTML, and plain text
- Configurable chunk size and overlap
- Real-time index updates when docs change
Find the right answer, every time
Vector similarity search retrieves the most relevant chunks from your knowledge base. Customers ask in their own words and still get the right answer.
- Cosine similarity with configurable thresholds
- Hybrid search (vector + keyword) for precision
- Per-agent knowledge base scoping
- Source attribution in agent responses
Keep your agents current
Update documents and the knowledge base re-indexes automatically. No stale answers, no manual reprocessing. Manage knowledge per workspace and per agent.
- Automatic re-indexing on document updates
- Per-agent and per-workspace knowledge scoping
- Usage analytics — which docs get cited most
- Bulk document management via API and CLI
Frequently Asked Questions
“Only 18% of organizations have successfully deployed an AI agent in production. The gap isn't building — it's testing, observability, and trust.”
Gartner, 2025 — AI Agent Readiness Report