RAG Chatbots & Knowledge Assistants
A retrieval-augmented assistant answers from a corpus you control rather than from a model’s memory. We ingest your documents, tickets, product data, or policy library, index them for retrieval, and wire an assistant that quotes the passage it answered from. When it cannot find a grounded answer, it says so and hands the conversation to a person instead of inventing one.
- Document ingestion
- Vector search
- Cited answers
- Escalation to a human
What gets built
Ingestion pipeline
Scheduled sync from the sources you name — Drive, SharePoint, a helpdesk, a database, or a scraped internal site — with chunking and re-indexing on change.
Grounded retrieval
Vector plus keyword retrieval, with per-answer citations back to the source document and page.
Access control
Retrieval scoped by user or role, so an assistant cannot surface a document the asker could not open directly.
Escalation path
Confidence thresholds and an explicit "I don’t have that" response, routed to a human queue on WhatsApp, email, or your helpdesk.
Evaluation set
A written set of question/answer pairs from your team, run against the assistant before handover and re-runnable after any change.
Built on
- n8n
- Anthropic API
- OpenAI API
- pgvector
- Qdrant
- Postgres
Timeline
Typically 4 to 8 weeks from a signed scope, depending on how many sources are in play.