What the service covers
- Ingestion and chunking tuned to the source material's structure, not a generic fixed-size split.
- Embeddings and vector storage on Supabase pgvector, indexed and filtered for the retrieval pattern actually used.
- RBAC-filtered retrieval so a chunk is only ever surfaced to a user or role permitted to see it.
- Source-freshness tracking so stale answers are flagged or refreshed instead of served as current.
- Guardrails against hallucination: constrained answering, confidence thresholds, and explicit "not found" behavior.
Where RAG is the right tool
RAG fits when a business has documents, product catalogs, policies, or support history that a model should answer from accurately — not when the goal is open-ended creative generation. A knowledge base that changes rarely and has a small number of authoritative sources is the strongest starting point.
Verified production evidence
The Standout4Growth RAG chatbot runs a live Supabase vector store behind an n8n agent workflow, answering company-specific questions while holding a brand-coach persona — documented with architecture and outputs in the mission archive, not claimed scale figures.
Related technical Field Notes
RAG source freshness, evidence, and access control covers the freshness and RBAC pattern in depth. Defending RAG agents against indirect prompt injection covers what happens when retrieved content itself tries to redirect the model.
RAG developer questions
What is a RAG system and why does it need a developer, not just a model?
RAG connects a language model to a business's own documents or data through a vector database, so answers come from retrieved facts instead of the model's memory. The engineering work is in chunking strategy, embedding quality, retrieval ranking, source-freshness, and access control — not the model call itself.
How is hallucination controlled in a RAG pipeline?
By constraining the model to answer only from retrieved context, returning 'not found' when retrieval confidence is low, citing the source passage, and testing the pipeline against known-answer and known-absent queries before launch. Standout4Growth's live assistant runs on this pattern.
What does 'source freshness' mean in a RAG system?
It means the retrieval layer tracks when each source document was last verified and either refreshes stale embeddings on a schedule or flags stale answers rather than serving them as current. This matters for pricing, policy, or compliance content that changes over time.
Can a RAG system respect per-user or per-role access?
Yes, with RBAC metadata attached to each embedded chunk at ingestion time, so retrieval filters out documents a given user or role should not see before the model ever receives them.
What vector database do you use for RAG in Kenya-based projects?
Supabase pgvector by default — it keeps the vector store in the same Postgres instance as the rest of the application data, which simplifies RLS, backups, and operational ownership for small and mid-size teams.
What to send for a useful RAG brief
Send the source material (documents, catalog, or data shape), who needs to query it, any per-role access rules, how often the source changes, and what "wrong answer" would cost. If a simpler keyword search or FAQ page is the better fit, the recommendation will say so directly.
Full n8n workflow orchestration around a RAG pipeline is covered separately at n8n Automation Services, and on the agency side at HarLyn Digital Partners.