Retrieval (RAG)
Answers pulled from your docs, tickets, and data - with citations - so responses are accurate and verifiable, not hallucinated.
Retrieval-augmented generation is what separates a real AI system from a demo. Instead of relying on the model's training, the system looks up your actual content and answers from it, with sources.
We build the retrieval layer properly - clean indexing, good chunking, and citations - so answers are grounded and you can trust them.
How we deliver this
- Ingest and index your knowledge sources
- Tune retrieval for accuracy and freshness
- Return answers with citations
- Keep the index current as content changes
Key deliverables
- A retrieval pipeline over your data
- Cited, grounded answers
- A refresh/ingestion process
Expected outcomes
- Accurate, sourced answers
- Less hallucination
- Trustworthy AI over your data
Ready to implement Retrieval (RAG)?
Talk with our team and get a tailored roadmap for this feature in your growth stack.
Ideal for
- Businesses with lots of documentation
- Support and internal knowledge use cases
- Anyone who needs verifiable AI answers
Frequently asked questions
Where does my data live?
In your environment or a store you control - we design for privacy, and can keep sensitive data out of third-party training entirely.
Related features
View all in AI Agents & AutomationAI copilots & assistants
Assistants grounded in your own knowledge that draft, answer, and act - inside the products and channels your team already uses.
Learn moreWorkflow automation
Multi-step automations that triage, route, summarize, and update records - removing the repetitive work between your tools.
Learn moreData & integrations
Pipelines and integrations that connect your CRM, inbox, database, and docs so the automation has the context it needs.
Learn more