Retrieval-augmented generation (RAG)
Grounding model answers in retrieved documents to improve accuracy and provide citations.
Where we use it
- Knowledge assistants
- Support answers
- Policy Q&A
Strengths
- Answers grounded in your content
Considerations
Trade-offs worth knowing before you choose it.
- Retrieval quality limits answer quality
Retrieval-augmented generation (RAG) is a trademark of its owner. Listing it describes work we offer and does not imply a partnership or certification.
Services that use Retrieval-augmented generation (RAG)
AI-powered automation
Automation that combines AI understanding of documents, emails and messages with deterministic workflows and human review.
Generative AI
LLM-powered features: retrieval-augmented assistants, content drafting, summarisation and search over your documents with evaluation and guardrails.
Chatbot development
Website, app and WhatsApp chatbots — rule-based or AI-powered — with knowledge grounding, human hand-off and analytics.
More in ai and automation
- LL
Large language models
Hosted and open models for understanding and generating text, compared on your own tasks.
- AG
AI agent frameworks
Patterns and tooling for models that plan steps and call tools.
- ST
Speech-to-text
Transcribing speech for voice agents, call analytics and accessibility.
- TT
Text-to-speech
Natural-sounding synthetic voices.
- CV
Computer vision
Models that interpret images and video — OCR, detection and classification.
- n8
n8n
A self-hostable workflow automation platform.
- Zp
Zapier
Hosted automation connecting thousands of SaaS apps.
- Mk
Make
A visual automation platform for multi-step scenarios.
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