Retrieval-Augmented Generation at Shivacha
Grounding LLM answers in your documents with retrieval, citations and permissions.
Overview
RAG retrieves relevant passages from your knowledge sources and provides them to a language model as context, producing answers grounded in current, approved information with citations. Quality depends on parsing, chunking, hybrid search, re-ranking and permission-aware retrieval — each of which we engineer and measure.
Why we use it
- Current, source-grounded answers
- Citations for verification
- Permission-aware access
- No retraining required
How we use it
Retrieval-Augmented Generation in our engineering work
Knowledge assistants
Policy, product and technical Q&A.
Support automation
Grounded customer answers.
Research tools
Search across large corpora.
Services
Services that use Retrieval-Augmented Generation
RAG Development
Retrieval-augmented generation systems that ground LLM answers in your documents with permissions, citations and measurable accuracy.
Learn moreAI Chatbot Development
LLM-powered chatbots for customer service, sales and internal support — grounded, on-brand and integrated with your systems.
Learn moreEnterprise AI Solutions
Enterprise AI programmes: shared AI platforms, governance, security and a portfolio of production use cases across the organisation.
Learn moreAI Development
End-to-end AI development: from use-case selection and data preparation to models, applications and production operations.
Learn moreGenerative AI Development
Generative AI applications for text, documents, code and media — grounded in your data and engineered for production reliability.
Learn moreAI Agent Development
AI agents that plan, use tools and complete multi-step business tasks — with scoped permissions, approvals and full audit trails.
Learn morePairs well with
What we combine with Retrieval-Augmented Generation
Models, retrieval, agents and ML operations.
Insights
Related insights
Why most AI pilots never reach production — and the engineering that fixes it
The model is rarely the problem. Retrieval quality, evaluation, integration and governance decide whether an AI pilot becomes a production system.
Learn moreAI development cost: what you pay for when you build an AI product or agent
Model fees are rarely the main cost. Data preparation, evaluation, integrations and guardrails decide both the budget and whether the system works.
Learn moreDesigning AI products users trust
Trust in AI products is designed: show sources, make uncertainty visible, make correction easy and keep humans in control of consequential actions.
Learn more
Build with Retrieval-Augmented Generation.
Tell us about your project, or the engineers you need, and we will propose an approach.