AI service
RAG & Knowledge Assistants
Ask questions across all your documents and get cited, permission-aware answers.
Discuss your project
What is retrieval-augmented generation (RAG)?
Retrieval-augmented generation (RAG) is a technique where an AI model first retrieves relevant passages from your own documents and then writes an answer based on them, with citations. Unicorn Softech builds RAG systems that index your files, wikis, tickets and databases so teams can search and ask questions across company knowledge.
See it
What it looks like
- policy.pdf
- contract.docx
- handbook.pdf
- pricing.xlsx
What is our refund window?
[1] policy.pdf · p.4[2] handbook.pdf · p.12
How it works
RAG & Knowledge Assistants in 5 steps
- 01Documents
- 02Chunk & embed
- 03Search
- 04LLM
- 05Cited answer
What we deliver
What you get with RAG & Knowledge Assistants
Ingestion pipelines
Connectors for PDFs, Office files, websites, SharePoint, Drive, Notion and databases.
Chunking & embeddings
Document parsing, smart chunking and embeddings tuned to your content.
Hybrid search
Vector plus keyword search with re-ranking for precise retrieval.
Cited answers
Every answer links back to the source passage so users can verify it.
Access control
Results filtered by each user's permissions at query time.
Quality evaluation
Retrieval and answer accuracy measured on a test set of real questions.
Common use cases
- Company knowledge base search
- Policy and compliance Q&A
- Technical documentation assistants
- Contract and legal document review
- Support agent assist
Tools and technology
We choose per project. These are the tools we use most for rag & knowledge assistants.
- pgvector
- Pinecone
- Qdrant
- Elasticsearch
- LlamaIndex
- LangChain
- OpenAI
- Anthropic Claude
- Ingestion pipelines
- Chunking & embeddings
- Hybrid search
- Cited answers
- Access control
- Quality evaluation
- Company knowledge base search
- Policy and compliance Q&A
- Technical documentation assistants
- Contract and legal document review
- Support agent assist
FAQ
RAG & Knowledge Assistants: common questions
What is RAG in simple terms?
RAG lets an AI model look things up in your documents before answering, so answers are based on your content and come with sources.
RAG or fine-tuning — which do I need?
Use RAG when answers depend on facts that change or live in documents. Use fine-tuning to change the model's style or format. Many systems use both.
How do you handle confidential documents?
Documents stay in your infrastructure or a private cloud, and retrieval respects each user's existing permissions so nobody sees content they could not open themselves.
Related
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Let's talk about RAG & Knowledge Assistants
Book a free 60-minute consultation. You'll leave with a feature list, a rough estimate and a practical first step — no obligation.
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