AI knowledge assistants built with RAG
Retrieval-augmented generation (RAG) is a technique where an AI assistant first searches your own documents and data, then uses the most relevant passages to write its answer and cite its sources. Visions builds RAG assistants that give accurate, up-to-date and permission-aware answers from your company knowledge.
Overview
AI Knowledge Assistants (RAG) explained
Every organisation has knowledge spread across PDFs, wikis, help desks, shared drives and databases. A knowledge assistant built with retrieval-augmented generation (RAG) lets staff or customers ask a question in plain language and get an answer drawn from those sources, with links to the passages it used.
The quality of a RAG assistant depends mostly on retrieval: finding the right passages before the model writes anything. We spend most of our effort there, on document parsing, chunking, search tuning and access control, then measure answer accuracy with real questions from your team before launch.
- 01Audit your knowledge sources
- 02Build ingestion pipelines
- 03Tune retrieval
At a glance
AI Knowledge Assistants (RAG)
Best for
- Internal help desk for HR, IT and policy questions
- Customer support assistant over product documentation
- Sales enablement assistant over proposals and product sheets
- Technical assistant over manuals and maintenance records
What you gain
- Answers in seconds
- Trustworthy, cited answers
- Always up to date
Built with
- Python
- LlamaIndex
- LangChain
- pgvector
- Pinecone
- Weaviate
What our AI Knowledge Assistants (RAG) service includes
Knowledge ingestion pipelines
Connectors and parsers for PDFs, Office files, web pages, wikis, help desks and databases, with scheduled re-indexing.
Search and retrieval tuning
Chunking, embeddings, hybrid keyword and vector search, and re-ranking tuned to your content.
Cited answers
Every answer links back to the source passages so users can verify it.
Access control
Users only see answers drawn from documents they are allowed to access.
Chat interfaces and integrations
Web chat, intranet widgets, and assistants inside tools your team already uses.
Answer quality evaluation
Test question sets and metrics for retrieval accuracy and answer faithfulness.
Benefits of AI Knowledge Assistants (RAG)
What your business gains when it is designed, built and run well.
Answers in seconds
People find information without searching several systems or asking colleagues.
Trustworthy, cited answers
Each answer links to its sources so users can check it.
Always up to date
Updating a document updates the assistant; no model retraining is needed.
Respects permissions
Users only receive answers from documents they are allowed to see.
Our method
How we build AI Knowledge Assistants (RAG) solutions
A practical, step-by-step approach that proves value early and reduces risk.
Step 1
Audit your knowledge sources
We list where information lives, how often it changes and who may access it.
Step 2
Build ingestion pipelines
We parse, clean and split documents, then index them with embeddings and keyword search.
Step 3
Tune retrieval
We test chunk sizes, hybrid search and re-ranking against real questions until the right passages come back.
Step 4
Design answer behaviour
The assistant answers only from retrieved sources, cites them and says clearly when it does not know.
Step 5
Launch and keep improving
We monitor questions, failed answers and feedback, and refine content and retrieval over time.
Common use cases
- Internal help desk for HR, IT and policy questions
- Customer support assistant over product documentation
- Sales enablement assistant over proposals and product sheets
- Technical assistant over manuals and maintenance records
- Legal and compliance search over contracts and policies
Technologies we work with
- Python
- LlamaIndex
- LangChain
- pgvector
- Pinecone
- Weaviate
- Elasticsearch
- OpenAI embeddings
- Anthropic Claude API
- FastAPI
Where AI Knowledge Assistants (RAG) helps
Examples of how teams in different industries apply it.
Customer support
Assistants that answer product and policy questions from your help centre.
HR and internal operations
Instant answers on policies, benefits and procedures for employees.
Legal and compliance
Search across contracts, policies and regulations with cited passages.
Manufacturing and field service
Assistants over manuals, specifications and maintenance records.
Glossary
AI Knowledge Assistants (RAG) key terms
Plain-English definitions of the terms used on this page, so everyone on your team is speaking the same language.
Read the FAQ- Retrieval-augmented generation (RAG)
- A method where relevant passages are retrieved from your data and given to a language model to write a grounded answer.
- Embedding
- A numerical representation of text that lets software find passages with similar meaning.
- Vector database
- A database designed to store embeddings and search them by similarity.
- Hybrid search
- Combining keyword search with semantic (embedding) search to improve retrieval accuracy.
Our process
How we deliver
Design first, build carefully, test everything. Every AI, web and app project moves through the same six stages, adapted to your requirements.
- Mockups approved before any code is written
- Tested across screen sizes and browsers
- Direct communication throughout
- Support after launch
Step 01
Discover and plan
We review your goals, requirements and existing systems, agree the scope and assemble the right team.
- Requirements review
- Agreed scope
- Project team
Step 02
Design mockups
Our graphic designer creates mockups first. Development starts only after you approve the design.
- Mockups
- Your approval
- Figma or XD files welcome
Step 03
Develop
We build to closely match the approved design, responsive on desktop, tablet and mobile, with content that is easy to edit.
- Responsive build
- Editable content
- Progress updates
Step 04
Test
We test spacing, typography, interactions and functionality against the mockup, across screen sizes and browsers.
- Design accuracy
- Cross-browser
- Mobile and tablet
Step 05
Launch
We deploy to production, make final refinements and hand over everything you need.
- Production deployment
- Final refinements
- Handover
Step 06
Support
We stay available for maintenance, updates, performance improvements and the next release.
- Maintenance
- Updates
- Performance
FAQ
AI Knowledge Assistants (RAG): frequently asked questions
Common questions about our AI Knowledge Assistants (RAG) service.
What does RAG mean in AI?
RAG stands for retrieval-augmented generation. The system retrieves relevant passages from your own data and gives them to a language model, which writes an answer based on those passages instead of relying only on what it learned in training.
Why use RAG instead of fine-tuning?
RAG keeps answers current because you update documents, not the model. It also provides source citations and respects document permissions. Fine-tuning is better for teaching a model a style or task format.
Can a RAG assistant say when it does not know?
Yes. We design prompts and checks so the assistant answers only from retrieved sources and states clearly when the information is not in the knowledge base.
Which document types can be used?
PDFs, Word and Excel files, web pages, wiki and help-desk articles, database records and transcripts are all common sources. Scanned documents can be included with OCR.
Can a RAG assistant work in more than one language?
Yes. Modern embedding and language models handle many languages, and an assistant can answer in the user's language from documents written in another, which we test during evaluation.
Where is our data stored?
Documents and indexes can be stored in your own cloud account or servers. We can also use model providers and regions that match your data residency requirements.
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