Generic AI answers
Off-the-shelf chatbots answer from general knowledge. A RAG assistant answers from your documentation.
RAG development for teams that need AI answers grounded in their own documents.
We build retrieval-augmented generation (RAG) systems on your documentation, policies, and product knowledge. The assistant retrieves the relevant passages first and answers from them, which keeps responses grounded in what your business has written down.
RAG Knowledge Assistants
Custom scope after consultation
Scoped, built, and documented by our founder. You get a fixed scope and price before work starts.
- Document ingestion and cleaning pipeline
- Chunking and embedding strategy matched to your content
- Vector search with a managed vector database
- Answer generation restricted to retrieved sources
What we deliver
- An ingestion pipeline that keeps the knowledge base up to date
- A retrieval and answer API your website, app, or team chat can call
- A test set of real questions with reviewed answers
- Documentation of the system structure so your team can maintain it
- Guidance on what content to add or fix to improve answers
Outcomes clients want
- Answers that reflect your documentation, not general internet knowledge
- Less time spent by staff searching for the same information
- Support replies that stay consistent across the team
- A knowledge system you own and can extend
Service strategy
Problems a RAG assistant solves
A language model on its own does not know your pricing, policies, or product details. Retrieval-augmented generation fixes that by searching your documents first and answering from what it finds.
Axenor AI builds the whole pipeline: document ingestion, chunking, embeddings, vector search, answer generation, and an API your team or website can call. We also show you how the system is structured so you can maintain it.
Knowledge locked in documents
Staff lose time searching shared drives and old threads for answers that already exist in writing.
Inconsistent support replies
Different team members give different answers to the same question. One grounded source keeps replies consistent.
Build process
How a RAG build works
Every rag knowledge assistants project starts with the business outcome, then the workflow, content, tools, prompts, and handoff rules are designed around that goal.
01
1. Collect and clean
We gather the source documents, remove duplicates and outdated content, and set up automated ingestion.
02
2. Chunk and embed
Documents are split into passages and stored as embeddings in a vector database for semantic search.
03
3. Retrieve and answer
Each question retrieves the most relevant passages, and the model answers only from those passages.
04
4. Evaluate and hand over
We test against real questions, tune retrieval, and document the structure for your team.
Business use cases
Where RAG assistants fit
Internal team assistant
Staff ask questions about processes, policies, and product details and get answers from company documentation.
Customer support agent
Customers get accurate answers from your help content, with handoff to a person when needed.
Portfolio or product assistant
Visitors ask about your services, projects, or documentation and get grounded answers in real time.
Next reading
Related Axenor AI resources
These pages help connect rag knowledge assistants with the wider automation system your business may need next.
Work of this kind we have built
Built by our founder. Client systems stay private, so these are the builds we can show in full.
Ask Haseeb AI
LiveA live retrieval-augmented (RAG) assistant that answers questions about our founder's work from his own documents, and says so when the answer is not in them. New files are picked up from Google Drive, split into passages, embedded, and stored in a vector database; every answer is written from the passages it retrieves.
- Automatic ingestion of PDF, Markdown, and HTML files from Google Drive
- Token-based chunking (about 350 tokens, with overlap)
- OpenAI embeddings in Pinecone, LangChain retrieval, FastAPI backend
- Deployed on a VPS with Gunicorn and Nginx
Built with Python, FastAPI, OpenAI, LangChain, Pinecone
What clients said about this kind of project
Published word for word, with the platform and date.
“Haseeb built an internal AI assistant for our team using our own documentation. The main thing we wanted was reliable answers instead of the usual generic AI responses. The RAG setup is working well and he also showed us how the system was structured so we can maintain it later.”
“Haseeb did exactly what we needed. The project was an AI agent for handling customer enquiries and pulling information from our internal knowledge base. The first version needed some tweaking but he was patient with the feedback and improved the responses a lot. The workflow is now running properly.”
RAG Knowledge Assistants: common questions
Direct answers about how this service works, what it includes, and how it is priced.
Have a different question?
Ask it directly. You get an answer from the engineer who would build your system.
What is RAG?
- RAG stands for retrieval-augmented generation. Before the language model answers, the system searches your own documents for the most relevant passages and gives them to the model, so the answer is based on your content.
Why use RAG instead of a general AI chatbot?
- A general chatbot answers from its training data and can give generic or wrong answers about your business. A RAG assistant answers from your documentation, which makes it more accurate and easier to correct: you fix the document and the answer changes.
What documents can a RAG assistant use?
- PDFs, Word and Google Docs, help-centre articles, policies, product documentation, spreadsheets, and exported knowledge-base content. Messy or outdated documents are cleaned up as part of the build.
Is our data kept private?
- We agree where your documents are stored and who can access them before the build starts. Documents are used only to answer questions in your assistant and are not shared with other clients.
Have you built a RAG system we can look at?
- Yes. Ask Haseeb AI is an open-source RAG assistant built by our founder with FastAPI, OpenAI, LangChain, and Pinecone. You can try it live at ask.haseebsagheer.com, and the code and walkthrough video are linked from our work page.