Customer assistant
Answers guests and shoppers on your website from your menu, rooms, prices and policies, and hands over to staff when needed.
Fuell AI / AI & RAG pipelines
AI that answers from your business, not the internet.
We build retrieval-augmented generation (RAG) pipelines and assistants on the documents and records you choose: menus, room policies, SOPs, price lists and catalogs. Every answer shows its sources, so your team can check it.
One platform. Every part of your business.Connected by design.
Inside a RAG pipeline
From your documents
to a cited answer.
RAG gives a language model the right passages from your own sources at the moment a question is asked, so answers stay current and specific to your business.
Example guest question: Is breakfast included with the Deluxe room?
Collect your sources
We connect the documents you approve, such as room policies, menus, price lists, SOPs and FAQs, from PDFs, Docs, Sheets, web pages or your Fuell workspace.
WHAT IT LOOKS LIKE
- room-policies-2026.pdf
12 pages - tariff-card.xlsx
4 sheets - guest-faq (website)
38 answers
Illustrative example. Select a stage or run the pipeline.
What we build
AI for the questions your business gets every day.
Staff knowledge assistant
New team members ask about SOPs, recipes, check-in steps or return rules and get the approved answer.
Document search and Q&A
Search across agreements, invoices and supplier documents in plain language, with the page each answer came from.
Questions about your data
Ask about sales, occupancy or stock in your Fuell workspace and get a clear summary, alongside Don AI.
Workflow agents
Sort enquiries, draft replies and prepare records, with a person approving each action before it happens.
Multilingual answers
Questions in English, Hindi or Hinglish, answered from the same sources.
How we work
Measured before launch. Monitored after.
We test every assistant against real questions from your business before it goes live, and keep measuring once people use it.
Discover
We agree the questions to answer, who will ask them and which sources the system may use.
Prepare data
We clean, chunk and index your approved documents and records, with access rules attached.
Build & evaluate
We build the pipeline and score it on a test set of real questions for accuracy and citations.
Deploy & monitor
It goes live on your website, workspace or internal tools, with feedback and quality tracking.
Principles
Useful, checkable,
and under your control.
AI is only useful when people trust it. These are the rules we build every project around.
- Retrieval, chunking, embeddings and vector search
- Hybrid keyword search and re-ranking
- Guardrails, evaluation sets and monitoring
- Hosted or open models, and Fuell LLM when it is ready
Sources on every answer
Answers cite the passage they came from. When sources do not cover a question, the assistant says so.
People approve actions
Anything that changes a record or reaches a customer can require a person to approve it.
Agreed data access
We agree in writing which data the system can use, where it is stored and who can see it.
The right model for the job
We choose models for quality, speed and cost, and can change them without rebuilding your pipeline.
Is it a good fit?
Signs RAG will help your team.
- The same questions come up every dayGuests, customers or staff ask things that are already answered in a document somewhere.
- Answers live in many placesPDFs, spreadsheets, chats and your workspace each hold part of the story.
- Accuracy mattersYou need to see where an answer came from, not trust a general chatbot.
- Your information changesPrices, menus and policies change, and the assistant should keep up without retraining.
Questions
AI and RAG, answered.
- What is RAG (retrieval-augmented generation)?
- RAG finds the most relevant passages in your own documents and gives them to a language model with each question, so answers are specific to your business and can cite their sources.
- Will the AI make things up?
- No AI system is perfect. RAG reduces made-up answers by answering from retrieved sources, showing citations and saying when it does not know. We test it on real questions before launch.
- What data do you need from us?
- Only the documents and records you choose to share, such as menus, policies, SOPs or price lists. We agree access in writing before we start.
- Can the assistant answer in Hindi?
- Yes. Assistants can answer questions in English, Hindi or Hinglish from the same sources.
Popular topics
What people look for in AI & RAG pipelines
- RAG pipeline development
- retrieval-augmented generation services
- AI chatbot for business
- AI chatbot for restaurants
- AI assistant for hotels
- chatbot trained on your documents
- knowledge base chatbot
- document Q&A with AI
- Hindi AI chatbot
- AI agents for business workflows
Put AI to work on your own data.
Tell us the questions you want answered and where the answers live today. We will suggest a pipeline and a first version to test.
Explore Fuell AI
Find what you need next.
A closer look at the platform, or a conversation with us.