AI Chatbot & RAG Development
Chatbots that answer from your own documents, cite their sources and embed on your website or connect to your helpdesk.
Custom quote after a short consultation · Timeline agreed in your quote
Overview
An AI chatbot built with retrieval-augmented generation (RAG) answers questions by first finding the relevant passages in your own documents, then writing a reply from them. That keeps answers tied to your policies, product guides or knowledge base rather than general internet knowledge. Where AI automation works quietly in the background, a chatbot is a conversation that customers or staff start.
RF ETS builds these assistants in Python with GPT models. We prepare and index your documents, design how the bot should respond and when it should hand over to a person, then embed it on your website.
The Advanced tier covers a multi-source assistant over up to 1,000 documents, source citations in answers, up to three integrations such as a CRM or helpdesk, conversation logs with an admin view, and an answer-quality test set used to check responses before launch. Model, API and vector-database fees are paid by you.
Who it’s for
- Support teams answering the same product or policy questions every day
- Businesses with large manuals, FAQs or knowledge bases that staff struggle to search
- Companies that want a website assistant grounded in their own content
- Internal teams that need quick answers from procedures, handbooks or technical documents
Problems it solves
- Give customers answers from your own content at any hour
- Help staff find the right section of a long document without searching manually
- Show where each answer came from so it can be checked
- Route conversations into your CRM or helpdesk instead of losing them
What RF ETS delivers
- Prepared and indexed document collection
- Configured chatbot with response guidelines and hand-off rules
- Website embed and agreed integrations
- Conversation logs and admin view (Advanced)
- Answer-quality test set with results (Advanced)
- Handover notes on adding documents and updating behaviour
How we work
Collect and review documents
We review the documents, their format and how often they change, and agree which sources the bot may use.
Index and design responses
We split and index the content, then define tone, citation style and when to hand over to a person.
Integrate and test
We embed the bot, connect agreed tools and test answers against a set of realistic questions.
Launch and hand over
We go live with you and explain how to add documents, read logs and adjust behaviour.
What we need from you
- The documents the chatbot should answer from, in editable or text-based formats
- A list of typical questions your customers or staff ask
- Access to your website and any CRM or helpdesk to be connected
- Your own AI-model, API and vector-database accounts for usage billing
How pricing works
This work varies too much for fixed packages. We scope it with you, then send a written quote with price, timeline and deliverables before any work starts.
Tell us what you need
Use the quote form or book a consultation and describe your goal, constraints and deadline.
We scope it with you
We review your material and agree deliverables, assumptions and what is out of scope.
Written quote
You receive a fixed price or milestone plan, timeline and revision terms before any work starts.
Not included
- AI-model / API usage and vector-database fees (paid by you)
- Guaranteed answer accuracy
- Third-party costs: domains, hosting, paid plugins and themes, software licences, API or AI-model usage fees, data-provider credits
- Work outside the written scope agreed before work starts (handled as an add-on or a custom quote)
- Ongoing support after the delivery and launch-support window unless a support package is bought
Need something different?
Tell us what you need, and we’ll prepare a solution and pricing based on your requirements.
AI Chatbot & RAG Development: common questions
How is a chatbot project priced?
Tiers show typical scope with 'from' prices. The final price depends on document volume, formats and integrations, and is confirmed in a written quote after we review your content. See the packages above for what each tier includes.
Will the chatbot always give the right answer?
We do not promise answer accuracy. RAG keeps responses grounded in your documents and citations let users check them, and the Advanced tier adds an answer-quality test set, but any AI assistant can still misread a question or a source.
What running costs should we expect?
AI-model, API and vector-database usage fees are billed to your own accounts and are not included in our price. We explain the main cost drivers during design so you can set sensible limits.
How does this differ from AI automation?
A chatbot responds to questions people type. AI automation processes emails, files or records in the background without a conversation. Many clients start with one; we can scope both together if needed.
Can we update the documents after launch?
Yes. The handover notes explain how to add or replace documents and re-index them. Revisions within scope are included as listed for your tier, and new sources or integrations are added as an add-on or confirmed in a quote.
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