Phoenix - Your AI AssistantBuild RAG chatbot trained on your documents & knowledge base
Delivery in
4 days
- Views 1
Amount of days required to complete work for this Offer as set by the freelancer.
Rating of the Offer as calculated from other buyers' reviews.
Average time for the freelancer to first reply on the workstream after purchase or contact on this Offer.
What you get with this Offer
Most "AI chatbot" offers are a ChatGPT wrapper that makes things up when it doesn't know the answer. A RAG (retrieval-augmented generation) chatbot is different: it retrieves the actual relevant passage from your documents before answering, so responses are grounded in your real content and can cite exactly where the answer came from.
I build the full pipeline: ingesting your documents (PDFs, docs, help center articles, internal wikis, spreadsheets, or a website's existing content), chunking and embedding them into a vector database, and connecting retrieval to an LLM so it answers using your material instead of guessing. When your content changes, re-indexing is set up so the chatbot stays current without you rebuilding it from scratch.
This fits internal use (an employee-facing knowledge base assistant that actually knows your policies and procedures) just as well as customer-facing use (support that answers from your docs instead of generic troubleshooting). Deployment options include a website widget, Slack/Teams bot, or an API endpoint your own app calls.
Every build includes basic guardrails so the bot says "I don't know" instead of fabricating an answer when the information isn't in your documents, plus documentation on how to update the knowledge base afterward.
Features
Document ingestion: PDFs, Word/Google Docs, websites, spreadsheets, wikis
Vector database setup with chunking & embeddings tuned for your content
Answers with cited sources, not generic LLM guesses
Guardrails against hallucination — says "I don't know" when appropriate
Re-indexing workflow so the knowledge base stays current
Deployment as a website widget, Slack/Teams bot, or API endpoint
Documentation on updating and maintaining the knowledge base
I build the full pipeline: ingesting your documents (PDFs, docs, help center articles, internal wikis, spreadsheets, or a website's existing content), chunking and embedding them into a vector database, and connecting retrieval to an LLM so it answers using your material instead of guessing. When your content changes, re-indexing is set up so the chatbot stays current without you rebuilding it from scratch.
This fits internal use (an employee-facing knowledge base assistant that actually knows your policies and procedures) just as well as customer-facing use (support that answers from your docs instead of generic troubleshooting). Deployment options include a website widget, Slack/Teams bot, or an API endpoint your own app calls.
Every build includes basic guardrails so the bot says "I don't know" instead of fabricating an answer when the information isn't in your documents, plus documentation on how to update the knowledge base afterward.
Features
Document ingestion: PDFs, Word/Google Docs, websites, spreadsheets, wikis
Vector database setup with chunking & embeddings tuned for your content
Answers with cited sources, not generic LLM guesses
Guardrails against hallucination — says "I don't know" when appropriate
Re-indexing workflow so the knowledge base stays current
Deployment as a website widget, Slack/Teams bot, or API endpoint
Documentation on updating and maintaining the knowledge base
What the Freelancer needs to start the work
What documents/content the chatbot should be trained on, and roughly how much (page count, number of files)
Where it needs to live: website, Slack, internal tool, or an API you'll call
Internal use (employees) or customer-facing, and who the end users are
How often the source content changes, so I can set the right re-indexing approach
LLM preference (OpenAI, Anthropic/Claude, open-source) if you have one
Any existing chatbot or search tool this is replacing