How to Build an Internal AI Assistant for Your Business Using Claude
An internal AI assistant — trained on your company's specific knowledge, processes, and data — is more valuable than a generic AI tool. It answers questions specific to your business, maintains your brand voice, and integrates with your actual workflows.
Generic AI tools — Claude, ChatGPT in their standard forms — do not know your company's products, processes, policies, or customers. Every employee who uses them has to provide extensive context with every query. The result: AI is used inconsistently, context is duplicated endlessly, and the outputs reflect general knowledge rather than your specific business reality.
A custom internal assistant, built on Claude's API with your company knowledge loaded into a RAG system, knows: your product documentation, your internal processes and policies, your frequently asked questions, your brand voice and communication standards, and your team's most common information needs. The result: employees get specific, accurate answers to company-specific questions in seconds, without having to provide context the assistant already has.
Answer questions from your documentation
Load your product documentation, HR policies, SOPs, and knowledge base into the RAG system. Employees ask natural language questions — 'What is our refund policy for annual subscribers?' 'How do I set up a new client account?' 'What is the approval process for expenses above $500?' — and receive accurate, specific answers from your actual documentation rather than generic AI responses.
Draft company-specific communications
A custom assistant loaded with your brand guidelines, tone of voice, past communications, and product knowledge produces drafts that sound like your company — not generic AI output. Sales proposal drafts, customer support responses, internal announcements, and client email templates all benefit from company-specific context.
Onboard new employees faster
New hires spend their first weeks asking experienced colleagues basic questions that are answered in existing documentation — if only they could find it. An internal assistant with all company knowledge loaded reduces the time new employees need from colleagues, accelerates their time-to-productivity, and is available outside working hours.
Analyse and summarise internal data
Pass meeting transcripts, customer feedback, sales call notes, or project status reports to the assistant for instant summaries, action item extraction, and theme identification. What takes a team lead 30 minutes to synthesise from weekly standups takes the assistant 30 seconds.
A Technical Walkthrough
Set up your knowledge base in Bubble
Create a Bubble database with a 'Knowledge Articles' data type: fields for title, content (text, long), category, last updated date, and embedding vector (text field to store the numerical representation of the content). Load your existing documentation by pasting content into this database or importing via CSV.
Generate embeddings for your documents
For RAG to work, each document needs an embedding — a numerical vector representation that enables semantic similarity search. Use the OpenAI Embeddings API (text-embedding-3-small) to generate an embedding for each knowledge article. Store the embedding in the Bubble database. A Bubble backend workflow calls the embeddings API for each article and stores the result.
Build the query interface
Create a Bubble page with a text input for the user's question and a display area for the assistant's response. On question submission, a backend workflow: (1) generates an embedding for the user's question, (2) searches the knowledge base for the 5 most semantically similar articles (using vector similarity — this requires a Bubble plugin or external vector database like Pinecone for production scale), (3) passes the user's question and retrieved articles to Claude via API, (4) displays the response with source citations.
Configure the Claude system prompt
'You are [Company Name]'s internal knowledge assistant. Answer questions using only the provided knowledge base articles. If the answer is not in the provided articles, say so clearly rather than drawing on general knowledge. Always cite which knowledge article your answer comes from. Maintain a [professional/friendly/etc.] tone consistent with [Company Name]'s communication standards.'
Add conversation history and user authentication
Implement Bubble's built-in authentication so the assistant is only accessible to registered employees. Store conversation history in a Bubble database table so users can refer back to previous queries. Pass the last 3–5 conversation turns to the API with each new query to maintain conversational context within a session.
Connect to live business data
Extend the assistant beyond static documentation to live data: current customer records, live inventory levels, recent sales data, active project statuses. Bubble workflows can query the live database and pass current data to the AI. The assistant answers 'What is the status of the Smith account?' with current CRM data, not static documentation.
Proactive knowledge surfacing
Rather than waiting for employee questions, the assistant proactively surfaces relevant knowledge: when a customer support agent opens a ticket, the assistant automatically retrieves relevant documentation based on the ticket category. When a sales rep prepares for a call, the assistant surfaces the account history and relevant case studies. Proactive delivery reduces the friction of looking things up.
Usage analytics for knowledge gap identification
Track every question asked of the assistant. Questions that receive low-confidence responses or that the assistant cannot answer from the knowledge base identify gaps in your documentation. A monthly analysis of unanswered questions is a content roadmap for your knowledge base team — documenting what employees actually need to know.
Want a Custom Internal AI Assistant Built for Your Business?
SA Solutions builds internal knowledge assistants on Bubble.io — loading your documentation, configuring the RAG system, building the chat interface, and integrating with your existing tools.
