An AI chatbot for business that answers from your own documents, with sources

What is an AI chatbot for business, and what makes it trustworthy?

It is an assistant that answers in plain language from a defined body of company knowledge. What makes it trustworthy is not the model but the design: it answers only from retrieved passages, cites them, and refuses when the evidence is missing.

Generic chatbots fail because they answer from the model's general training, which sounds confident and is often wrong about your products, prices and policies. Ours is grounded. Every answer links to the page, section or file it came from, so a customer can check it and an employee can open the full policy in one click.

How does retrieval with citations work?

Retrieval-augmented generation, usually shortened to RAG, works in two stages. First, your documents are split into passages and stored in a search index that understands meaning, not just keywords. Then, for each question, the system pulls the closest passages and asks the model to answer only from them, quoting where each point came from.

Good RAG development is mostly about the first stage. We split documents along their real structure (headings, clauses, table rows) rather than fixed lengths, attach metadata such as product, region and effective date, and combine meaning-based search with keyword search so part numbers and policy codes are found exactly. Indexes live in Pinecone, Qdrant, Supabase with pgvector or Azure AI Search, depending on where your data is allowed to sit.

Which sources can the AI knowledge base include?

Most text your teams already maintain can go in: SharePoint sites and document libraries, Google Drive folders, help center articles, PDFs, Word files, spreadsheets and exported wiki pages. Solved tickets from Zendesk can be added too, which helps the bot answer the long tail of questions your help articles never covered.

The knowledge base stays in sync. We schedule re-indexing or trigger it when files change, remove passages from deleted or superseded documents, and record each source's last update date. A policy updated on Monday is what the bot cites on Tuesday.

• SharePoint and OneDrive libraries.
• Google Drive and Google Docs.
• Zendesk help center articles and solved tickets.
• PDF manuals, contracts and product sheets.
• Structured data such as price lists or spec tables.

Where can the chatbot live: website, Slack, Teams or Zendesk?

Wherever the questions are asked. A public website widget serves customers with public content only. An internal assistant in Slack or Microsoft Teams serves staff with internal content filtered by their permissions. Inside Zendesk, the bot can draft a reply with sources for the support agent to edit and send.

The same knowledge base can power all three with different rules for each channel. Public bots get tighter scope and a clear route to a human, such as a ticket form or live chat handoff. Internal bots can cover HR policy, IT how-tos, sales collateral and SOPs, each section visible only to the groups that should see it.

Each channel also gets its own analytics: questions asked, answer rate, escalation rate and the topics people return to most. Support leads use that view to decide which help articles to write next, and HR or IT teams use it to spot policies that staff keep misreading.

How do you stop the chatbot from making things up?

By refusing to answer without evidence, and by measuring it. If retrieval returns nothing above a relevance threshold, the bot says it could not find an answer and offers to pass the question on. The model is instructed to use only the supplied passages, and a check confirms each answer includes at least one valid citation.

Before launch we build an eval set of real questions with correct answers, including questions the bot should decline. We score accuracy, citation correctness and refusal behavior, and rerun the set after every document sync or prompt change. Questions that end in escalation are logged, which shows you exactly which documents are missing or out of date.

Is our data safe in an AI chatbot?

It can be, with the right account terms and architecture. We call models only through API or enterprise tiers rather than consumer apps, keep the index in your own cloud account where required, and log prompts and outputs so you can audit what was asked and answered.

Permissions are enforced at retrieval time: the bot only searches passages the user is allowed to see, so an internal assistant cannot leak a salary sheet to someone outside HR. For regulated content, models can run through Amazon Bedrock, Microsoft Foundry or as open-weight models inside your network, and any health data requires a signed BAA with each vendor that touches it.