AI service

AI Chatbots and Knowledge Assistants

We build AI chatbots and internal knowledge assistants that answer from your own documents and data, and show where each answer came from.

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Definition

What is an AI knowledge assistant?

An AI knowledge assistant is a chatbot that answers questions using your own documents and data instead of only what a general model already knows. It uses retrieval-augmented generation (RAG): it searches your content for the relevant passages, then writes an answer based on them and points to the source. This keeps answers specific to your business and makes them easy to verify.

A general AI model does not know your products, policies or procedures, and it will guess when asked. Retrieval-augmented generation (RAG) solves this by finding the relevant passages in your own content first, then having the model answer from them. Growmerz handles AI chatbot development on that foundation: assistants for your website, your support channel or your internal team that stay within your material, cite their sources and hand over to a person when they should.

Use cases

What it can do for your team

  • Website support assistant

    Answers product, policy and how-to questions from your help content and passes the conversation to a person when needed.

  • Internal knowledge assistant

    Lets employees ask questions across handbooks, procedures, wikis and shared drives from one place.

  • Sales and pre-sales assistant

    Answers detailed product questions from specifications and catalogues, and captures the enquiry for your sales team.

  • Document question answering

    Finds and summarises the relevant clauses in long contracts, manuals or reports, with a reference to the page.

  • Messaging channel assistant

    Brings the same grounded answers to the chat and messaging channels your customers already use.

Outcomes

What changes for you

  • Customers get accurate answers at any hour
  • Your support team handles fewer repeated questions
  • Staff find internal information without asking around
  • Answers come with sources, so they can be checked
  • Knowledge stays current as your documents change

How it works

From first call to a system you can rely on

  1. 01

    Map

    We identify the questions people ask most, who asks them and which documents and data hold the right answers.

  2. 02

    Build

    We prepare and index your content, build the retrieval pipeline and test answers against a set of real questions.

  3. 03

    Launch

    The assistant goes live on the channel you choose, with clear handover to a person and limits on what it will answer.

  4. 04

    Improve

    We review the questions it could not answer, fill the gaps in your content and tune retrieval from real conversations.

Stack

Tools we commonly work with

We choose tools to fit the task and your existing systems, not the other way round.

Language models
OpenAI GPTAnthropic ClaudeGoogle GeminiOpen-source models
Retrieval
Vector databasesEmbedding modelsHybrid searchRetrieval-augmented generation (RAG)
Frameworks
LangChainLangGraphCustom PythonCustom Node.js
Channels
Website chat widgetMessaging platformsInternal chat toolsREST APIs

FAQ

Common questions

What is RAG?

RAG stands for retrieval-augmented generation. Before the AI model answers, the system searches your own documents for the most relevant passages and gives them to the model as context. The answer is then based on your content, not on the model's general memory.

Will the chatbot make things up?

No system removes that risk completely, but RAG reduces it a great deal. We instruct the assistant to answer only from retrieved content, to cite its sources and to say when it does not know. We also test it against real questions before launch.

What content can it learn from?

Most text-based sources work: help articles, PDFs, policy documents, product catalogues, wikis, spreadsheets and records from your systems. During the free AI audit we review what you have and how ready it is.

Is our data kept private?

Your content is stored in a knowledge base set up for your project and is only used to answer your users. We agree with you where it is hosted and which model provider processes it, and for sensitive data we can use self-hosted or open-source models where that is agreed in the proposal.

Can the assistant take actions, not just answer?

Yes. A knowledge assistant can be extended into an agent that books appointments, creates tickets or looks up an order. We usually start with reliable answers and add actions once those are working well.

Other services

Find out what this could take off your plate

Book a free 30-minute AI audit. We map the manual work, tell you what can be automated and what it would involve.

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