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SupportEase ChatBot: Enterprise RAG & Knowledge Support Assistant

Enterprise RAG & Knowledge Support Assistant

We built SupportEase ChatBot, a retrieval-based assistant that gives support teams fast, accurate answers from organisational knowledge using large language models, LangChain and a curated knowledge base.

  • Customer Support / Enterprise AI
  • AI & machine learning
  • Project
SupportEase ChatBot case study

The Challenge

What stood in the way.

Support teams needed fast, accurate answers from organisational knowledge stored across multiple document formats.

Knowledge spread across formats

Information sat in documents of different types, which made it slow to search.

Speed

Agents needed answers quickly while handling support requests.

Accuracy

Answers had to reflect the organisation's own knowledge rather than general information.

Our Solution

How we answered it.

We combined large language models and LangChain with a curated knowledge base. The assistant retrieves relevant content and uses prompt chaining, conversation memory and multilingual responses to answer support questions.

  • ProblemKnowledge was stored in multiple document formats.
    Our answerWe brought it into a curated knowledge base that the assistant retrieves from.
  • ProblemAnswers had to be accurate to the organisation's own content.
    Our answerWe used LangChain to ground AI responses in the knowledge base.
  • ProblemSupport conversations often run over several turns and languages.
    Our answerWe added conversation memory and multilingual responses.

How It Works

The parts that made the difference.

Curated knowledge base

Organisational documents are gathered into a curated knowledge base the assistant draws from.

LangChain orchestration

LangChain connects the knowledge base to the language model and manages each step of an answer.

Prompt chaining

Questions are handled through a chain of prompts, so complex queries are broken into steps.

Conversation memory

The assistant remembers earlier turns in a conversation and answers in context.

Results

What changed for the client.

Support teams can now get answers from organisational knowledge through one assistant.

  • Improved access to organisational knowledge
  • Improved support response efficiency
  • Context-aware, multilingual answers from a curated knowledge base

Technology

What it was built with.

Part of our AI and machine learning work.

  • LLM APIs
  • LangChain
  • Custom knowledge base

Let’s Talk

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  2. Discovery callWe talk through your goals, timeline and scope.
  3. A clear proposalA fixed price or monthly rate before any work begins.
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