Knowledge spread across formats
Information sat in documents of different types, which made it slow to search.
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.

What stood in the way.
Support teams needed fast, accurate answers from organisational knowledge stored across multiple document formats.
Information sat in documents of different types, which made it slow to search.
Agents needed answers quickly while handling support requests.
Answers had to reflect the organisation's own knowledge rather than general information.
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.
The parts that made the difference.
Organisational documents are gathered into a curated knowledge base the assistant draws from.
LangChain connects the knowledge base to the language model and manages each step of an answer.
Questions are handled through a chain of prompts, so complex queries are broken into steps.
The assistant remembers earlier turns in a conversation and answers in context.
What changed for the client.
Support teams can now get answers from organisational knowledge through one assistant.
What it was built with.
Part of our AI and machine learning work.
The next big thing starts with a conversation.
An idea, a challenge, a “what if”. Tell us what you have in mind, and we’ll figure out the next step together.
What happens next
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