BRD: conversational access to banking knowledge

An AI assistant using documents from a knowledge base to answer questions in natural language.

Published June 22, 2026 · Updated October 27, 2026

BRD: conversational access to banking knowledge
Client / project
BRD
Focus
AI Automation

From our project portfolio

15+ Years of consulting & engineeringISO 27001 Information security
BRD Groupe Société GénéraleB&H Photo VideoSelgros Cash & CarryPPCCouncil of EuropeDatecsPayJerry's PizzaChristian Tour

Project context

The BRD project addresses access to banking information. Knowledge-base materials provide the context used by the assistant.

The challenge

A chat interface is useful when it finds information relevant to a question. The challenge is connecting the user’s question, available documents and the resulting answer.

Project objectives

  • Access knowledge through natural-language questions.
  • Retrieve relevant material from the connected knowledge base.
  • Use documents as the context for answers.

Solution and implementation

UP2DATE implemented an AI assistant connected to a banking knowledge base.

  1. The user asks a question in the conversational interface.
  2. The system retrieves relevant documents.
  3. The retrieved context informs the answer.

This workflow connects the AI interface to the available knowledge materials.

Delivered outcome

The assistant provides a way to explore connected information through questions. Answer quality depends on the available documents and evaluation; decisions requiring specialist judgment remain subject to human review.

What to take away

For a similar assistant, define its document sources and how answers will be checked. Knowledge access needs explicit evaluation criteria.

Focus and technologiesAI AutomationGPT-4PythonFastAPI

UP2DATE’s contribution, step by step

The document and the answer are evaluated separately

The implemented assistant uses a banking knowledge base as context for the conversation.

  1. Question

    The user expresses their request in natural language.

  2. Retrieval

    The system searches for relevant documents in the connected knowledge base.

  3. Answer

    The retrieved material is used as context to formulate the response.

For a similar project, evaluation checks both information retrieval and the support for the answer. Quality depends on the sources and validation of its use; accuracy is not guaranteed.

AI evaluation and integration →

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