What They Do

Brize is a platform focused on improving how users interact with organizational knowledge. Its goal is to help teams access the right information quickly, understand it in context, and learn more effectively, without searching across scattered documents or relying on manual knowledge sharing.

As organizations grew and internal content expanded, Brize identified a recurring challenge: information existed, but access, accuracy, and context were breaking down.

The Challenge

At Brize, the challenge was not conversation - it was trustworthy knowledge retrieval and contextual understanding.

Traditional chatbots and search tools struggled because they:

• Returned generic or incomplete answers
• Lost context across follow-up questions
• Failed to ground responses in authoritative organizational data
• Could not summarize information meaningfully for different users

Brize needed an AI system that could retrieve the right information, reason over it, and respond accurately while staying grounded in internal data.

This required Retrieval-Augmented Generation (RAG), not a standalone generative model.

Services Delivered

End-to-End AI Product Development

AI Strategy · RAG Architecture Design · Backend Engineering · AI Integration · Knowledge Retrieval Pipelines · UX Enablement · Testing & Optimization

Technology Stack

• AI & LLMs: Gemini
• Backend & Orchestration: Python
• Database: PostgreSQL
• Frontend: Streamlit

The NonStop Solution

A RAG-Powered Intelligent Knowledge Assistant

NonStop designed and implemented a Gen-AI-enabled chatbot for Brize, built on Retrieval-Augmented Generation (RAG) to ensure responses were accurate, contextual, and grounded in organizational data.

Instead of relying on the LLM’s general knowledge, the system retrieves relevant internal content first, then uses the LLM to generate responses strictly based on that information.

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How AI Was Implemented

1. Retrieval-Augmented Generation (RAG) Architecture

NonStop implemented a RAG pipeline where:

• User queries trigger intelligent document retrieval from internal data sources
• Relevant content is fetched and ranked
• The LLM generates responses using retrieved data as context

This ensures fact-based answers, not hallucinated outputs.

2. Context-Aware Question Answering

The AI maintains conversational context across interactions, allowing users to:

• Ask follow-up questions naturally
• Dive deeper into topics without restating intent
• Receive consistent, coherent responses across sessions

This transforms the system from a Q&A tool into a learning assistant.

3. Accurate, Grounded Responses

By combining retrieval with generation, the system delivers:

• Precise answers sourced from organizational knowledge
• Reduced misinformation risk
• Higher trust in AI-generated responses

Accuracy is enforced by design, not post-processing.

4. AI-Driven Summarization

Generative AI was used to create:

• Topic summaries for quick understanding
• Custom summaries tailored to user needs
• On-demand summarization for documents or conversations

This allows users to consume large volumes of information efficiently.

5. Personalized User Interaction

AI adapts responses and summaries based on:

• User intent
• Query context
• Interaction flow

This personalization improves engagement and learning efficiency without manual configuration.

The Impact

From Search to Intelligent Knowledge Interaction

The AI-driven solution enabled Brize to:

• Improve access to organizational knowledge
• Deliver accurate, context-aware answers at scale
• Enhance learning through AI-generated summaries
• Reduce time spent searching for information
• Increase user engagement with internal content

Users moved from manual search and fragmented answers to guided, intelligent interactions.

Why NonStop

This engagement highlights NonStop’s strengths in:

• Designing RAG-based AI architectures
• Implementing enterprise-grade Gen-AI systems
• Orchestrating retrieval and generation workflows
• Translating AI outputs into usable product experiences
• Moving AI from concept to production-ready systems

Want to Build AI-Powered Knowledge Systems?

At NonStop, we help teams build AI systems where Generative AI and retrieval work together to deliver accurate, contextual, and trustworthy experiences.

From RAG-powered chatbots to enterprise learning platforms, we design AI that actually works in real environments.