ENGINEERING CASE STUDY · DEMO

KnowledgeOS

RAG Knowledge Assistant

Portfolio concept

KnowledgeOS Search your workspaceNS

Your knowledge, connected.Find answers in what you already know.

Ask a question about your documents

How does our document review process work?

Workflow guide · Source 01

Knowledge library Browse sources ↗

CONTEXT IS EVERYTHING.

PROJECT OVERVIEW

A private knowledge assistant that converts company documents into searchable, citation-aware AI answers using retrieval pipelines and vector search.

ROLEAI Application Engineering

STATUSPortfolio concept

01 / THE PROBLEM

Understanding the workflow.

Useful company knowledge can be difficult to discover across an expanding collection of documents.

02 / THE SOLUTION

A connected approach.

A retrieval-based assistant that answers questions using relevant source passages and visible citations.

03 / ARCHITECTURE

Designed as a system.

Document parsing & chunking

Embedding pipeline

Vector retrieval

Citation-aware response

04 / KEY FEATURES

Purposeful functionality.

Private document library
Semantic search
Source citations
Grounded answers

05 / ENGINEERING CHALLENGES

Beyond the happy path.

Handling incomplete evidence and communicating source context without inventing certainty.

06 / TECHNOLOGY STACK

PythonFastAPIPostgreSQLVector SearchRAGReact

07 / OUTCOME

A foundation to build on.

A demo exploration of a source-grounded knowledge experience.

Live and source links will be added when available.

NEXT PROJECT

Adhyayan

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