Enterprise Intelligence
Case Study
DataBotX - AI-Based Enterprise Repository
We partnered with BK Techouse, a Rwanda-based technology company, to design, develop, and deploy an AI-powered enterprise data repository-an intelligent platform built to unify enterprise data, enforce secure access, and enable natural language interaction through Large Language Models (LLMs).
The Vision
To build a secure, intelligent, and scalable enterprise data solution that consolidates information across platforms, enforces user-level access control, protects PII, and empowers employees to retrieve insights through a conversational AI interface, making enterprise data access as simple as chatting.
Scenario
An AI-Driven Enterprise Data Platform for Secure & Smart Access
BK Techouse wanted to transform the way their organization interacts with data by creating a centralized repository that intelligently consolidates data across multiple sources. The goal was to ensure strict access controls, automated privacy protection, and LLM-powered query handling-all accessible through everyday tools like Slack.
BK Techouse wanted to transform the way their organization interacts with data by creating a centralized repository that intelligently consolidates data across multiple sources. The goal was to ensure strict access controls, automated privacy protection, and LLM-powered query handling-all accessible through everyday tools like Slack.
What we did
We engineered an end-to-end AI data pipeline that handles everything from extraction to intelligent access, focusing on security, compliance, and efficiency.
● Data Extraction : Integrated Microsoft SharePoint via Azure credentials (Client ID, Tenant ID, Secret) to extract structured and unstructured enterprise documents.
● Entitlement Mapping : Stored user-specific access rights in Elasticsearch to dynamically enforce file-level access controls for every query.
● PII Masking : Developed automated pipelines to detect and mask Personal Identifiable Information (PII) before any indexing or storage.
● Vectorized Search with Encryption : Embedded documents into a FAISS vector store, with all data encrypted using 256-bit AES, enabling fast, secure, semantic search capabilities.
● Conversational Interface via Slack : Integrated a Large Language Model (LLM) with Slack to allow users to ask questions and receive personalized, entitlement-aware responses directly in chat.
● Workflow Automation : Employed Apache Airflow for orchestrating daily data refresh, masking, embedding, and indexing operations seamlessly.
Key features of the experience
The Impact
Unified Data Access
Consolidated enterprise information across platforms, eliminating silos and streamlining user access.
Privacy & Compliance by Design
Automated detection and masking of PII ensured the platform adheres to data privacy regulations.
AI-Powered Interactions
Enabled users to interact with enterprise data conversationally using secure and smart LLM integrations.
Productivity Gains
Replaced manual search processes with real-time responses, significantly boosting team efficiency
Scalability & Flexibility
Built to support 300+ enterprise data connectors, future-proofing the system for organizational growth.