Integrated Enterprise Data Sources
Built secure connectors to enterprise platforms such as Microsoft SharePoint using Azure credentials, enabling seamless ingestion of files and documents into a centralized repository.
Case Study
AI Data Hub
Data Template, we’ve developed and deployed a cutting-edge AI-based Enterprise Data Repository, a secure, scalable, and intelligent platform that consolidates enterprise data, enforces fine-grained access control, and enables natural language interaction using powerful Large Language Models (LLMs).
Vision
To empower enterprises with a unified data platform that ensures secure access, data privacy, and AI-driven insights, enabling users to interact with organizational data effortlessly through conversational interfaces while maintaining strict control over who can access what.
The Challenge
Enterprise data was distributed across multiple applications, repositories, and storage systems, making information discovery slow and inefficient. Legacy platforms lacked robust user-level entitlement controls and offered limited protection for sensitive information such as Personally Identifiable Information (PII). Organizations needed a secure way to centralize data, enforce access permissions, and provide users with quick access to relevant information without navigating complex systems.
The Solution
Developed an AI-powered enterprise data repository that integrates with multiple data sources, securely consolidates and processes information, and enforces role-based access controls. The platform leverages Large Language Models (LLMs) to deliver context-aware, entitlement-driven responses through conversational interfaces such as Slack, enabling users to access enterprise knowledge efficiently while maintaining compliance, privacy, and security standards.
What we did
Implemented Secure Access and Privacy Controls
Enforced file-level and user-level entitlements through Elasticsearch and developed automated PII detection and masking capabilities to ensure compliance, privacy, and secure data access.
Enabled Encrypted Semantic Search
Created a vector-based search framework using FAISS with AES-256 encryption, allowing users to retrieve relevant information through secure and context-aware semantic search.
Delivered AI-Powered Knowledge Discovery
Integrated Large Language Models with Slack and automated the entire data processing lifecycle using Apache Airflow, providing users with secure, conversational access to enterprise knowledge.
Key Features
AI-Powered Repository
Centralized platform for secure document management and retrieval.
Role-Based Access Control
Ensures secure access based on user roles and permissions.
PII Data Protection
Automatically detects and masks sensitive information.
Semantic Search Engine
Retrieves relevant documents using AI-powered search.
AI Query & Chat Interface
Answers natural language queries with access-aware responses.
The
Impact
1
Centralized Data Access
Unified enterprise information from multiple channels into a single, searchable platform.
2
Compliance and Privacy Built-In
Ensured enterprise-grade security and privacy through PII masking and data encryption.
3
Conversational Search Experience
Simplified information discovery by replacing complex navigation with AI-powered conversational search.
4
Operational Efficiency
Reduced manual effort through automation, enabling teams to focus on strategic and high-value initiatives.
5
Future-Ready Architecture
Built a scalable platform supporting 300+ enterprise data connectors to accommodate future growth.
Testimonials
Customer Stories
Bank of Kigali TecHouse
Deo Massawe
CEO, Bank of Kigali TecHouse, Rwanda.
"BKTECHOUSE has been using Data Template technology services to help us with our digital transformation journey. We are very pleased with the professionalism of the management team, project management, and various engineering expertise. We are looking forward to our continuing partnership and I highly recommend their services. Thank you for being a great partner."
Feeling inspired?
Reach us at sales@datatemplate.com