AI Data Hub
Case Study
AI-Based Enterprise Data Repository
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).
The 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.
Scenario
Transforming Enterprise Data Access Through AI and Automation
Organizations today face growing challenges in managing and securing their data scattered across multiple applications and storage systems. Most legacy systems do not enforce user-level entitlements and lack the capabilities to protect sensitive information like Personal Identifiable Information (PII). We built an intelligent data repository that seamlessly connects to multiple enterprise sources, consolidates and processes data securely, and leverages LLMs to deliver relevant, access-aware answers through platforms like Slack, offering a new way to explore enterprise knowledge.
What we did
We designed a complete AI-powered data pipeline that automates ingestion, enrichment, security, and natural language querying.
- Data Ingestion from Enterprise Systems
Secure integration with platforms like Microsoft SharePoint using Azure credentials to ingest files and documents.
- Access Control Mapping
Enforced file-level and user-level entitlements using Elasticsearch, ensuring only authorized users can access relevant content.
- Automatic PII Detection and Masking
Built data cleansing modules to identify and mask PII before indexing, maintaining data privacy and compliance.
- Encrypted Vector-Based Search
Indexed documents in a FAISS vector database with AES-256 encryption, enabling semantic search with top-tier security.
- Conversational AI via Slack
Integrated a Large Language Model to respond to user queries via Slack, restricted by entitlement logic and delivering secure, natural answers.
- Automated Pipelines with Airflow
Utilized Apache Airflow to automate end-to-end processes including extraction, masking, embedding, indexing, and response handling.
Key features of the experience
The Impact
Centralized Data Access
Unified enterprise information from multiple channels into a single, searchable platform.
Compliance and Privacy Built-In
Ensured enterprise-grade security and privacy with PII masking and data encryption.
Conversational Search Experience
Replaced complex UI navigation with simple AI-powered Q&A via chat.
Operational Efficiency
Automated data handling and minimized manual effort, freeing teams to focus on strategy and innovation.
Future-Ready Architecture
Scalable system with support for 300+ enterprise data connectors, designed to grow with your organization.