Gencore AI for HDFS

Gencore AI helps enterprises rapidly build secure, enterprise-grade generative AI (GenAI) systems, copilots, and AI agents. It accelerates enterprise GenAI adoption by simplifying the creation of AI and data pipelines for both structured and unstructured data, powered by proprietary enterprise information from hundreds of data systems and applications.

Organizations can leverage any foundation model available on Google Cloud—including Vertex AI, Gemini, and PaLM 2—as well as leading third-party models such as Anthropic Claude, Meta Llama 2, and Mistral.

Core Capabilities

1. Create Secure Enterprise AI Copilots
Build AI copilots and knowledge systems in minutes by combining data from multiple sources. Built-in enterprise controls provide AI usage monitoring and complete end-to-end provenance tracking.

2. Securely Sync Data to Vector Databases
Ingest and synchronize large volumes of data securely from diverse systems. Generate custom embeddings enriched with metadata to prepare enterprise data for LLM-powered use cases.

3. Curate and Sanitize Data for Model Training
Quickly assemble, cleanse, and sanitize high-quality datasets for training and fine-tuning AI models.

4. Protect AI Interactions
Safeguard prompts, responses, and data retrievals with a conversation-aware LLM Firewall designed to enforce enterprise policies and prevent data exposure.

Key Features

1. Enterprise-Wide Data Connectivity
Securely ingest data using hundreds of native connectors, enabling AI applications that work across both structured and unstructured data in SaaS, on-premise, public cloud, and data cloud environments.

2. Inline Security and Governance Controls
Protect the entire AI pipeline with layered security, including pre-model data sanitization, LLM firewalls for policy enforcement, and continuous compliance monitoring aligned with standards such as NIST AI RMF and the EU AI Act.

3. End-to-End AI System Visibility
Maintain complete visibility into data and AI usage across the organization—down to individual files, users, models, and usage endpoints.

How to Load HDFS Data to Build Your GenAI Pipeline in Minutes

1. Select HDFS as the data source.

2. Choose the relevant data system, bucket, or attribute.

3. Define the appropriate HDFS data scope.

4. Apply optional filters, such as:

   • Object prefix (text or regex)

   • Object name (regex-based)

   • Object tags (key:value pairs)

   • Extension category

   • File extensions

   • Object size

   • Last modified date

5. Click Save and Continue to proceed to the Data Sanitizer stage.