Gencore AI for Confluence Data Center

Gencore AI empowers businesses to quickly and safely create enterprise-grade generative AI (GenAI) systems, copilots, and AI agents. It speeds up GenAI adoption by simplifying the process of building pipelines that combine structured and unstructured data with AI, leveraging proprietary enterprise data across hundreds of systems and applications.

You can use any Google Cloud foundation model, including Vertex AI, Gemini, and PaLM 2, as well as third-party models like Anthropic Claude, Meta Llama 2, Mistral, and more.

Core Capabilities:

1. Build Safe Enterprise AI Copilots: Generate AI copilots and knowledge systems by integrating data from multiple sources in minutes, with automatic enterprise controls, usage monitoring, and full provenance tracking.

2. Securely Sync Data to Vector Databases: Ingest and synchronize enterprise data at scale. Create custom embeddings with metadata for vector databases to prepare data for LLM usage.

3. Curate and Sanitize Data for Model Training: Easily assemble, clean, and refine high-quality datasets for AI model training and fine-tuning.

4. Protect AI Interactions: The conversation-aware LLM Firewall safeguards user prompts, responses, and data retrievals, helping enforce enterprise policies and prevent risks such as data leaks, prompt injections, or jailbreaking.

Key Features:

1. Seamless Enterprise Data Connectivity: Safely connect hundreds of native data sources, enabling AI applications across structured and unstructured data in SaaS, on-premise, public, and cloud environments.

2. Inline AI Pipeline Controls: Ensure AI operations comply with policies through layered security, including data sanitization, LLM firewalls, and continuous compliance monitoring (NIST AI RMF, EU AI Act, etc.).

3. Comprehensive AI System Provenance: Track the full lifecycle of your AI systems, including data and AI usage at the file, user, and model levels.

Loading Your Confluence Data Center Data for GenAI Pipelines:

1. Choose Confluence Data Center as the source.

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

3. Define the scope for Confluence Data Center data.

4. Apply optional filters:

   • Object Prefix (text or regex)

   • Object Name (regex)

   • Object Tags (key:value pairs)

   • Extension Category or specific extensions

   • Object Size

   • Last Modified date

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