Gencore AI for Alfresco

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

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

Key Capabilities:

1. Create Secure Enterprise AI Copilots
   Quickly build AI copilots and knowledge systems by integrating data from multiple sources. Built-in enterprise controls, AI usage monitoring, and end-to-end data lineage ensure safety and transparency.

2. Secure Data Synchronization to Vector Databases
   Ingest and synchronize data securely and at scale from diverse systems. Generate customized embeddings enriched with metadata to prepare enterprise data for LLM-powered use cases.

3. Data Curation and Sanitization for Model Training
   Assemble, clean, and sanitize high-quality datasets to support AI model training and fine-tuning with confidence.

4. AI Interaction Protection
   A conversation-aware LLM Firewall safeguards user prompts, model responses, and data retrievals. It enforces enterprise policies, prevents sensitive data exposure, and protects against threats such as prompt injection and jailbreaking.

Key Features:

1. Unified Enterprise Data Connectivity
   Securely ingest data using hundreds of native connectors, enabling AI applications that leverage structured and unstructured data across SaaS platforms, on-premise systems, public clouds, and data clouds.

2. End-to-End Security Controls Across the AI Pipeline
   Protect AI workflows with layered security, including data sanitization prior to model usage, LLM firewalls for compliant interactions, and continuous monitoring aligned with standards such as NIST AI RMF and the EU AI Act.

3. Complete AI System Visibility and Provenance
   Gain comprehensive insight into data and AI usage, down to individual files, users, models, and AI endpoints.

How to load your Alfresco data in minutes to build your GenAI pipeline:

1. Choose Alfresco as the data source.

2. Select the relevant data system, repository, or other attributes.

3. Define the scope applicable to Alfresco.

4. Apply optional filters, such as:

   • Object prefix (text or regex)

   • Object name (regex-based)

   • Object tags (key-value pairs)

   • File extension category or specific extensions

   • Object size

   • Last modified date

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