Gencore AI for SMB

Gencore AI empowers organizations to rapidly and safely develop enterprise-grade generative AI (GenAI) systems, AI copilots, and intelligent agents. It simplifies GenAI adoption by enabling the construction of AI pipelines that handle both structured and unstructured data across hundreds of enterprise systems and applications.

You can leverage any foundation model available on Google Cloud—such as 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 Secure Enterprise AI Copilots: Quickly combine data from multiple sources to create AI copilots and knowledge systems. Includes automatic enterprise controls, AI usage tracking, and full provenance monitoring.

2. Safely Sync Data to Vector Databases: Ingest and synchronize data at scale. Generate custom embeddings with metadata for vector databases, preparing enterprise data for LLM usage.

3. Curate and Clean Data for Model Training: Assemble, cleanse, and sanitize high-quality datasets for AI training and fine-tuning.

4. Protect AI Interactions: The conversation-aware LLM Firewall safeguards user prompts, responses, and data retrievals in AI workflows.

Key Features:

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

2. Inline AI Pipeline Controls: Ensure safe AI operations with data sanitization before model usage, LLM firewalls for policy-compliant interactions, and ongoing compliance monitoring (NIST AI RMF, EU AI Act, etc.).

3. Complete AI System Transparency: Track every aspect of AI usage and data flow—including each file, user, model, and endpoint—for full system provenance.

How to load your SMB data into a GenAI pipeline quickly:

1. Choose SMB as the Source.

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

3. Define the applicable scope for SMB.

4. Optionally set conditions:

   • Object Prefix or regex pattern

   • Object Name via regex

   • Object Tags (key:value)

   • Extension Category

   • Object Extensions

   • Object Size

   • Last Modified date

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