Gencore AI

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 that combine structured and unstructured data with proprietary enterprise data from hundreds of systems and applications.

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

Key Capabilities:

1. Build Secure Enterprise AI Copilots
   Quickly create AI copilots and knowledge systems by unifying data from multiple sources. Built-in enterprise controls, AI usage monitoring, and end-to-end provenance tracking are enabled automatically.

2. Safely Sync Data to Vector Databases
   Securely ingest and synchronize data at scale from diverse systems. Generate custom embeddings with rich metadata to prepare enterprise data for LLM-powered use cases.

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

4. Protect AI Interactions
   A conversation-aware LLM Firewall safeguards prompts, responses, and data retrievals. It enforces enterprise policies, prevents sensitive data leakage, and defends against threats such as prompt injection and jailbreaking.

Key Features:

1. Seamless Enterprise Data Connectivity
   Safely ingest data using hundreds of native connectors, enabling AI applications that span structured and unstructured data across SaaS platforms, on-premises environments, public clouds, and data clouds.

2. Inline Security Controls Across the AI Pipeline
   Secure every stage of AI operations with layered protections, including pre-model data sanitization, LLM firewalls for policy compliance, and continuous regulatory monitoring (e.g., NIST AI RMF, EU AI Act).

3. Complete AI System Visibility and Provenance
   Gain full transparency into data usage and AI activity—down to individual files, users, models, and endpoints—across the entire AI lifecycle.