Glean (Glean Technologies) vs Siemens

Governs model traffic, tool calls and enterprise AI context flowing through third-party AI applications.

Build, deploy and orchestrate enterprise AI agents at scale.

Build custom generative AI apps and agents on a company's own knowledge.

Enterprise-context AI coworker that answers questions, summarizes documents and takes action grounded in a company's own connected data.

Builds enterprise context for AI from a company's applications, people and relationships (the knowledge/enterprise graph underlying Glean's other products).

Model-routing and cost-optimization layer for the Glean platform: routes each task to the best of 40+ LLMs by cost/performance, runs continual memory learning across surfaces, and orchestrates an agent harness for long-running work.

Governs AI wherever work happens: sensitive-data and AI-interaction protection, agent-permission enforcement, prompt-injection defense (96.9% detection accuracy) and end-to-end audit logging.

Permissions-aware enterprise search across 100+ connected apps, using deep-learning semantic understanding and a company knowledge graph for personalized, real-time-indexed results.

Announced ("coming soon") module that maps enterprise workflows, ranks AI-automation opportunities, drafts agent blueprints for Glean Agent Builder, and tracks ROI of AI adoption.

An intelligent company homepage that brings work, knowledge and agents into one surface.

Searches a company's connected applications and runs assistants and agents over what it finds.

Generative-AI assistant for industrial engineers that answers equipment-troubleshooting questions by chat, generates and debugs automation code, and supports digital-twin simulation, built with Microsoft Azure AI.

Ready-to-use AI visual quality inspection system that trains on roughly 20 good samples to detect anomalies, deviations and missing parts on a production line without machine-vision or AI expertise.

Cloud predictive-maintenance software that applies industrial AI to machine and process data to forecast asset failures, rank risk across sites and prioritise maintenance work.

Generative and agentic AI system for Siemens EDA tools that ingests and vectorises multimodal design data, exposes a natural-language interface across the design workflow and automates debugging, with customer-choice LLM support.

Edge software suite for packaging, deploying, running and monitoring AI models on the factory floor, comprising an AI SDK, AI Inference Server and AI Asset Manager running on Siemens Industrial Edge and NVIDIA-accelerated industrial PCs.

Visual drag-and-drop and notebook-based platform in the Rapidminer portfolio for building, training and explaining machine learning and generative AI models, with AutoML tooling and deployment to cloud, on-premises or edge infrastructure.

Enterprise knowledge-graph platform in the Rapidminer portfolio, built on open W3C standards (RDF, SPARQL, OWL, SHACL), that federates cross-domain data into a governed semantic layer and gives AI agents a queryable, traceable context for grounded answers.