Build. Collaborate.
Stay at the frontier.
A professional intelligence hub for building agentic AI workflows, evaluating foundation models, and connecting with a community of industry leaders.
127
Members
342
Workflows
1247
Completed
Guided Analysis
Step-by-step wizard for structured workflows. Choose a goal, select data, configure methods — the platform handles the orchestration. No code required.
AI Copilot
Natural language interface backed by specialized agents. Ask questions, get grounded answers with citations. Agents route to the right tools and models.
Expert Notebook
Interactive code environment for full control. Write Python, build pipelines, run experiments. Jupyter-compatible with integrated model access and MCP tools.
Foundation Models
Gemini 2.5 Flash
Fast, efficient reasoning model for high-throughput agentic tasks. Excellent cost-performance ratio.
Gemini 2.5 Pro
Most capable reasoning model with deep thinking. Best for complex multi-step workflows.
Claude Sonnet 4
Balanced intelligence and speed. Strong at code generation, analysis, and nuanced reasoning.
Claude 3.5 Haiku
Fastest Anthropic model. Ideal for real-time agent routing and lightweight tasks.
Tutorials
Your First Agentic Workflow
RAG Pipeline from Scratch
Multi-Agent Orchestration
Tool Use with MCP
Model Comparison & Evaluation
Community
Which model handles regulatory text best in production?
Dr. Sarah Meier · 24 replies · 2 hours ago
MCP tools we've built — share yours
Marco Bernasconi · 18 replies · 5 hours ago
Paper discussion: Causal Foundation Models reliability concerns (April 2026)
Prof. Anna Kovács · 31 replies · 1 day ago
Build an explainability agent for automated decisions
Create an agent that can explain any ML model's decision in natural language, with causal reasoning and counterfactual explanations. Must comply with EU AI Act Article 86 requirements.
Evaluate & Govern
Model Arena
Side-by-side comparison
Compliance Sandbox
Regulatory testing
Evidence Packages
Signed audit artifacts
Cost & Carbon
Sustainability metrics
Research Feed
Causal Foundation Models: Promise and Production-Readiness
LLM Agents as Causal Orchestrators, Not Causal Reasoners
Structured Outputs at Scale: Constrained Decoding in Production
The MCP Standard: Universal Tool Integration for AI Agents
Evidence Packages
Signed, tamper-proof artifacts. Every analysis generates a traceable compliance document.
Methodology
Data Profile
Results
Validation
Limitations
Decision Trace
Instructor Console
Moodle LTICohort Management
Create cohorts, assign students, set programme dates. Integrates with Moodle via LTI.
Content Management
Create and organise tutorials, scenarios, and exercises. Align with any CAS programme structure.
Budget Configuration
Set per-user and per-cohort budgets for model API usage. Monitor spending in real-time.
Scenario Configuration
Configure evaluation scenarios with custom rubrics, datasets, and scoring dimensions.
Live Monitoring
See student activity in real-time — who is working, which models they're using, where they're stuck.
Review & Assessment
Review student workflows, evidence packages, and notebook submissions. Export grades to Moodle.
Trust & Safety
Grounded · Honest · Compliant · TransparentGrounded
Every claim backed by evidence
Source Attribution
Hallucination Detection
Calibrated Uncertainty
Honest
Pushback over agreement
Honest Disagreement
Multi-Model Consensus
Built-in Red Teaming
Compliant
Regulation-ready by design
EU AI Act Readiness
Signed Evidence Packages
Swiss Data Sovereignty
Transparent
Nothing hidden, everything traceable
Full Decision Trace
Reproducibility by Design
Cost & Carbon Accounting
Expert Network
Dr. Sarah Meier
Head of AI, Swiss Re
Marco Bernasconi
Principal Engineer, PostFinance
Lucas Tran
VP Analytics, Zurich Insurance
Dr. Elena Rossi
Director, Data Science, UBS
Portfolio Showcase
Multi-Model Regulatory Review Agent
by Dr. Elena Rossi
Agentic Document Q&A with Evidence Trail
by Thomas Gruber
Cost-Optimized Routing Agent
by Marco Bernasconi
Guest Speakers
Dr. Ilya Sutskever
Co-founder, SSI
What AI Safety Means for Enterprise Deployment
Dr. Judea Pearl
Professor, UCLA
Causal Reasoning in the Age of Large Language Models
Amanda Askell
AI Policy Lead, Anthropic
Designing AI Systems That Know What They Don't Know