Slaunt is an all-in-one agentic AI solution that turns scattered work signals into structured, queryable context. It creates organizational memory that both humans and AI can reason from using the Central Context Repository (CCR).

How does Slaunt integrate with AI tools?

Slaunt uses the MCP (Model Context Protocol) to integrate with AI assistants and tools like ChatGPT, Claude, Cursor, and custom AI agents, providing them with structured organizational context for better reasoning and decision-making.

Is Slaunt free to use?

Yes! Slaunt offers a free Starter tier for individuals and small teams with core features. Professional ($29/seat/month) and Enterprise tiers are available for larger organizations with advanced needs.

QUAERO is Slaunt's Remote Company Agent Control (RCAC) system for enterprise AI governance. It provides centralized management, monitoring, and control of AI agents across your organization.

What is the MCP Protocol?

MCP (Model Context Protocol) is a standard protocol for AI tool connectivity that Slaunt natively supports. It allows AI assistants to access structured organizational context and perform actions within your workflow.

How is Slaunt different from Notion or Confluence?

Unlike traditional documentation tools, Slaunt is built AI-first with structured, queryable context rather than static documents. It provides real-time organizational memory that AI can reason from, not just search.

research lab

👤

H3L-BENCH (Human-in-Loop Benchmark)

4/13/2026

Muhammad Elfsaid, Tu Trinh, Kelvin Lian, Guanpé Lun, Nathan Hunt, Ernesto Hernández, Nandan Marasha, Yan...

Research

🛡️

Defensive Refusal Bias: How Safety Alignment Fails Cyber Defenders

3/12/2026

David Campbell, Neil Kale, Udari Madhushani Sehwag, Bert Huang, Nick Price, Dan Borges, Alex Levine...

Safety

🧬

LLM Novice Uplift on Dual-Use, In Silico Biology Tasks

2/26/2026

Chen Bo Calvin Zhang, Christina Q. Knight, Nicholas Kruus, Jason Hausenloy, Pedro Medeiros, Nathaniel...

Safety

🤖

VeRO: An Evaluation Harness for Agents to Optimize Agents

2/25/2026

Varun Ursekar, Apaar Shanker, Veronica Chatrath, Yuan Xue, Sam Denton

Agents, Post-Training, Evaluation and Alignment

🎯

LHAW: Controllable Underspecification for Long-Horizon Tasks

2/12/2026

George Pu, Michael S. Lee, Udari Madhushani Sehwag, David Lee, Bryan ZHU, Yash Maurya, Mohit Raghavend...

Agents, Safety, Evaluation and Alignment

🔬

SciPredict: Can LLMs Predict the Outcomes of Research Experiments in Natural Sciences?

1/15/2026

Udari Madhushani Sehwag, Elaine Lau, Haniyeh Ehsani Oskoule, Shayan Shabbi, Erich Liang, Andrea Tokad

Safety, Evaluation and Alignment

✅

Agentic Rubrics as Contextual Verifiers for SWE Agents

1/6/2026

Mohit Raghavendra, Arosha denul, Bing Liu, Tonghong He

Agents, Safety, Evaluation and Alignment

⚖️

MoReBench: Evaluating Procedural and Pluralistic Moral Reasoning in Language Models, More than Outcomes

12/22/2025

Yu Yang Chiu, Michael S. Lee, Rachel Calcott, Brandon Handoko, Paul de Font-Reaulx, Paula Rodriguez, C...

Reasoning, Safety