**Last Updated:** March 2026 **Website:** [clairelabs.ai](http://clairelabs.ai) --- ## Introduction **Claire Labs** is a research-to-market initiative bringing responsible, human-centric AI into high-risk environments in higher education. We build an **end-to-end platform for agentic, conversational learning**: AI agents and workflow tools that support dynamic, structured dialogue so educators can evaluate how students think, adapt, and communicate — not just what they submit. ### --- ## Company Information ### Founding & Location - **Founded in:** Hong Kong - **Date:** June 2025 ### Founders See https://www.clairelabs.ai/about 1. **Alexander Hein** - Adjunct Professor, HKBU School of Business 2. **Nils Hein** - Ex-Microsoft Lead Architect, former CTO ### Backing & Partnerships - **INSEAD AI Venture Lab** (supported by Harvard University) - **Google Cloud** ### Research Partners - University of Zürich --- ## Mission & Core Value Proposition See https://www.clairelabs.ai/about ### Three Core Pillars 1. **Conversational learning with human-AI collaboration at its core** - Human-centric design - Educator-in-the-loop workflows - Responsible AI to streamline assessment, feedback, assignments, and learning scenarios - Built from the ground up for human-AI collaboration - Educators build custom agents for any oral or hybrid exercise 2. **Regulation-informed guardrails to protect users** - Compliance as a unique selling point (USP) - Aligned with GDPR & EU AI Act - Supports human intervention - Requires approval for AI content - Ensures system transparency - Mitigates/prevents automation biases 3. **Secure, scalable infrastructure for agentic AI** - Innovate, adapt, and scale assessment, feedback, and conversational learning scenarios - Deploy agents across assessments, assignments, exercises, and learning activities - Shift focus from product (submission) to progress (mastery) through dialogue - Help students retain feedback more effectively - Assess students' agency and ownership over their work --- ## The Problem Claire Labs Solves See https://www.clairelabs.ai/ ### The Assessment Integrity Problem Generative AI has made written output dramatically cheaper to produce. As a result, many traditional take-home assignments and written assessments are losing signal: they increasingly measure prompt skill and tooling access rather than reasoning, mastery, and student agency. ### The Shift to Oral Assessment (and Its Limits) In response, universities are turning to higher-signal formats like oral exams and interactive assessments. But human-led oral assessment is hard to run well at scale: - **Not scalable and expensive** — scheduling, staffing, moderation, and documentation become prohibitive in large cohorts. - **Requires upskilling** — students need preparation for the format (communication skills, confidence, structure), and instructors need training for consistent questioning and rubric calibration. - **Fairness is non-trivial** — bias mitigation is essential to ensure equitable outcomes across accents, neurodiversity, and cultural backgrounds. The result is an assessment gap: written assessment is becoming less trustworthy, while high-integrity alternatives are too resource-intensive to deploy broadly. ## Product primitives (current) - **Agent builder** — educators can write simple instructions, and Claire turns them into structured playbooks they can review before deployment. - **Oral scenarios** — students speak directly with an AI agent without submitting work first. - **Hybrid scenarios** — students submit work first, then continue into a dynamic interview that probes agency, ownership, and understanding. - **Tool-augmented conversations** — agents can present images, lecture slides, and whiteboards during interviews and exercises. - **Review and annotation workspace** — educators can review submissions and transcripts manually in a dedicated interface built for fast, high-quality assessment workflows. - **AI-assisted grading and feedback** — Claire can draft repetitive remarks and recommend rubric-aligned scoring, but only from educator remarks and approved AI suggestions. - **Performance analytics** — educators can query performance and feedback data conversationally to identify patterns, blind spots, and learning signals. - **Audit reporting** — exportable audit trails document AI use on a submission basis for internal and external stakeholders. - **Student feedback publishing** — publish feedback reports to students after educator review and sign-off. --- ## Research Foundation See https://docs.clairelabs.ai/research/overview Claire Labs is informed by the latest research in educator-AI collaboration, assessment, and feedback. --- ## Key Differentiators ### 1. Compliance-First Design Unlike generic AI tools, Claire Labs is built specifically for the high-risk educational environment with compliance as a core feature, not an afterthought. ### 2. Human-in-the-Loop by Default Draft remarks and observations are always pending until explicitly approved by educators, preventing automation bias and maintaining academic integrity. ### 3. Research-Backed Approach Platform development is informed by cutting-edge research in educator-AI collaboration and assessment practices. ### 4. Educator Augmentation vs. Automation Philosophy of enhancing educator expertise rather than replacing it, maintaining the critical role of human judgment in assessment. ### 5. Transparent AI Usage Students are always informed about AI use through clear disclaimers and transparent communication. ### 6. Agentic AI Capabilities Unique conversational learning agents that shift focus from product (submission) to process (mastery and agency). Educators build custom agents for any learning scenario, with tool call support for multimodal interactions. ### 7. European Data Protection EU-based data storage and processing ensures highest standards of privacy and data protection. --- ## Contact & Resources Request a demo: https://www.clairelabs.ai/request-demo Our help center: https://docs.clairelabs.ai/ ### Website [clairelabs.ai](https://clairelabs.ai/) ### Demos - **AI Copilot Demo:** [links.clairelabs.ai/demo](https://links.clairelabs.ai/demo) - **Company Deck:** https://links.clairelabs.ai/deck --- ### Version Information **Document Version:** 2.0 **Based on Materials:** - Claire Labs Update 2026 (March 2026) - Claire Labs Introduction (October 2025) **Status:** Current as of March 2026 **Platform Status:** Live --- *This summary is intended for AI agents interacting with users about Claire Labs. For official information, please refer to [clairelabs.ai](https://clairelabs.ai/).*