Humanloop
To enable the safe and rapid adoption of AI by providing the first integrated development environment (IDE) for Large Language Models (LLMs).
At a Glance
- Enterprise AI teams
- AI Startups
- Product Teams building LLM apps
AI Tools by Humanloop
(1)
Humanloop
LLM Evaluation and Prompt Management
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Latest News
Anthropic acquires Humanloop team to bolster enterprise AI strategy
Humanloop moves to General Availability, setting standards for AI engineering
Humanloop raises $2.6M from Index Ventures to help humans teach AI algorithms
UCL Technology Fund invests in AI spinout Humanloop
Products & Services
An end-to-end platform for prompt engineering, evaluation, and observability of LLM applications.
Developer tools to integrate LLM evaluation and logging into existing codebases.
Market Position
Humanloop positioned itself as the 'IDE for LLMs,' focusing on evaluation and safety, which led to its strategic acquisition by Anthropic.
Leadership
Founders
Raza Habib
Co-founder and CEO. Holds a PhD in Machine Learning from UCL (University College London). Previously focused on AI research and uncertainty in machine learning.
Peter Hayes
Co-founder and CTO. PhD in Machine Learning from UCL and graduate of Trinity College Dublin. Researcher in AI uncertainty.
Jordan Burgess
Co-founder and CPO. Holds an MPhil in Machine Learning from Cambridge University. Background in product management for AI/ML tools.
Emine Yilmaz
Co-founder and Advisor. Professor at UCL and Amazon Scholar. Research expert in information retrieval and NLP.
David Barber
Co-founder and Advisor. Professor of Machine Learning at UCL and Director of the UCL Centre for Artificial Intelligence.
Executive Team
Raza Habib
CEO
PhD in ML from UCL. Lead visionary for the LLM developer platform.
Peter Hayes
CTO
PhD in ML from UCL. Led the technical development and infrastructure of the Humanloop platform.
Board of Directors
Founding Story
Founded as a spinout from UCL by a team of machine learning researchers (PhD students and professors) who wanted to bridge the gap between AI research and practical, reliable application development.
Business Model
Revenue Model
SaaS subscription-based model with tiers based on usage (logs) and team size.
Pricing Tiers
Up to 2 members, limited logs per month (approx 50).
Increased limits and features for small teams.
Full evaluation suite, RBAC, VPC deployment, and high-volume observability.
Target Markets
- Enterprise AI teams
- AI Startups
- Product Teams building LLM apps
- Testing and evaluating LLM application performance
- Prompt engineering and version control
- Monitoring AI agents in production
- Gathering human feedback to improve model outputs
- Gusto
- Vanta
- Duolingo