Toptal ExpertCrowd Expert Data and Evaluation Platform
Positioned as more than extra labelling: highly specialised human judgment applied to model training and evaluation across finance, healthcare, legal, biotech and cybersecurity
Tool Overview
Details, capabilities and usage guidance
Features
- ✓ Expertise, not generic crowdsourcing: Toptal says the platform draws on more than 30,000 specialised domain experts across 140+ countries, including more than 2,000 AI and big data specialists, 12,000 professionals with advanced degrees and 1,000 PhD-level AI specialists, available on demand
- ✓ An in-house AI sourcing engine: Toptal says the AI-driven matching engine was developed in-house by its AI research team, composed of more than a dozen PhDs and researchers from institutions such as Harvard, EPFL and the University of Liege, and uses 15 years of Toptal's proprietary data in sourcing, screening and matching experts
- ✓ Works with mainstream RL methods: Toptal says AI researchers applying GRPO, PPO and other RL techniques have already used the platform to further the development of their models and systems
- ✓ Multimodal annotation and live tracking: Toptal says the platform offers multimodal annotation with customisable workflows and live project tracking, giving real-time progress updates while domain experts do the work
- ✓ Comes with the ExpertCrowd-SWE benchmark: Toptal says it evaluates state-of-the-art AI coding agents on previously unseen issues and spans 87 codebases in 11 programming languages, with every task and grading rubric authored by engineers from Toptal's talent network
How to Use
- Read Toptal's official press release to confirm what ExpertCrowd covers: RLHF, RL data generation, data labelling and model evaluation
- Define the domain and expert profile the project needs; Toptal says the platform spans financial services, healthcare, legal, biotechnology, cybersecurity and more
- Use the AI-driven sourcing engine as documented by Toptal to match experts, then run the project with customisable workflows and live tracking
- To evaluate coding agents, review the official ExpertCrowd-SWE benchmark - Toptal says it spans 87 codebases in 11 programming languages - while noting it is Toptal's own benchmark
FAQ
What is ExpertCrowd?
Toptal's official press release says ExpertCrowd is a platform for training and improving AI models and systems through RLHF, RL data generation, data labelling and model evaluation. Toptal says it combines its platform for AI model development and testing with its global network of specialised talent to help frontier labs, enterprises and startups build more accurate and capable models and systems.
How is it different from generic crowdsourced labelling?
Toptal says the platform enables organisations to move beyond generalised crowdsourcing and data annotation and apply highly specialised human judgment to AI applications and models across industries including financial services, healthcare, legal, biotechnology and cybersecurity.
How large is the expert network?
Toptal says the platform is powered by its global talent network of more than 30,000 specialised domain experts across 140+ countries, including more than 2,000 AI and big data specialists, 12,000 professionals with advanced degrees and 1,000 PhD-level AI specialists, available on demand.
What commercial figures did Toptal publish?
Toptal says that prior to the official launch, ExpertCrowd had generated over $5 million in recurring revenue from work with frontier and neo labs, as well as companies including Scale AI, Sterling Labs and Vetto that service frontier labs.
What is the ExpertCrowd-SWE benchmark?
Toptal says it evaluates state-of-the-art AI coding agents on previously unseen issues, spanning 87 codebases in 11 programming languages, and that it exposes the most significant deficiencies in model capability relative to any other currently published SWE benchmark. Toptal says engineers from its talent network led development of the benchmark and authored every task and grading rubric, and that researchers including several PhDs from Toptal's AI team reviewed the methodology and results.
How does Toptal frame the benchmark's significance?
The release quotes Toptal founder and CEO Taso Du Val saying the benchmark's results expose more deficiencies than any other published benchmark on leading models such as Opus 5.5 and Fable 5.1, and that this demonstrates the ability to push frontier AI forward. He adds that developing frontier models requires not only powerful compute but specialised human expertise to train, evaluate and continuously improve them. These are Toptal's own claims.