Handshake spent a decade as the career network for university students, then re-founded itself around AI almost overnight. Handshake AI launched in early 2025, turning that recruiting reach into an expert data business: PhDs and specialists producing reasoning chains, preference rankings, and domain evaluations for frontier labs. By press reports it reached roughly a billion dollars in gross annualized revenue within its first year and a half. Growth that fast puts a vendor on every shortlist, and it puts "Handshake AI alternatives" into search bars, because nobody builds an AI program around a single supplier anymore.
This guide covers who competes with Handshake AI in each lane, and when the better answer is not an expert network at all but the underlying data.
What Handshake AI is known for
Handshake AI's edge is where its experts come from: the parent company's career network of roughly 18 million students and alumni across more than 1,600 universities. That gives it unusual reach into PhD-level talent in mathematics, physics, and computer science, onboarded through its MOVE Fellowship and paid by the hour to produce expert demonstrations, reasoning data, and evaluations. It also acquired Cleanlab, a data quality company, to put automated label auditing on top of the human work.
The profile that emerges is academic depth: if you need graduate-level reasoning data in the hard sciences, Handshake AI's pipeline of university talent is exactly shaped for it.
Teams look for alternatives for familiar reasons. Vendor diversification first, the lesson the market keeps re-learning. Fit second: an academic network is strongest on academic domains, and programs that need working practitioners, enterprise workflows, or multilingual breadth may find other networks deeper there. Maturity third: the AI division is young, and some buyers want vendors with longer track records in their specific lane. And as always, some teams discover mid-evaluation that their real gap is data that exists in the world, which no expert can write to order.
Alternatives for expert data and demonstrations
The direct competitors are the other expert networks. Mercor is the biggest name, with an expert marketplace spanning software, medicine, law, and finance, and it has been expanding into evals and RL environments by acquisition. Micro1 runs a similar recruit-and-vet model, and our guide to micro1 alternatives breaks down how it compares. Surge AI is the premium name for human feedback and expert labeling. Scale AI runs some of the largest programs, with the Meta ownership caveat every buyer now weighs. Turing specializes in expert engineers and coding data. AfterQuery sells expert data with published benchmarks, and Toloka and Prolific come at it from managed crowds and vetted research participants. For that participant-pool side, see our guide to Prolific alternatives.
For the human feedback lane specifically, our guide to RLHF data providers maps the market in depth.
When the answer is data, not experts
Expert networks produce knowledge work to order: a physicist writes the ideal derivation, an engineer completes the task, a rater ranks outputs. That is the right purchase when your model lacks judgment or domain skill.
It is the wrong purchase when your model lacks contact with the real world. Video models need real motion. Voice agents need natural conversation. World models need gameplay and first-person footage. Agents need genuine workflow traces. No PhD can write that data; it has to be captured from reality and licensed from whoever owns it. That is a different market, covered in our guides to AI training data marketplaces and where AI labs source training data.
The landscape
| Provider | Category | Best for |
|---|---|---|
| Handshake AI | Expert network | PhD-level reasoning data from a university pipeline |
| Mercor | Expert marketplace | Domain-expert demonstrations, evals, environments |
| Micro1 | Expert recruiting | Vetted specialists, recruit-and-vet model |
| Surge AI | RLHF and evaluation | Frontier-grade preference data |
| Scale AI | Full-stack data services | Very large programs, Meta caveat applies |
| Turing | Expert data | Coding data and expert engineers |
| AfterQuery | Expert data and benchmarks | SFT and RLHF data with published benchmarks |
| Toloka | Managed crowds to expert data | Global reach, lab-neutral positioning |
| Prolific | Vetted participant pool | Studies, evals, human baseline data |
| Troveo | Licensed data marketplace | Rights-cleared video, audio, gameplay, and business data |
How to choose
Start with the domain. Academic reasoning in math, physics, and computer science favors a university pipeline like Handshake AI's. Practitioner domains, medicine as practiced, law as billed, enterprise software as actually used, favor marketplaces that recruit working professionals. Language breadth favors the global networks. Then weigh the usual: vetting quality, quality control on top of the humans, who owns the resulting data, and concentration risk with any vendor growing this fast.
And if the gap is world knowledge rather than expert knowledge, stop comparing networks: that budget belongs in licensed data.
Where Troveo fits
Troveo does not run an expert network. Troveo licenses the data experts cannot write: real-world video, audio, gameplay, and business data from more than 7,000 rights holders, cleared for AI training with documentation per asset and delivered training-ready. The two purchases are complements, and serious model programs make both: expert networks for judgment, licensed data for reality. Browse the catalog in Lens or talk to us about what your model is missing.