AfterQuery is the benchmark-forward member of the expert data generation: founded in 2024, it supplies AI labs with expert-produced training data, evaluations, and development environments, and distinguishes itself by publishing benchmarks alongside the data it sells. By press accounts the formula worked fast: reported annualized revenue passed 100 million dollars within roughly two years, and the company raised a 30 million dollar Series A led by Altos Ventures at a 300 million dollar valuation in April 2026, with reported customers across the leading labs. Fast-growing vendors end up on shortlists, and shortlists produce comparison shopping. This guide covers who competes with AfterQuery in each lane, and when the better answer is not an expert vendor at all.
What AfterQuery is known for
AfterQuery's expert network is reported at nearly 100,000 contributors, developers, attorneys, and other professionals, producing prompt-response data with step-by-step reasoning, supervised fine-tuning sets, and RLHF data. Around it sits a broader toolkit by its own descriptions: evaluation suites, development environments, and AI training sandboxes for business automation tasks.
The distinguishing habit is publishing benchmarks: putting evaluation results into the open rather than keeping quality claims private. In a market where every vendor says their data is frontier-grade, showing your work is a real differentiator, and it earned the company attention beyond its size.
Teams look for alternatives for the usual reasons: diversification on critical programs, needing depth in domains beyond AfterQuery's strongest, wanting a larger or longer-tenured partner, or discovering that the real gap is data that exists in the world rather than data experts produce to order.
Alternatives for expert data and evals
The direct competitors are the other expert networks, each with a different engine. Mercor is the largest of the generation, recruiting working professionals across industries and expanding into evals and RL environments. Micro1 vets applicants with its AI recruiter at high selectivity. Handshake AI draws PhDs from its university career network. Turing comes at the market from a software engineering base with reported profitability. Surge AI is the premium name for frontier-grade human feedback, Scale AI runs the largest full-stack programs with the Meta ownership caveat, and Toloka and Prolific cover managed crowds and vetted participants.
For the human feedback lane in full, see our guide to RLHF data providers.
When the answer is data, not experts
Expert vendors sell knowledge work produced to order, and benchmarks measure how well that work tunes a model's judgment. None of it supplies what a model has never seen: real motion for video models, natural conversation for voice agents, gameplay and first-person footage for world models, genuine workflow traces for enterprise agents. That data cannot be written by experts or measured into existence by benchmarks. It has to be captured from reality and licensed from whoever owns it, a different market covered in our guide to AI training data marketplaces.
The landscape
| Provider | Category | Best for |
|---|---|---|
| AfterQuery | Expert data and benchmarks | Benchmarked SFT and RLHF data, lean and fast |
| Mercor | Expert marketplace | Domain-expert demonstrations, evals, environments |
| Micro1 | Expert recruiting | AI-vetted specialists, lean challenger economics |
| Handshake AI | Expert network | PhD-level reasoning data from a university pipeline |
| Turing | Expert data and services | Coding data, expert engineers, lab post-training work |
| Surge AI | RLHF and evaluation | Frontier-grade preference data |
| Scale AI | Full-stack data services | Very large programs, Meta caveat applies |
| Toloka | Managed global crowds | Scaled human data with global reach |
| Prolific | Vetted participant pool | Representative human feedback and evals |
| Troveo | Licensed data marketplace | Rights-cleared video, audio, gameplay, and business data |
How to choose
The expert field now separates on three questions. Proof: AfterQuery publishes benchmarks, most rivals rely on reputation, so ask every vendor for evidence at your task, not in general. Center of gravity: coding (Turing), academic depth (Handshake AI), breadth of professions (Mercor), selectivity (Micro1), premium feedback (Surge AI). And scale versus tenure: the two-year-old companies are growing fastest, the older names have survived more market cycles, so match the risk to how critical your program is.
And if what your model lacks is contact with the world rather than expert judgment, stop comparing expert vendors: 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. Browse the catalog in Lens or talk to us about what your model is missing.