AfterQuery

What do they actually do

AfterQuery builds and sells curated training datasets and human‑expert feedback programs to help AI models reason better and perform real tasks. Offerings include supervised fine‑tuning pairs, expert ratings for reinforcement‑style training, “computer use” interaction traces, and custom evaluation environments, with public benchmarks and leaderboards to measure progress (homepage, research).

Live products include AfterQuery Experts, a vetted marketplace of domain specialists who create, review, and rate model outputs via an apply → interview → assessment → paid‑project flow; the site lists open roles, pay ranges, and advertises $1M+ paid out to experts (Experts, open positions). The team also ships domain benchmarks and leaderboards (e.g., LeetBench, FinanceQA, UI‑Bench, VADER) with associated papers for some datasets (research/leaderboards, FinanceQA, UI‑Bench). Typical users are foundational‑model builders, AI agent teams, and regulated enterprise ML groups seeking higher‑signal data and reproducible evaluations (YC company page).

Who are their target customer(s)

• Foundational model teams at AI labs and startups: Web‑scraped or synthetic data misses complex professional reasoning, leading to systematic errors on real tasks. They need expert‑curated examples and tight evaluations to reliably lift reasoning quality.
• Product teams building AI agents and automation: Agents trained on weak data fail in real software and multi‑step workflows, causing unreliable or unsafe behavior. They need realistic interaction traces and targeted human feedback to complete tasks robustly.
• Enterprise ML teams in regulated domains (finance, legal, healthcare): Public data and generic models can be inaccurate or non‑compliant, blocking deployment. They need vetted experts, auditable labels, and domain‑specific benchmarks to meet accuracy and regulatory requirements.
• Research groups and evaluation teams: It’s hard to compare models or show real progress without standardized, domain‑specific benchmarks. They need reproducible evaluation suites and curated datasets for fair comparisons.
• Labeling/annotation ops managers at mid/large companies: Recruiting, vetting, and managing domain experts is slow and costly to scale internally. They want a pre‑vetted network and managed workflows to deliver consistent high‑quality labels without building the program in‑house.

How would they acquire their first 10, 50, and 100 customers

• First 10: Run tightly scoped paid pilots for model labs, 2–3 agent startups, and 2–3 regulated enterprises via cold outreach and YC/intros, aiming for a specific metric lift with an improvement‑or‑refund offer; convert results into case studies (anonymized if needed).
• First 50: Publish benchmarks/leaderboards and open challenges tied to your datasets, then follow up with entrants and top performers with pilot and paid evaluation offers; do targeted BD at major conferences and sell standardized 30‑day lift packages to convert pilots into short contracts and testimonials.
• First 100: Productize into a self‑serve dataset catalog and a subscription for ongoing human‑feedback pipelines with compliance artifacts; add partnerships (consultancies, cloud, model hubs) and a small enterprise sales team, and invest in docs/integrations to shift from bespoke pilots to recurring contracts.

What is the rough total addressable market

Top-down context:

Published estimates put the 2024 AI training‑dataset market at roughly $2.6–$2.9B and broader data‑labeling/annotation services around $18.6B, implying a combined market in the low tens of billions today (Grand View – training datasets, Fortune BI, Grand View – data labeling). Multiple forecasts expect strong growth through 2030 (MarketsandMarkets).

Bottom-up calculation:

Approximate TAM by combining spending on curated training datasets (~$2.6–$2.9B) with outsourced data‑labeling/managed annotation (~$18.6B), yielding ≈$21B+ in 2024 for the services AfterQuery targets (Grand View – both markets, Grand View – labeling).

Assumptions:

• Labeling/annotation figure includes managed services relevant to expert‑driven data and feedback; narrower studies would reduce the total (Mordor Intelligence).
• Overlap between “training datasets” and “labeling services” is limited for a high‑level TAM estimate; double‑counting is likely small at this granularity.
• Figures reflect 2024 baselines; growth expectations are directional and not applied to the base TAM.

Who are some of their notable competitors

• Scale AI: Large provider of data annotation, evaluation, and RLHF‑style services for AI labs and enterprises; a go‑to vendor for high‑end training data (site).
• Surge AI: Expert data labeling and evaluation network focused on high‑quality, domain‑specialist tasks (including RLHF) for model teams (site).
• Sama: Outsourced data‑annotation and enrichment services with enterprise programs across vision and language (site).
• Labelbox: Data‑labeling and data‑ops platform used by enterprises to manage annotation workflows, quality, and compliance (site).
• Humanloop: Tooling for human feedback, evaluation, and iteration workflows to improve LLM applications (site).