AfterQuery
Contact SalesExpert-curated training data for frontier AI models.
By Tanmay Verma, Founder · Last verified 03 Jul 2026
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Added 6d ago
75/100Safe BetIn short
AfterQuery — Expert-curated training data for frontier AI models. Best for Frontier AI research labs, Enterprise teams building specialized agents, Domain-specific model trainers (e.g., finance, coding). Contact Sales pricing.
Is AfterQuery actually worth it?
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Editorial Verdict
Best for
Frontier AI research labsEnterprise teams building specialized agentsDomain-specific model trainers (e.g., finance, coding)Companies needing reasoning-focused training data
Not ideal for
Casual hobbyists or solo developersTeams looking for pre-built chat modelsAnyone without AI/ML expertiseUsers needing free or low-cost data
AfterQuery is a premium data provider for teams pushing model reasoning beyond generic limits. Its expert-curated approach delivers measurable gains on benchmarks, but the custom pricing and enterprise focus means it's not for casual users. If you need to improve agent performance on specialized tasks, it's worth the investment.
Compare with: AfterQuery vs Goodfire, AfterQuery vs LangSmith, AfterQuery vs Humata AI
Last verified: July 2026
What's new in AfterQuery
Checked 6 days agoAcross the latest 6 updates: 2 feature updates, 3 launches and 1 news mention.
LaunchBlog·Jun 8Newest
How we achieved a net win-loss margin of +21.4% on GDPval with on-policy distillation
AfterQuery achieved a +21.4% net win-loss margin on GDPval via on-policy distillation, improving model performance against baselines.
NewsBlog·Jun 3
Why DeployCo and ServiceCo Are Betting on the Last Mile
AfterQuery discusses partnerships with DeployCo and ServiceCo focusing on last-mile enterprise AI deployment.
FeatureBlog·Apr 28
Solving the Last Mile Problem in Partnership with The Raine Group
AfterQuery partners with The Raine Group to address last-mile challenges in enterprise AI adoption.
FeatureBlog·Apr 9
Human expertise, reimagined
AfterQuery discusses its approach to integrating human expertise with AI data curation methodologies.
LaunchBlog·Apr 8
How AfterQuery Expert Data Drives Model Performance on τ²-bench
AfterQuery expert-curated data improves model performance on τ²-bench benchmark.
LaunchBlog·Mar 31
How We Improved Terminal-Bench 2.0 Scores by Over 5x Using Tinker and Harbor
AfterQuery boosted Terminal-Bench 2.0 scores by 5x using Tinker and Harbor tools.
What independent users actually report about AfterQuery
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
1 mentions across 1 source (Hacker News).
50% positive50% critical
Recurring strengths
- +Focus on expert reasoning, not just static outputs.
- +Publishes proprietary benchmarks like SpreadsheetBench and IDE-Bench.
- +Attracted $30M Series A and $100M ARR signaling viability.
- +Partners with domain experts for specialized training data.
- +Provides tooling like Tinker and Harbor for agent improvement.
Recurring frustrations
- −Zero community or user reviews across any platform.
- −Pricing is opaque—only available on request.
- −No free tier or trial to test before purchasing.
- −Entirely dependent on marketing claims without validation.
- −Limited to advanced teams; not accessible to solo developers.
Patterns worth knowing
Lack of community presence and user feedback
Seen on Hacker News
Confusion with other products named 'afterquery'
Seen on Hacker News
Learning curve
advancedProductive in ~Several days to weeks of setup
Hidden costs people mention
- • No public pricing—costs may be substantial and vary widely
- • Potential setup fees or minimum contract commitments
Viability Score
75/100
Safe BetHow likely is AfterQuery to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
momentum
55
funding runway
70
website health
90
wrapper dependency
100
Last calculated: July 2026
How we score →Key Features
- Expert-curated supervised fine-tuning data
- Reinforcement learning with expert-designed rubrics
- Agent environments via API/MCP
- Computer-use trajectories (browser/desktop)
- Proprietary benchmarks (SpreadsheetBench, IDE-Bench, etc.)
- On-policy distillation
- Tooling (Tinker, Harbor)
- Domain-specific data (finance, coding, UI)
- Partnership with The Raine Group
- Research publications on data quality
- Model evaluation frameworks
About AfterQuery
Contact SalesAdvancedNo API
AfterQuery is an applied research lab that captures how domain experts think—reasoning, decisions, and tradeoffs—and structures it into training data for foundation models. Unlike conventional data pipelines that scrape generic web content, AfterQuery produces high-quality SFT pairs, RL rubrics, agent environments, and computer-use trajectories. The company targets AI researchers and enterprises building specialized agents and coding assistants. Its datasets are used to improve model performance on benchmarks like Terminal-Bench 2.0 and IDE-Bench, and it partners with firms like The Raine Group for domain-specific encoding. What makes AfterQuery different is its focus on reasoning rather than outputs. The company argues that models plateau when trained on static answers, but improve when trained on expert thought processes. This approach has attracted $30M in Series A funding and surpassed $100M in annual revenue. AfterQuery publishes its own benchmarks and provides tooling such as Tinker and Harbor to boost agent scores. Its offerings are primarily custom enterprise datasets, with pricing available on request.
Behind the Verdict
AfterQuery fills a specific gap: high-quality training data that captures expert reasoning, not just outputs. In practice, this matters most for frontier AI labs and enterprise teams building agents for complex domains like finance or coding. We'd reach for this when generic web data causes model plateaus and you need to push performance on benchmarks like Terminal-Bench 2.0 or IDE-Bench. The $30M Series A and $100M revenue run rate signal strong market validation. But it's not for everyone—pricing is custom and likely high, and you need internal ML expertise to integrate and evaluate the datasets. Compared to alternatives like Scale AI or Surge AI, AfterQuery's emphasis on reasoning trajectories and on-policy distillation sets it apart. However, the lack of public pricing tiers and self-service options is a barrier for smaller teams. Where it bites: if you're a solo developer or a small startup without a dedicated ML team, you'll find the process opaque and expensive. The tooling like Tinker and Harbor is powerful but adds learning overhead. Bottom line: for serious model training where reasoning quality matters, AfterQuery is a strong bet; for everything else, look elsewhere.
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Use Cases
- Train a coding agent on expert-curated SFT pairs and reinforcement learning rubrics for IDE use.
- Create a domain-specific financial assistant using AfterQuery's FinanceQA and SpreadsheetBench data.
- Improve an agent's ability to navigate browser and desktop interfaces with computer-use trajectories.
- Leverage on-policy distillation to boost model performance on proprietary benchmarks.
- Collaborate on custom dataset creation for niche professional fields like law or medicine.
- Evaluate model reasoning chains using expert-designed scoring frameworks.
Limitations
- Pricing is not publicly listed, requiring contact for custom quotes.
- No API or self-service platform; datasets are delivered through direct collaboration.
- No integrations with third-party tools are documented.
- Current evidence suggests a focus on enterprise clients rather than individual developers.
Resources & Guides
Frequently Asked Questions
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