AfterQuery
Applied research lab that captures expert reasoning and structures it into SFT, RL rubric, agent, and computer-use training data for frontier models.
If your team's bottleneck is agent benchmark performance rather than model access, AfterQuery is one of the few data vendors with third-party receipts — NVIDIA named it in a technical report and credited its Off-The-Shelf Office Agent Training Dataset with 11.4 GDPval points on Nemotron 3 Ultra. The +21.4% GDPval net win-loss margin from on-policy distillation and the 5x Terminal-Bench 2.0 lift with Tinker and Harbor are the numbers to interrogate in your own eval harness. Labs weighing it against generic data vendors or synthetic pipelines should compare per-benchmark deltas, not corpus size. The tradeoff is a scoped engagement rather than a self-serve platform, and you need research staff
Verified 14d ago · liveness 43/100 · cite: rightaichoice.com/tools/afterquery
- Frontier AI research labs needing reasoning-focused SFT and RL data to push agent benchmarks
- Enterprises building finance, legal, coding, or support agents that need expert-curated domain data
- Teams running on-policy distillation that need high-quality traces and grading rubrics
- Groups improving computer-use models with human-demonstrated browser and desktop trajectories
- Solo developers or hobbyists without ML research staff or an enterprise budget
- Anyone who needs generic web-scale corpora rather than expert reasoning traces
- Projects that already perform well with synthetic or scraped data at far lower cost
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Skip AfterQuery if you have no ML research staff or eval harness to validate benchmark deltas internally, if you need generic web-scale corpora rather than expert reasoning traces, or if you are a solo developer looking for a dataset you can pull yourself.
Custom dataset design is scoped per engagement, so the cost scales with how many domains and expert hours your use case requires rather than a flat rate.
AfterQuery runs custom, engagement-scoped engagements and does not publish a rate card, so it fits research labs and funded enterprises that already carry ML research headcount and can absorb a scoping cycle. Cheaper volume-oriented suppliers such as generic data marketplaces price per token or per label and suit teams optimizing cost over benchmark deltas. Scale AI and Surge AI compete on breadth of data supply; AfterQuery competes on published, per-benchmark receipts.
In short
AfterQuery — Applied research lab that captures expert reasoning and structures it into SFT, RL rubric, agent, and computer-use training data for frontier models. Best for Frontier AI research labs needing reasoning-focused SFT and RL data to push agent benchmarks, Enterprises building finance, legal, coding, or support agents that need expert-curated domain data, Teams running on-policy distillation that need high-quality traces and grading rubrics. Contact Sales pricing.
What's new in AfterQuery
Checked 7 days agoAcross the latest 4 updates: 4 news mentions.
AfterQuery and Legora build a frontier legal evaluation benchmark
Legora BAR covers end-to-end legal agentic reasoning tasks across 28 practice areas, with a public case co-created with AfterQuery.
AfterQuery was sole data partner for Motif 3 release
AfterQuery served as sole data partner on the Motif 3 release, per its blog post marking the launch.
AfterQuery improves Qwen3.5-9B on tau^3-Banking with under 250 tasks
Using PivotRL and fewer than 250 tasks, AfterQuery reports gains on the tau^3-Banking benchmark with Qwen3.5-9B.
AfterQuery helped NVIDIA hill-climb GDPval
Blog post details AfterQuery's work supporting NVIDIA's GDPval benchmark improvements.
What people actually say about AfterQuery — is it worth it?
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) · researched Jul 3, 2026.
Average across the 1 source that answered — each source counts once, not each post.
- +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.
- −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.
- • No public pricing—costs may be substantial and vary widely
- • Potential setup fees or minimum contract commitments
Viability Score
How well maintained and how widely used is AfterQuery? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- Expert-curated supervised fine-tuning pairs with chain-of-thought reasoning traces
- Reinforcement learning rubrics for reasoning and code generation
- Custom agent environments delivered via API and MCP
- Computer-use trajectories from human demonstrations in browser and desktop
- On-policy distillation with expert data for benchmark win-rate gains
- Off-The-Shelf Office Agent Training Dataset used by NVIDIA for Nemotron 3 Ultra
- Proprietary benchmarks including Terminal-Bench 2.0, GDPval, τ²-bench, SpreadsheetBench, IDE-Bench
- Tinker and Harbor tooling for agent training and evaluation
- Domain datasets across finance, coding, UI, legal, and enterprise workflows
- Public legal benchmark co-created with Legora spanning 28 practice areas and real case data
- Sole data partner for the Motif 3 model release
- Research publications on model failure modes and data quality
- Custom dataset design scoped to enterprise use cases
- Data quality and curation services in partnership with model labs
- Expert capture methodology for encoding domain-specific judgment
About AfterQuery
AfterQuery is an applied research lab that captures how domain experts actually work — their reasoning, decisions, and tradeoffs — and structures that thinking into training data for frontier foundation models. Its core claim is blunt: models trained on outputs plateau, models trained on reasoning improve. The data stack spans four product lines. Supervised fine-tuning pairs come with chain-of-thought reasoning traces. Reinforcement learning rubrics give graders a framework for reasoning and code generation, turning subjective expert judgment into reward signals. Agent environments (API and MCP) let teams train and evaluate agents against real APIs, tools, and services. Computer-use trajectories are recorded from human demonstrations across browser and desktop, so models learn to operate software end-to-end rather than describe it. Evidence matters more than positioning here, and AfterQuery publishes both. NVIDIA used its Off-The-Shelf Office Agent Training Dataset to improve Nemotron 3 Ultra on GDPval — the only data vendor named in the technical report, worth 11.4 GDPval points in a warmup ablation. In June 2026 AfterQuery reported a +21.4% net win-loss margin on GDPval from on-policy distillation with expert data, and earlier tooling work with Tinker and Harbor lifted Terminal-Bench 2.0 scores more than 5x. Recent engagements run wide: a public legal benchmark co-created with Legora across 28 practice areas and real case data, sole data partner for the Motif 3 release, and work with The Raine Group, DeployCo, and ServiceCo on the last-mile enterprise deployment problem. It is built for research labs and enterprises that already have ML staff and evaluation harnesses in place, not for teams that want a self-serve dataset download.
Behind the Verdict
AfterQuery competes on benchmark deltas rather than data volume, and the vendor is unusually explicit about that positioning. Its four product lines map onto distinct training needs: supervised fine-tuning pairs with chain-of-thought traces for behavior shaping; RL rubrics that convert expert judgment into reward signals for reasoning and code generation; agent environments delivered through API and MCP so agents can be trained and evaluated against real tools and services; and computer-use trajectories captured from human demonstrations in browser and desktop environments so models learn to operate software end-to-end. The published evidence is the differentiator. NVIDIA used the Off-The-Shelf Office Agent Training Dataset to improve Nemotron 3 Ultra on GDPval and named AfterQuery as the only data vendor in the technical report, with the dataset worth 11.4 GDPval points in a warmup ablation. AfterQuery's own June 2026 work reports a +21.4% net win-loss margin on GDPval via on-policy distillation with expert data, and its Tinker and Harbor tooling work lifted Terminal-Bench 2.0 scores more than 5x. Domain coverage shows up in named benchmarks and partnerships: a public legal benchmark built with Legora spanning 28 practice areas and real case data, sole data partner status on the Motif 3 release, and last-mile deployment work with The Raine Group, DeployCo, and ServiceCo. Proprietary benchmarks cited in its material include Terminal-Bench 2.0, GDPval, τ²-bench, SpreadsheetBench, and IDE-Bench. Where it does not fit: teams without ML research staff, projects already performing well on synthetic or scraped data, and buyers who need benchmark gains proven on their own evals before signing — expect a scoping cycle first. Solo developers and hobbyists have no path in. And if you need generic web-scale corpora rather than expert reasoning traces, this is the wrong vendor.
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Real-world workflow fit
Concrete scenarios for the personas AfterQuery actually fits — and what changes day-one when you adopt it.
Your agent team is stuck on Terminal-Bench 2.0 and needs trajectories plus grading rubrics to run an on-policy distillation pass. You scope a dataset with AfterQuery covering the tool and API surfaces the agent actually uses, then train and re-evaluate against your harness.
Outcome: You get expert-curated trajectories and RL rubrics designed for the target benchmark rather than a generic corpus, with the same tooling pattern that lifted Terminal-Bench 2.0 scores more than 5x for AfterQuery's own work.
You need an agent that operates your internal software end-to-end, not one that only describes what to click. You engage AfterQuery to capture browser and desktop demonstrations from staff, then use those trajectories to train the model.
Outcome: The model learns real interface navigation from human demonstrations, closing the last-mile gap between a model that answers and one that executes in your environment.
You want an evaluation you can show to stakeholders, not a vendor scorecard. You adopt the public legal benchmark co-created with Legora — 28 practice areas, real case data — and use AfterQuery's expert-designed grading frameworks to score reasoning chains.
Outcome: You can point to a public, third-party-visible legal benchmark rather than an internal-only metric.
Use Cases
- Train a coding agent on expert-curated SFT pairs and RL 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.
- Stand up legal-domain evaluation using the public benchmark built with Legora across 28 practice areas.
- Hill-climb GDPval scores using the Off-The-Shelf Office Agent Training Dataset.
Limitations
- AfterQuery delivers data through direct collaboration rather than a self-service platform, so there is no path for individual developers to pull datasets on their own.
- The datasets are expert-curated and may not cover every domain — coverage tracks the practice areas the company has built out, such as finance, coding, UI, legal, and enterprise workflows.
- Benchmark improvements are reported by the vendor or by partner technical reports (NVIDIA on GDPval, the Tinker and Harbor Terminal-Bench 2.0 work); how much of that transfers to your model, your task, and your eval harness is something you have to validate internally before committing.
- That validation step means a scoping cycle before you see proven gains on your own evals.
as of 2026-09-24
Verification history
We have re-verified AfterQuery 8 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 8 verification passes.
Free to cite with attribution — this page re-verifies continuously.
Where the pricing makes sense
The company stage and team size where AfterQuery's pricing actually pencils out — and where peers do it cheaper.
AfterQuery runs custom, engagement-scoped engagements and does not publish a rate card, so it fits research labs and funded enterprises that already carry ML research headcount and can absorb a scoping cycle. Cheaper volume-oriented suppliers such as generic data marketplaces price per token or per label and suit teams optimizing cost over benchmark deltas. Scale AI and Surge AI compete on breadth of data supply; AfterQuery competes on published, per-benchmark receipts.
Setup time & first value
How long it actually takes to get something useful out of AfterQuery — broken out by persona, not the marketing-page minute.
For a research lab, first value typically comes after a scoping conversation and dataset design cycle rather than on day one, since data is delivered through direct collaboration. Enterprises should plan for expert-capture time on top of that — browser and desktop demonstrations come from your own staff. The legal and office-agent datasets (Legora benchmark, Off-The-Shelf Office Agent Training
Switching to or from AfterQuery
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From a generic data marketplace: replace per-token labeling volume with expert-curated reasoning traces and chain-of-thought SFT pairs scoped to your target benchmark.
- →From a synthetic data pipeline: layer expert SFT pairs, RL rubrics, and human-demonstrated computer-use trajectories where synthetic outputs plateau.
- →From in-house expert annotation: use AfterQuery's expert capture methodology and grading frameworks instead of standing up your own rubric design.
- ↗To a volume-oriented data supplier such as Scale AI or Surge AI: use when cost per label outweighs benchmark-delta performance.
- ↗To open web or synthetic corpora: use when your project already performs well on scraped or generated data and expert reasoning traces are not the bottleneck.
- ↗To building expert data in-house: use when you have the annotation staff and rubric design capability and want the capture loop fully inside your walls.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “AfterQuery”, and we withheld 6: 6 could not be judged, because “AfterQuery” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about AfterQuery.
Official links
Tools that pair well with AfterQuery
Common stack mates teams adopt alongside AfterQuery, with the specific reason each pairing earns its keep.
Snorkel AI
Snorkel AI builds expert training data, evals, and runnable environments for frontier models and agents.
Deepfabric
Open-source Python framework for generating grounded synthetic datasets from real sandboxed tool execution traces.
Markov
Human-recorded computer-use datasets that teach AI agents to operate real software the way people do.
Featured Head-to-Head Comparisons
Afterquery vs Spider Cloud
Choose AfterQuery if your goal is improving model reasoning and domain-specific performance using expert-curated data and proprietary benchmarks. Choose Spider Cloud if you need a fast, cost-effective web scraping and crawling API to feed real-time data to your AI agents or RAG pipelines. They solve fundamentally different problems: one trains models, the other feeds them data.
Afterquery vs Temporal Ai
AfterQuery and Temporal AI serve fundamentally different needs. AfterQuery provides expert-curated training data and distillation for improving AI model reasoning, ideal for research labs and enterprises building specialized agents. Temporal AI is a durable execution platform that ensures reliability and state recovery for AI agents and workflows. If you're training a frontier model, choose AfterQuery; if you're deploying agents in production with fault tolerance, choose Temporal AI.
Afterquery vs Praktika
If you're building specialized AI agents and need domain-expert training data, AfterQuery is unmatched with its proprietary benchmarks and recent 5x boost on Terminal-Bench 2.0. For language learners wanting affordable, on-demand speaking practice, Praktika's AI tutors provide immediate feedback and adaptive plans. These tools serve entirely different buyers—choose based on whether you're training models or training yourself.
Alternatives to AfterQuery
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Snorkel AI
Snorkel AI builds expert training data, evals, and runnable environments for frontier models and agents.
Deepfabric
Open-source Python framework for generating grounded synthetic datasets from real sandboxed tool execution traces.
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