AfterQuery
(https://afterquery.com) 📸 Data Snapshot: June 21, 2026
Information Density — The Lens
Classify each sentence as substantive or hollow. Grounding markers — numbers, currencies, dates, technical units, named entities — outweigh marketing adjectives. When fluff sits right next to hard evidence, the fluff is forgiven.
Info Density
Power-words vs. Substance ratio.
25
Impact Weight: 30 / 100
83% Reputation
Information density is exceptionally high, with a low power-word-to-noun ratio. Heading fluff is minimal, with H2s and H3s citing specific benchmarks like Terminal-Bench 2.0 and IDE-Bench rather than generic value propositions. The body text includes highly technical specifics such as Chain-of-Thought reasoning traces and on-policy distillation, which represent a significant departure from standard industry fluff.
Information Density is read straight from the body copy: how much of the text carries grounded, checkable substance versus hollow filler. Below is the clean text the engine analyzed, then the industry’s known generic-claim patterns to weigh it against.
📝 The Narrative — clean text per page (the substance-vs-filler signal)
HOMEPAGE (https://afterquery.com) AfterQuery
[H1] We teach machines how experts think. |The future of AI won’t be trained on more data, it will be trained on better thinking.Get dataExplore researchBacked by angels fromPowering every frontier AI research labProblem [H2] AI researchers and enterprises are hitting walls with suboptimal data solutions. Today’s models can generate answers. But they struggle with real work. Because real work isn’t just outputs. It’s decisions, tradeoffs, and context. That knowledge doesn’t live on the internet — it lives inside experts.Expertise has never been captured. Until now.The most valuable knowledge isn’t written down. It exists in how professionals think — not just answers, but reasoning, decisions, tradeoffs, and context. We work with domain experts to capture that thinking, then structure it into training data models can learn from.Our solution [H2] We turn real-world work into training data. AfterQuery is an applied research lab curating data solutions for frontier foundation model development. Models trained on outputs plateau. Models trained on reasoning improve. We build datasets that reflect how experts actually solve problems — step by step, decision by decision.Our data includes:Supervised Fine-Tuning (SFT)High-quality prompt–response pairs and chain-of-thought reasoning traces — teaching models how to behave across complex tasks.Reinforcement Learning + RubricsExpert-designed prompts with grading frameworks for reasoning and code generation — turning subjective judgment into scalable reward signals.Agent Environments (API / MCP)Custom environments across APIs, tools, and services — enabling training and evaluation of agents in real workflows.Computer Use TrajectoriesHuman-demonstrated interactions across browser and desktop environments — teaching models to navigate and operate software end-to-end. [H2] Research Our approach starts with research: where exactly do models break down in real professional contexts? Why do these failure modes exist? We take a proactive stance — every domain has its own failure patterns.More research [H3] How We Improved Terminal-Bench 2.0 Scores by Over 5x Using Tinker and Harbor How expert-curated trajectories and tooling lifted Terminal-Bench 2.0 scores more than 5x — and what it says about training agents.Blog·Mar 31, 2026 [H3] Human expertise, reimagined Capturing how experts think — turning real-world decisions, judgment, and workflows into training data models can learn from.Blog·Apr 9, 2026 [H3] Solving the Last Mile Problem in Partnership with The Raine Group Encoding domain-specific excellence into forms machines can learn — so agents think and execute like real-world experts.Blog·Apr 28, 2026 [H5] Careers We’re hiring for engineering, operations, and research roles to help us accelerate AI training data solutions. Join the team revolutionizing AI Research and Training.See open roles↗
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SUB-PAGE (https://afterquery.com/research/) AfterQuery
Research [H1] Data quality makes all the difference. We’re driven by the conviction that model performance is fundamentally bounded by training data quality. Through expert collaboration, rigorous curation methodologies, and deep domain expertise, we research datasets that power tomorrow’s models. [H2] SpreadsheetBench 2 Evaluating LLM agents on challenging, expert-curated, end-to-end spreadsheet tasks — financial modeling, debugging, and visualization in complex multi-sheet workbooks.View benchmark↗ [H2] IDE-Bench A comprehensive framework for evaluating AI IDE agents on real-world software engineering tasks through an IDE-native tool interface.Read paper↗ [H2] How we achieved a net win-loss margin of +21.4% on GDPval with on-policy distillation Michael E.Spencer M.·Jun 8, 2026Read blog↗ [H2] Why DeployCo and ServiceCo Are Betting on the Last Mile Sam J.Agustin G.Drew·Jun 3, 2026Read blog↗ [H2] Solving the Last Mile Problem in Partnership with The Raine Group Carlos G.Sam J.·Apr 28, 2026Read blog↗ [H2] Human expertise, reimagined Spencer M.·Apr 9, 2026Read blog↗ [H2] How AfterQuery Expert Data Drives Model Performance on τ²-bench Michael E.Spencer M.Arya F.·Apr 8, 2026Read blog↗ [H2] How We Improved Terminal-Bench 2.0 Scores by Over 5x Using Tinker and Harbor Spencer M.Michael E.Carlos G.·Mar 31, 2026Read blog↗ [H2] IDE-Bench: Evaluating Large Language Models as IDE Agents Spencer M.Jeff Y.Tiana C.·Jan 20, 2026Read paper↗ [H2] Market-Bench: Evaluating LLMs on Introductory Quantitative Trading Abhay S.Sam J.Spencer M.·Dec 13, 2025Read paper↗ [H2] App-Bench: Evaluating Coding Agents on Generating Economically Useful Web-Apps Andrew Z.Sam J.Spencer M.·Oct 25, 2025Read paper↗ [H2] The AfterQuery Thesis Spencer M.·Oct 20, 2025Read blog↗ [H2] UI-Bench: A Benchmark for Evaluating User Interface Understanding Sam J.Agustin G.Spencer M.·Aug 28, 2025Read paper↗ [H2] FinanceQA: A Benchmark for Assumption-Based Financial Analysis Spencer M.Sam J.·Jan 30, 2025Read paper↗ [H2] Core Research Areas 1 [H3] Computer Use We’ve created training data and reinforcement learning environments that teach AI agents to navigate real software workflows end-to-end, capturing judgment calls and edge cases that only experienced practitioners recognize.Computer UseMultimodalAI Safety & SecurityData Quality & CurationModel Evaluation
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SUB-PAGE (https://afterquery.com/careers/) AfterQuery
Careers [H1] Shape how AI learns. Behind our exceptional training data is an equally exceptional team.With a founding team that left jobs at Goldman Sachs, McKinsey, Jane Street, Palantir, NVIDIA, Google, and top startups, we’re seeking exceptional talent to own one of the defining problems of our generation.All Jobs25 jobs [H3] Engineering 6Software Engineer - Platform/Applied AI (Fullstack)San FranciscoFull-timeApply now↗Software Engineering InternSan FranciscoInternApply now↗Senior Software Engineer - Infrastructure & PlatformSan FranciscoFull-timeApply now↗Software Engineer - Security/InfrastructureSan FranciscoFull-timeApply now↗Software Engineer - RL Environments San FranciscoFull-timeApply now↗Engineering Manager San FranciscoFull-timeApply now↗ [H3] Growth 2Growth AssociateSan FranciscoFull-timeApply now↗Growth InternSan FranciscoInternApply now↗ [H3] Internal Ops 5Business Operations GeneralistSan FranciscoFull-timeApply now↗People Programs LeadSan FranciscoFull-timeApply now↗Business Talent Acquisition LeadSan FranciscoFull-timeApply now↗Technical Recruiter San FranciscoFull-timeApply now↗Talent Coordinator San FranciscoFull-timeApply now↗ [H3] Operations 6Strategic Projects LeadSan FranciscoFull-timeApply now↗Strategic Projects - Coding InternSan FranciscoInternApply now↗Strategic Projects AssociateSan FranciscoFull-timeApply now↗Strategic Projects Lead - CodingSan FranciscoFull-timeApply now↗Strategic Projects Associate - CodingSan FranciscoFull-timeApply now↗Strategic Projects InternSan FranciscoInternApply now↗ [H3] Research 2Research Scientist - Frontier DataSan FranciscoFull-timeApply now↗Research Scientist - Post TrainingSan FranciscoFull-timeApply now↗ [H3] Revenue 4Marketing LeadSan FranciscoFull-timeApply now↗Business Development San FranciscoFull-timeApply now↗Strategic FinanceSan FranciscoFull-timeApply now↗Customer EngagementSan FranciscoFull-timeApply now↗ [H4] Benefits Health insuranceMedical, vision & dental401(k)With employer matchDaily mealsDaily UberEats stipendCommute coveredUber stipend for safe transportationWellness stipendMonthly — covers Equinox membership
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SUB-PAGE (https://afterquery.com/leaderboard/) AfterQuery
Leaderboards [H1] Rigorous benchmarks, not cherry-picked results. Design custom evaluations that measure your specified model capabilities.Collaborate with usIDE-Bench [H2] Evaluating AI Agents on Software Engineering Assessing AI agents across real-world software engineering workflows—measuring how models navigate, reason, and execute complex development tasks.IDE-Bench·Jan 20, 2026Market-Bench [H2] Introductory Quantitative Trading Evaluating AI models on real-world market scenarios—measuring how they reason, predict, and make decisions under dynamic conditions.Market-Bench·Dec 13, 2025App-Bench [H2] AI Web App Generation A benchmark for evaluating how well AI coding agents can generate real web apps from a single natural language prompt. One-shot generations. Zero human edits.App-Bench·Oct 25, 2025FinanceArena [H2] FinanceQA, Assumption-Based Analyzing AI models on real-world financial analysis—measuring how they reason, interpret data, and make decisions under uncertainty.FinanceArena·Jan 30, 2025
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🧭 Industry Context — common generic-claim patterns in Science, Research & Laboratories to weigh the text against
Generic Claims:
world-class research, pioneering scientific breakthroughs, advancing knowledge, trusted by leading institutions, cutting-edge laboratory, precision and accuracy…
Red Flags:
accreditation claims without certificate numbers, no publication record for research claims, unnamed scientists or researchers, breakthrough claims without peer review, laboratory photos that are stock images, quality claims without accrediting body…
Semantic Drift Patterns:
homepage claims cutting-edge but equipment list is dated, claims accredited but no accreditation schedule or scope shown, research claims but no publication list, claims GLP but no regulatory inspection history…
Proof Expectations:
accreditation certificate numbers and scope (ISO 17025, GLP), publication list with peer-reviewed journal citations, named principal investigators with verifiable track records, specific equipment list with calibration status, quality management documentation, regulatory inspection history and compliance…
Explore the other reputation pillars for AfterQuery
Analysis Disclosure & Source Attribution
This page presents a snapshot of public data from AfterQuery, captured on June 21, 2026, to show how machine logic reads Information Density signals into an AI reputation evaluation.
Purpose: This data is presented under “Fair Use” for the purpose of independent signal analysis, allowing readers to see the raw signals behind the reputation score.
Notice to AfterQuery: This analysis is part of a non-adversarial audit conducted by 1 Euro SEO. The results are intended as professional feedback to help improve any website’s machine-readability and authority signals. The evaluation is free, and any company can request a fresh audit at any time.
Any company can use the insights for free and improve its voice. When a company has updated its content, it can always submit a new audit request, which will be reflected in a new current score.
To all users: You are encouraged to visit the live site at https://afterquery.com to view the most current version of its content and see directly what this company is about and what it offers.