AfterQuery Launches: High-quality AI Starts with High-Quality Human Data

AfterQuery recently launched!

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^{"Powering AI models with expert-level human data"}

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^{TL;DR AI models need high-quality training data to effectively automate complex professional tasks - AfterQuery provides that data. If you are building an AI model or need fine-tuning data for your agent, they would love to help. Reach out to the founders here.}

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‍Founded by Carlos Georgescu, Spencer Mateega & Danny Tang

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The Team

Carlos and Spencer first met in high school at a summer program at Google and then interned together at Meta. Danny and Spencer met in high school, where they built a startup. They then both got into Wharton, sold that startup, and became roommates.

Carlos has built multiple software businesses on his own, exited an edtech startup himself, and interned twice at Citadel Securities as a software engineer. He’s won the largest possible educational scholarship in all of Canada (the equivalent of two full rides), competed in state and national sailing competitions, and has 8x his bankroll in poker. He’s turned down numerous quant offers to pursue startups.

Spencer wrote an award-winning research paper and interned at a private equity firm in high school, before deciding to intern at Meta (SWE), Google (SWE), Morgan Stanley (IB), and Silver Lake (PE) in the next 3 years. He was the sole summer analyst at Silver Lake globally and has programmed numerous apps in his spare time (and sold some of them). He trains for half marathons and lifts in his free time between completing a master’s in computer science concurrently with his Wharton undergraduate degree.

Danny competed and won international competitions in public speaking during high school while pursuing his interest in AI, winning an international Microsoft AI competition and conducting research at a machine learning neuroscience lab. He then learned how people build real estate, from grocery stores to billion-dollar luxury hotels, eventually working on the largest real estate IPO in history and the largest IPO in 2024. He also really likes movies and attended the world's largest film festival, catching premieres of films like Anora and The Substance.

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Why They’re Building AfterQuery

There is a dearth of high-quality training data.

Current AI models are trained to be smart but are not trained on the work of actual professionals in many fields.

For example, current models are largely unusable to professionals working in finance (private equity, hedge funds, investment banks, etc). See the founders paper, where LLMs fail 60% of realistic daily tasks – but with some fine-tuning on high-quality training data, significantly improve.

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Chart showing LLM performance against FinanceQA

And the lack of high-quality training data is everywhere:

• Domain-specific AI (legal, consulting, healthcare, etc.)
• Multimodal data (audio, multilingual, etc.)
• Foundational models looking to push the boundary on reasoning and grading

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Solution

The AfterQuery team is building a platform to onboard experts to work on custom datasets. Think working professionals at top firms and the smartest students in various subjects (PhDs). Then, whenever you have a request for a certain type of data, they will construct and lead a team to produce:

• Post-training / fine-tuning data
• Human-Led Reinforcement Learning (RLHF) data
• Custom benchmarks

You will know the people making your dataset and their qualifications while the team coordinates with you to ensure quality standards. They save you time and energy, so you can focus on the model and not the data.

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Example Datasets AfterQuery Provides

Foundational Model Advancing Coding Capabilities:

• New data structures and algorithms problems with test cases and step-by-step answer reasoning
• Computer architecture and system design reasoning
• Performance optimization code with benchmarks and reasoning

Enterprise SaaS Company Building Internal AI Developer Tool:

• Codebase-wide code snippets with corresponding refactoring suggestions and explanations
• System and runtime errors paired with step-by-step debugging solutions
• Database query optimization solutions and reasoning steps

Startup Building an AI Agent for Law:

• Answered legal questions with expert reasoning
• Computer use data for common tasks on legal project management software
• Contract redlines with expert corrections

If AI is one day to replace jobs entirely, it can’t just be good, it needs to be near perfect. This transformation will require an immense amount of high-quality training data.

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‍Learn More

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^{🌐 Visit www.afterquery.com to learn more.}

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^{🧠 Are you working on a foundational model advancing multimodal applications, nuanced reasoning, or domain-specific expertise?}

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^{🤖 Are you building an AI agent (Series A to enterprise) where prompt engineering and RAG aren't cutting it?}

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^{🔍 Are you implementing AI in a specialized domain where current models lack expertise (e.g., medicine, legal, finance, software, government, healthcare)?}

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^{🤝 The founders would love to meet you! Please schedule a time on Calendly or reach out via email.}

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^{‍‍👣 Follow AfterQuery on LinkedIn & X.}

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