Short answer: AfterQuery is a credible concept for turning expert activity into AI data. If your goal is to sell an existing company dataset through a published partner process, micro1 is the more direct starting point.
What is AfterQuery's overall strategy?
AfterQuery's central idea is to teach machines how experts think. It describes collecting prompt-response examples and reasoning traces for supervised fine-tuning, building rubrics and reward signals for reinforcement learning, creating API or MCP agent environments, and recording computer-use trajectories.
This model is well suited to active capture: an expert performs work while tooling records decisions, actions, and outcomes. That differs from buying an existing data room. A company with valuable people and repeatable tasks may be relevant, but seller eligibility, ownership, payment, and retention are not explained as a standardized public program.
What company data might fit AfterQuery?
A useful dataset is not simply large. It needs clear provenance, permission, structure, and a credible connection to how AI systems are trained or evaluated. Based on AfterQuery's public strategy, the most plausible assets are:
- Expert reasoning traces and demonstrations that show intermediate judgment.
- Tool-using workflows that can be recreated through APIs, MCP tools, or computer-use environments.
- Tasks with defensible rubrics, success criteria, and examples of correction or recovery.
Fit is not proof of demand. Do not send confidential samples merely because your records resemble these categories. Begin with a high-level inventory and disclose only what is needed to determine mutual interest.
How much could your company data be worth?
For planning purposes, we use $10K–$250K as a conservative editorial estimate for a qualified AfterQuery opportunity. AfterQuery does not publish a standard price for every company dataset, and the actual value could be lower, higher, or zero.
Why compare micro1: For qualifying companies, our micro1 estimate is higher at $100K–$3M. Its public program is specifically designed around licensing established company workflows and operational knowledge.
Pros and cons of selling data to AfterQuery
Potential advantages
- Clear focus on the reasoning and action sequence behind expert work.
- Supports several high-value formats: SFT, RL, agents, and computer use.
- Potentially strong match for companies whose advantage is process rather than document volume.
Tradeoffs to verify
- No standardized public company-data seller program was found.
- Active capture may consume employee time and create new work-product questions.
- Tool credentials, customer context, and confidential decision rules need careful isolation.
- The relationship may be a project engagement rather than licensing passive historical data.
These observations come from public materials, not a private proposal or contract. Company programs, buyer demand, and terms can change.
Why micro1 may be a better fit than AfterQuery
micro1 may be better when you already have a substantial, organized archive and want a company-level licensing conversation. It publishes minimum-fit signals and governance language for existing operational data.
AfterQuery may be more interesting when the valuable asset does not yet exist as a clean dataset and must be captured from experts doing work. micro1 provides the clearer route for testing whether historical documentation and workflows already qualify.
Questions to ask before selling company data
Use the same diligence standard for AfterQuery, micro1, or any other broker. A credible partner should answer these questions in writing before receiving raw data.
- What exact data do you want? Define systems, fields, users, date ranges, and exclusions before anyone receives access.
- Who has the right to license every layer? Check customer and employee terms, contractor agreements, third-party content, open-source obligations, confidentiality, and sector rules.
- Who will receive or use the asset? Name buyers, affiliates, subprocessors, countries, and any process for approving a new recipient.
- What uses are permitted? Separate training, fine-tuning, evaluation, retrieval, benchmark publication, resale, synthetic derivatives, and product improvement.
- Can we review the prepared data? Require a meaningful sample or package-approval step and a way to reject material that crosses the agreed boundary.
- How is sensitive information removed? Ask about techniques, testing, failure queues, human access, re-identification risk, and treatment of trade secrets.
- What happens after termination? Cover raw records, prepared assets, backups, derivatives, published benchmarks, trained-model effects, and evidence of deletion.
- How does payment work? Document price, acceptance, timing, taxes, expenses, refreshes, recurring use, audit rights, and dispute handling.
- What happens if controls fail? Review incident notice, remediation, indemnities, liability limits, insurance, audit evidence, and governing law with counsel.
Final verdict: should you sell to AfterQuery?
AfterQuery is a credible concept for turning expert activity into AI data. If your goal is to sell an existing company dataset through a published partner process, micro1 is the more direct starting point.
The final decision should depend on the specific dataset, who holds the rights, the named buyer, security evidence, license language, and total economics. Use qualified legal, privacy, security, and tax advisers. De-identification can reduce exposure; it does not erase every obligation or strategic risk.
Sources and methodology
We prioritize official company, regulator, and platform materials. Company claims are treated as claims rather than independent verification.