Sell Your Company Data to AI: micro1's $1 Million Opportunity

Your company has spent years learning how to do its work. Some of that knowledge lives in employees' heads. Much of it lives in support tickets, operating procedures, project histories, approval records, and the trail of decisions that keeps the business running.

AI developers want to understand that work. A model can read a textbook about logistics or accounting, but learning how a real team handles an exception requires something more specific: the problem, the information available, the decision, and what happened next.

micro1 offers companies a way to earn money from those records. Its company partnership page advertises compensation of $100,000 to $2 million+ for approved data packages. The appeal is straightforward: an asset you already have could produce additional income.

The business decision is whether the payment is worth the time and information involved. This guide explains what the company wants, how the process starts, and what to settle before your records leave the business.

micro1's data partnership offer presents three starting bands, based on the scale and distinctiveness of the contribution.

These are individually negotiated partnerships. A useful package combines enough material to capture a workflow with the expertise needed to explain it. A connected history of difficult decisions can be more interesting than thousands of generic documents.

Its payout estimator provides an initial estimate using company characteristics. The final offer follows a review of the actual data.

Ask whether the offer pays for a historical package, ongoing contributions, or both. Then ask when the money arrives: on signing, delivery, acceptance, or later milestones. A million dollar engagement spread across continuing work is a different proposition from a single archive sale.

The number that matters to your business is the payment after preparation costs. Include the time spent exporting records, reviewing confidential material, explaining workflows, and answering follow up questions. Establish who pays for this work before committing staff to the project.

The current enterprise opportunity targets businesses with 30 to 200 employees, mature workflows, substantial documentation, and modern software tools. Examples include technology, consulting, recruiting, financial services, legal, healthcare administration, and logistics.

An established business with a repeatable process is a natural starting point. You should be able to explain how a task gets completed, where the supporting records live, and who on your team understands the exceptions. You do not need to be an AI company.

The broader program accepts companies with 30+ employees, primarily working in English. Current demand favors the United States, United Kingdom, and Canada. The opportunity you apply to determines the relevant requirements.

A useful first conversation focuses on one process rather than your entire business. For example, how your support team resolves escalations, how accountants investigate reconciliation differences, or how dispatchers recover from a missed delivery. It is easier to describe, price, and review a defined contribution.

The enterprise listing asks for internal documentation, SOPs, playbooks, knowledge bases, communications, templates, project workflows, CRM metadata, and task histories. The common thread is evidence of how work gets done.

A procedure explains the intended process. The records around it show how employees applied that process when something went wrong. Together, they can reveal judgment that is missing from a clean instruction manual.

Imagine a customer support case. The useful material might connect the original request, troubleshooting steps, escalation, and final resolution. An accounting example might connect a discrepancy to the evidence reviewed and the approval that resolved it. These examples illustrate the kind of operational story a company can inventory.

The partnership process includes agreeing the data requirements. Use the discovery conversation to settle the delivery format before your team begins extraction.

Begin with a description of your systems and records. Ask which exports micro1 accepts, whether attachments and timestamps are needed, how related records should stay connected, and who handles conversion. Preserve those connections when preparing a sample: an isolated document can lose the context that makes it valuable.

Establish interest in the workflow before cleaning your entire archive. Then agree a small sample and an export method with the team that will use it.

The initial application asks for company and representative contact details, your role, and a website. You can begin without uploading your company's archive.

micro1 reviews the application, uses an initial AI interview to assess fit, and arranges a discovery call for promising companies. That conversation covers the records, privacy requirements, and the proposed contribution. Agreement and onboarding follow if both sides want to proceed.

Use the discovery call to establish how collection will work for your company: exports prepared by your team, employee demonstrations, or any proposed access to your systems. Agree the practical arrangements and who handles each task before starting.

The enterprise listing says micro1 handles coordination, anonymization, and program management. Your team still needs someone who understands the records and can review what is prepared.

A practical setup is an operations lead for the workflow, an IT contact for extraction, and a decision maker for commercial approval. One employee may fill several roles in a smaller company. Establish the expected hours, sample review, delivery deadlines, and any continuing contribution so the project does not quietly become a second job for your team.

Ask for a project schedule with the proposed offer, including when review ends and what triggers each payment.

micro1 builds data and evaluation tasks for AI developers. Its Realm platform combines expert selection, data production, review, and quality measurement to support model training and realistic work environments.

Business records help turn a general question into a task with real constraints. Instead of asking an AI to describe customer service, developers can test whether it resolves a case using the available information and reaches an acceptable result. Human expertise helps determine what a good answer looks like.

Its Cortex service evaluates and improves enterprise AI agents. Its robotics work uses demonstrations and recordings to teach physical tasks. Documents, decisions, feedback, and video contribute different forms of knowledge.

Your company is contributing material to that development process. The immediate commercial product is the data partnership, rather than a software subscription you must buy.

micro1 addresses this concern directly in its enterprise offer: it says the purpose is to train and evaluate AI, not to reproduce participating companies, their products, customers, or competitive advantages.

The broader effect is that AI systems become better at business tasks, and those capabilities can eventually reach other companies in your industry. Licensing a workflow contributes to that improvement even when names and confidential details are removed.

The distinction is between exposing your specific competitive information and helping improve general capabilities. A customer list, pricing strategy, proprietary design, or unreleased product roadmap raises different concerns from a sanitized example of how a routine task gets completed.

Before choosing a dataset, ask who receives the prepared material, whether it can reach direct competitors, and which uses you can exclude. If keeping a particular workflow exclusive is central to your advantage, select a different contribution or retain it for your own AI projects.

Will micro1 bring you a more efficient solution later? Its partnership FAQ says companies may benefit from improvements related to the use cases they contribute. Ask whether your offer includes a tool, feedback, access to a resulting solution, or only payment for the contribution.

micro1 says sensitive and confidential information is removed before downstream use, original datasets are deleted after processing, and prepared data is not publicly shared. Your company keeps ownership of the underlying records.

Its privacy process includes restricted access, separate processing pipelines, agreed retention periods, and review of representative samples. The important step for your team is deciding what must be removed before processing begins.

micro1's September 2026 research describes replacing real identities with consistent synthetic ones. If the same customer appears in a ticket, email, and invoice, the replacement keeps those records connected without preserving the customer's real name. This helps preserve the workflow rather than stripping away all its context.

Confidentiality extends beyond personal names. Commercial rates, unusual project details, contractual commitments, designs, and internal strategies may identify the business or reveal an advantage. Mark these categories explicitly and have the process owner review a transformed sample.

For especially sensitive files, ask whether they can be excluded or replaced with a synthetic example. Also ask who can see the originals and what remains after processing. Deleting an original archive is different from deleting a prepared training dataset or undoing what a model has learned.

Your IT or security team should review the transfer method, access arrangements, retention schedule, and incident response process before an export. Start with a selected workflow, not unrestricted access to every system.

Founded in 2022 by Ali Ansari, micro1 began in AI recruiting and moved into human data services in 2025, according to Inc.'s company profile. Its recruiting infrastructure helped it find the specialists needed to create and judge training data.

The company announced a $35 million Series A at a $500 million valuation in September 2025. Antler participated in the round led by 01 Advisors, the investment firm founded by former Twitter executives Dick Costolo and Adam Bain.

In September 2026, Forbes reported a further raise of more than $100 million at a $4 billion valuation, citing people familiar with the deal.

This is a young, rapidly expanding company, not a decades old data vendor. Its funded operating business and enterprise work make the opportunity more substantial than an anonymous data buying website. Its age also makes the proposed project's team, delivery plan, and payment milestones worth understanding.

Microsoft is among micro1's customers named by investor Antler. Forbes' September funding report also names Amazon and robotics company 1X.

Box has a published micro1 case study. The project uses domain experts to build realistic documents, prompts, and scoring rubrics for evaluating enterprise agents. It shows the kind of business specific AI work micro1 delivers.

On the supplier side, the enterprise program page reports more than 100 partner companies and more than $200 million generated for partners. For a closer comparison, ask for a reference from a business contributing similar records.

You are considering a project within an operating AI data business. The remaining question is how mature your particular program is: who runs it, what an accepted delivery looks like, and how previous participants handled the work.

The first risk is sharing something you cannot take back. Removing names may still leave valuable know how in the workflow. Keep your most sensitive commercial assets out of the initial sample and decide which knowledge you are comfortable licensing.

The second is assuming every record belongs to you to share. Customer contracts, employee information, licensed content, and third party systems can carry restrictions. Separate material created by your business from material entrusted to it by someone else.

The third is a project that costs more staff time than expected. Extraction, review, corrections, recording, and ongoing feedback all consume capacity. Agree a defined workload and a point at which additional requests become additional paid work.

The fourth is payment uncertainty. Establish what counts as an accepted delivery, how quickly micro1 must review it, and what happens if only part of the package is useful. That makes the revenue opportunity easier to compare with the effort.

The final risk is confusing immediate payment with long term value. A license may be attractive for routine historical records and unattractive for a distinctive process you intend to automate internally. Compare both uses before choosing the contribution.

For a substantial company data agreement, involving your commercial lawyer before signing or transferring records is a sensible use of the proposed revenue. You can begin the introductory conversation first, using descriptions of your business rather than confidential files.

Give counsel a concrete package: the proposed dataset, payment terms, intended recipients and uses, confidentiality requirements, and any ongoing access or work. Ask them to review what you can share and what permission the agreement grants.

Bring the workflow owner and IT or security lead into the same discussion. They can explain what the records contain and whether the collection plan is practical. If the material includes regulated information or customer secrets, flag it early so review focuses on the actual risk.

The company opportunities also include Commercial Video Data Collection. Instead of licensing an existing document archive, a participating business coordinates recordings of people performing manual tasks in a real commercial setting.

The listing mentions smartphones and wearable phone mounts, with 20+ employees among the preferred qualifications. This route requires planning around workers, locations, customer visibility, and safe task execution. It is closer to a managed collection project than an archive export.

The opportunity spans several regions, including North America, Latin America, Australia, and parts of Asia, the Middle East, and Africa. Ask about current availability, payment per accepted contribution, equipment, capture instructions, and reshoots.

micro1 is worth a conversation for an established company with well documented work and an interest in earning from it. The payment opportunity is substantial, and the company's products explain why real operational knowledge has value to AI developers.

Begin with one workflow and a short inventory. Find out whether micro1 wants it, what it would pay, and what your team must do. Then review the prepared sample and agreement before expanding the contribution.

The strongest partnership is one where the company earns worthwhile additional income, employees can support the project without disrupting operations, and the shared knowledge stays within boundaries the business is comfortable with.

Use the links below to review the current programs and start a conversation with micro1.