Monetize your B2B softwarecompany's data

DataCow partners with B2B software companies to help improve AI systems using real-world sales and CRM, support, delivery, and operations workflows. Participating companies receive compensation based on the value of the data provided and may also benefit from improvements tied to the specific use cases they contribute. Data is used to evaluate and improve AI capabilities in realistic B2B software environments.

Companies of many sizes can take part, starting from around 3 to 5 employees, as long as you serve business customers rather than only individual consumers. IT services firms, managed service providers, software companies, and product teams can all qualify. Beyond size, we look for operationally mature companies with established documentation and complete records: full years rather than fragments, with CRM records, tickets, runbooks, project records, and review notes kept together so the whole trail of a piece of work is intact. Our current focus is work in sales and CRM, IT support, implementation and professional services, software delivery, infrastructure and DevOps, and security operations. We prioritize US companies, followed by other Western markets.

We are looking for data that reflects how B2B software work is actually performed within a company, across sales and CRM, support, implementation, software delivery, infrastructure, and security. This may include Salesforce or HubSpot records, deal notes and proposals, standard operating procedures (SOPs), ticket histories and runbooks, implementation workpapers, pull-request and release records, incident timelines, internal knowledge bases, templates, review notes, and other practice artifacts. The most valuable datasets capture real sales, engineering, and operations expertise, professional judgment, and repeatable workflows that help AI systems learn how the work is executed in practice. Source code and repositories can be in scope when you approve them. Secrets, credentials, and production access are not.

Protecting customer data is a core principle of the program. Personally identifiable information (PII), customer identifiers, secrets, and credentials are removed when relevant, access is restricted to a limited number of authorized team members, and your data is processed through isolated pipelines that prevent it from being commingled with other engagements. Data is retained only for agreed periods, you have the opportunity to review representative samples before use, and deletion requests are honored according to the terms of the engagement. In addition, employees with access to data systems use company-managed devices and follow established security controls and training requirements.

All data is handled under strict contractual protections. Before participation, we agree on the approved data scope, security and confidentiality standards, anonymization and redaction requirements, and internal approval processes. Original datasets are retained only for processing purposes. Sensitive information is scrubbed before any downstream use, and all confidential information is removed. Original datasets are deleted after processing. No data is publicly shared, no customer information is exposed, and companies retain ownership of their underlying data.

You do. Companies retain ownership of their underlying data.

After data is shared, it is processed according to the agreed use case and handling requirements. Depending on the nature of the data, personally identifiable information, customer identifiers, secrets, and credentials may be removed and certain datasets may undergo synthetic rewrites that preserve workflow structure while reducing direct ties to original records. Data is retained only for a limited period based on agreed retention policies, and you are given the opportunity to review representative samples before prepared data is used. Upon request or at the end of an engagement, data is deleted according to the agreed retention and deletion terms.

Compensation is determined based on the characteristics and value of the data being contributed. Factors considered include dataset size and volume, workflow complexity, data quality, the B2B software expertise represented, and the uniqueness of the operational knowledge contained within the dataset. Each partnership is evaluated individually, and compensation is aligned with the overall value and relevance of the data provided.