DataFactor review: what sellers actually get

Developers with repositories, and companies with 20+ staff.

The partnership page covers three families of data: productivity tools (chat, email, documents, meetings), operations (CRM, support, finance, HR, procurement) and engineering (repos, pull requests, issue trackers, incidents). You can exclude any of them, including source code, HR records and executive email.

The tiers are wide, so ask which one you fall in and what moves an offer within it.

Its case studies give a narrower picture. DataFactor lists typical payouts by data type: code repositories $120K to $1.5M, workplace records $200K to $1.2M, call recordings $150K to $900K and CAD files $150K to $2.4M. These are its own figures for deals it says it closed, with every name removed.

DataFactor says submitting a form does not authorize any access, and no tool is connected until your company signs off on scope, legal terms and security. It also says it does not sell raw source data or give buyers open access to your systems. Permitted use, retention, deletion and buyer restrictions go into the partnership agreement.

The code score tool wants access to your repos. Per DataFactor, any GitHub token you paste stays with the session, for read-only access and for its sales team to follow up. If you test it, create a fine-grained read-only token scoped to one repo and delete it when done.

The website terms cap DataFactor's liability for anything on the site at $100 and say estimates and scores are not offers. Your deal terms live only in the separate written agreement.

Its privacy policy describes a referral program that takes contact details of companies you refer. We could not find a public page with its referral terms.

DataFactor sources private datasets from companies and licenses them to AI teams. On one side it runs a data partner program for business owners and a free code scoring tool for developers. On the other, AI teams can request a custom dataset. Its team page describes a small senior team; one co-founder is presented as the founder of BairesDev, a large software services firm. No funding is published.

1. 1

   Chat with the team

   It maps your tools and data footprint. Nothing is connected at this stage.

2. 2

   Data map and estimate

   An estimate based on tool coverage, volume, history, language and the categories you approve.

3. 3

   Legal and security review

   Your side reviews permitted use, exclusions, retention, deletion and buyer restrictions.

4. 4

   Connect approved tools

   Read-only, least-privilege access to the systems in scope only.

5. 5

   Extract and anonymize

   Personal, customer, company and credential details are removed or masked.

6. 6

   Payout and report

   Payment plus an analysis of your workflows, bottlenecks and automation openings.

Its published tiers make it a handy price benchmark. Find out who licenses the data (developers, research groups) and their retention period.

We run the same checks on every buyer we rank. They take an afternoon and they protect the one thing you cannot take back: the copy of your records.

• Find the legal entity. A registered company name, an address and at least one named person. A Facebook form alone is not enough.
• Get the offer and the contract in writing before any export. A sample agreement tells you more than a sales call.
• Ask who strips personal data, how, and when. You should approve the method and see a sample before anything leaves your systems.
• Ask where the data goes. Which AI companies receive it, whether you are told, and whether it can be resold.
• Ask how everyone is paid. By you, by the buyer, or out of your deal. Referral fees are normal; hidden ones are not.
• Get a second quote. Terms from at least one other buyer is the fastest way to know if an offer is fair.