Data hub

Customer support datasets pair real customer problems with the steps agents took to resolve them, often with satisfaction and QA scores attached. AI labs license them to train and evaluate support agents, agent-assist tools and intent models. SourceX looks for multi-year archives from helpdesks such as Zendesk, Intercom, Freshdesk and Salesforce Service Cloud.

Recorded calls and meetings capture how people actually talk at work, with interruptions, accents, jargon and decisions. SourceX looks for contact-center calls, sales calls and internal meetings with transcripts and outcomes, for speech recognition, voice agents and summarization models.

Workplace chat and email show how teams coordinate, decide and hand off work over months and years. SourceX looks for Slack and Microsoft Teams workspaces, email archives and complete archives from wound-down companies, for training agents that work inside real organizations.

Software engineering data shows how professional teams write, review and ship code: private repositories with full history, pull requests with review threads, issue trackers and incident records. AI labs license it to train and evaluate coding agents on real codebases that aren’t public.

Sales and CRM data records how deals actually progress: accounts, opportunities, activities, emails, proposals and the final won or lost outcome. AI labs license it to train sales and revenue agents and to test predictions against what really happened.

Company documents and knowledge bases hold how an organization actually works: SOPs, runbooks, wikis, and documents with their revision history. AI labs license them to train agents that read, follow and update real internal documentation.

Finance and accounting data shows the work behind the numbers: invoice coding, approvals, reconciliations, close checklists and financial models. AI labs license it to train agents for accounts payable, month-end close and planning, work that public filings don’t show.

Legal and compliance data covers how companies negotiate, approve and document obligations: contract drafts and redlines, signed agreements with clause data, and completed security questionnaires. AI labs license it to train legal and compliance agents on real negotiations rather than final public filings.

Operations and workflow data records how work actually gets done: task histories with decisions and outcomes, field-service jobs, quality inspections, and recordings of people doing real work. AI labs license it to train agents and robots on real-world processes that public data barely covers.

Multi-turn tickets with internal notes, tags, status history and the final resolution.

Real-time chats and in-app messages, including bot-to-human handoffs and outcomes.

Tickets, chats or calls graded by human reviewers against a scoring rubric.

Inbound and outbound service calls with transcripts, disposition codes and handle times.

Discovery calls and demos with transcripts, linked to whether the deal was won or lost.

Team meetings, planning sessions and reviews with transcripts, agendas and follow-ups.

Channel and thread history with reactions, files and bots over several years.

Mailbox exports with threads, attachments and calendars from Google Workspace or Microsoft 365.

Chat, tickets, code, documents and email from companies that have shut down, kept together.

Proprietary repositories with every commit, branch and tag, plus build and test setup.

Diffs, review comments, requested changes, approvals and CI results for each change.

Tickets, sprints and status changes from Jira, Linear or GitHub Issues, linked to code.

Alerts, incident timelines, responder chat and the postmortems that followed.

Accounts, opportunities, stage changes and activities through to the final outcome.

Prospecting emails and follow-ups with real replies, meetings booked and deals created.

Requests for proposal, the responses submitted and whether each bid was won.

Step-by-step procedures teams actually follow, with versions and owners.

Team wikis, internal help centers and decision records with links and edit history.

Business files with version history and comments, showing how drafts became final.

Vendor invoices with GL coding, approval chains, exceptions and payment status.

Close task lists, account reconciliations, journal entries and reviewer sign-offs.

Financial models, budget cycles and forecast revisions, with assumptions and comments.

Each draft of an agreement, tracked changes, comments and the emails around them.

Signed agreements with extracted clauses, key dates and obligations.

Vendor security questionnaires and audit requests with the answers given.

Work items from request to completion, with each action taken and the result.

Jobs from dispatch to completion: diagnosis notes, parts used, photos and outcomes.

Inspection checklists, defect findings, photos and corrective actions.

Recordings and event logs of people completing real tasks in business software.

New video of skilled physical work, coordinated with partner businesses.

AI agents that do business work need examples of that work: what a person saw, what they did in which system, and how it turned out. Licensed enterprise data supplies those examples from operating companies, including workflow histories, support tickets, screen recordings and the procedures teams follow.

Reinforcement learning needs a way to tell good work from bad. Enterprise records often carry that signal already: a ticket resolved or reopened, a deal won or lost, a QA score, a repair that needed a callback. SourceX sources data with those outcomes attached, for building RL environments and training reward models.

Evaluations only measure what they claim if the model hasn’t seen the test data. Private enterprise data isn’t on the public web, so it’s far less likely to be in training sets, and the license can limit use to evaluation. It gives labs held-out material in real domains: private code, support tickets, contracts, finance work and internal documentation.

Portfolio companies with years of tickets, code and customer conversations.

Companies shutting down, whose archives still have value.

Owners of established businesses, before or alongside a sale.

Clients with long, well-kept operating histories.

Clients whose systems you already know from the inside.

Support operations with recorded calls, chats and QA scores.