A
Workplace data
How real teams communicate, plan and get work done.
- Slack and Teams chat
- Documents
- Calendars
- CRM (Salesforce, HubSpot)
- Support tickets
- Project trackers (Jira, Linear, ClickUp)
Data brokerage for AI training and evaluation
We source data from operating and closed companies. We value it, package it and run the sale across many buyers. Sellers get paid for data they already have. Buyers get licensed, documented, model-ready datasets.
01What we broker
Four main categories, and anything else that is hard to find elsewhere.
A
How real teams communicate, plan and get work done.
B
Full repositories with the context around them.
C
What players did, and the games they did it in.
D
Operating records from specialised sectors.
E
Scarcity drives value. If few others hold it, ask us.
02Process
We handle sourcing, valuation, packaging, the buyer process and delivery.
01
Tell us what data you hold.
02
Get a written valuation range.
03
We run a competitive sale.
04
Deliver the data and get paid.
03Why a broker
Selling direct means one buyer sets the price. A broker brings several buyers to the same dataset.
| Topic | Selling direct | Through Gainfluence |
|---|---|---|
| Buyers | One buyer, on its own terms. | A competitive process across AI labs and data companies. |
| Data types | Often a single category. | Workplace, code, gameplay and industry data. |
| Company status | Often closed companies only. | Operating, winding down or closed. |
| Valuation | You get the buyer’s offer. | You get a written valuation before you commit. |
| Packaging | You export and clean the data. | We export, deduplicate, redact and document it. |
| Other services | Sometimes tied to a shutdown package. | None. We broker data and nothing else. |
Five things matter most. Volume: how many messages, files, repos, tickets or hours of footage exist. History: how many years the records cover. Headcount: how many people produced the data. Usage: how heavily and how consistently the team used its tools. Scarcity: how hard the data is to find elsewhere.
Linked context adds value. Code with its issues, pull requests and the chat around them is worth more than code alone. Rare languages and specialised domains are worth more than common ones.
It depends on the dataset and on how many buyers are in the process. The stages are: submission, valuation, buyer process, then delivery and payment. We give you an estimated timeline with the valuation.
TODO: Add typical durations for each stage once you are comfortable publishing them.
Yes. We work with operating companies, companies winding down, and companies that have already closed. Operating companies often license historical data or a defined slice of it, and keep running as before.
It can be either. A non-exclusive license lets the same dataset be licensed to several buyers. An exclusive license goes to one buyer and is priced higher. Scope and term are negotiated per deal. We will tell you which structure we expect to get the best result for your dataset.
Personal information is removed or redacted before delivery. That covers names, email addresses, phone numbers, account identifiers and other direct identifiers, using automated detection with human review. The exact treatment is agreed with the seller and documented for the buyer.
TODO: Confirm the redaction standard and any legal review step that applies.
For sellers
Send us an inventory. We reply with a written valuation range. There is no obligation to sell.
Sell your dataFor AI labs
Verified buyers get listing details, documentation and evaluation samples.
Request catalog access