Why we exist

We believe in a world where three things matter at once: models that keep getting better, data that is treated as the scarce resource it is, and companies that can put their own records to work.

Most people only think about one of them. We think they are the same problem.

AI model improvement

The first generation of large models learned by reading. They read most of the public internet, and it made them fluent.

Fluency is not competence. A model can describe how to resolve a billing dispute without ever having resolved one.

The next gains come from learning how work is actually done: the order of steps, the judgment calls, the mistakes and how they were fixed.

Evals carry the same lesson. A benchmark is only as honest as its tasks, and tasks drawn from real work tell you whether a model can do the job, not just pass the test.

Better models are not the end in themselves. They matter because they take on more of the work that fills people’s days.

The importance of data

Every leap in AI has followed a leap in data. Architectures spread in months. Good data stays scarce.

The public internet is finite, and the frontier has largely read it.

The most valuable data was never published. It lives in support queues, sales threads and operations logs, written by people solving real problems for real customers.

That data carries something the internet rarely does: an outcome. The ticket was resolved or it wasn’t. The deal closed or it didn’t.

Quality beats volume. One real resolution, with its context and its result, teaches more than a page of scraped text.

Making your company’s data liquid

Most company records are written once and never read again. Data that sits still earns nothing.

Liquid means it can move: pulled out of separate systems, cleaned, anonymized and priced, so it can change hands safely and on fair terms.

Privacy is not a cost of doing this. It is the condition for doing it at all. The people in the records stay out of the data.

A company’s history of doing its work is an asset. It belongs on the balance sheet, not in an archive.

Actual exists where these three meet. We find the work, make it safe to share, and get it to the people building what comes next.

Powering the frontier with actual data

We source, buy and anonymize data from enterprises: customer support threads, sales emails and operational records from the companies that do real work, to power the next generation of AI models and evals.

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Fig. 01 — Your archive, as it is

What we do

From scattered records to training data

Source

We partner with companies that hold years of real work: support threads, sales email, CRM and operational records, scattered across different systems and formats.

Unite

We bring those sources together and organize them into datasets by the kind of work they capture: support, sales, CRM and operations.

Clean

Each dataset goes through the sieve. Noise and duplicates fall through; what stays is structured, tied to its outcome and ready for post-training.

Anonymize

Names, emails, phone numbers and account details are masked before anything leaves. Labs get the work, never the people.

Our ecosystem

Every source. Every buyer.

Comprehensive data ecosystem

Data from any system

Workspace tools, CRMs, ERPs, support desks and accounting software. If a company runs on it, we can pull from it, unify it and make it usable.

Google Drive
Gmail
Notion
HubSpot
Zendesk
Intercom
SAP
QuickBooks

Deep buyer embedding

The best price for your data

No one is more embedded with the labs and vendors buying training data. We know what each one needs and what it will pay, so we can offer you the best price.

THINKING MACHINES

Why it matters

Post-training runs on real work

A model learns to talk by reading the internet. It learns to do a job in post-training, and that step needs examples of the job actually being done.

What post-training is

01
Pre-training
The model reads a large share of the public internet and learns language, facts and patterns. It can write about customer support, but it has never handled a ticket.
02
Post-training
The model is fine-tuned on examples of tasks done well, then reinforced for good outcomes. This is where it learns to resolve the ticket, answer the customer and close the loop.
03
Evaluation
The model is tested on real tasks it hasn’t seen, to check whether it does the work, not just whether it sounds right.

Why real work matters

What labs mostly train on today

Tasks written from scratch by paid contractors
Made for the dataset, not for a real customer
No real outcome attached
Slow and expensive to scale

What Actual Data brings

Work that actually happened, from real companies
Years of it: the edge cases, the mess, the follow-ups
Every thread ends somewhere: resolved, refunded, closed
Records companies have already created

The outcome is what makes it valuable. A support thread that ends in “resolved” is a worked example and a signal of what good looks like, which is exactly what post-training needs.

Models have read the internet. They haven’t done the job.

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