Turn real company data into complete AI benchmarks

Turing gives company data a second life. We take standalone datasets such as call transcripts, card-spend panels, clickstream, design files and codebases. We remove personal and identifying information, then place the data with frontier AI labs.

Data owners get paid for data that otherwise sits idle. Labs get real-world data they can't find anywhere else. The approach works because that data stands on its own. A transcript is still useful once the names are removed.

Think about a company's full working record. Email threads point to project files. Deals tie to spreadsheets. A codebase carries years of commits and the conversations behind them. The value is in the connections, and standard scrubbing breaks them.

Turing’s mission is to help this kind of coherent data accelerate real economic progress. We replace every identity across the web of people, clients, and projects, while every number, transaction, and relationship stays exactly as it was. The result can't be linked back to the source, and every record still ties out.

But that’s not all, we go a step further and build a complete benchmark on top of it.

Each benchmark is designed around three parts.

• RL environment. A working simulation of the rebuilt company, with its files, tools, inboxes, codebase and history. An AI model works inside it the way an employee would: opening documents, searching records, running code.
• Tasks. Realistic assignments based on the work the company actually did. Reconcile the numbers on a deal. Fix a bug using the repo's history. Write a project update from scattered threads. Many will take dozens of steps.
• Verifiers. Automated checks that decide whether the model got it right. The numbers tie out, the tests pass, the answer matches what the records support. Verifiers turn each attempt into a score.

Together they form a loop. The model takes a task, works through the environment, and the verifier scores the result. Labs can use that loop to train models with reinforcement learning and to measure how well they handle real work.

For AI labs, these benchmarks come from real companies. Synthetic environments miss the depth and quirks of how work really happens. A benchmark built on real records shows whether a model can do the job, not just pass a test. AI labs have the flexibility to purchase off-the-shelf datasets or fully private custom datasets. 

For data owners, data that was too interconnected for a simple scrub now has a buyer. Full company archives, project files and codebases with their history are exactly what we built this program to support.

Find out what your data is worth.