Post-training data operation for AI labs

Better RL tasks, at a better price, at full speedWe lay the data pipeline
You open the valve

Vendor sourcing, sample verification, QA ping-pong: the operation of buying data lives inside our pipe. Every task carries a grade and a price, so your lab ingests in cost-effectiveness order until the budget runs out.

35+

vendors in our pool

15+

under NDA, samples and pricing in hand

13

building Terminal-Bench-style tasks

The marketplace and acceptance desk between every lab and every vendor

  1. 01Vendor comparison reportDifficulty, quality, and price across the pool, measured by execution. Anonymized by default. Names and exact prices open under NDA.
  2. 02Random-sample re‑measurementVendor samples are cherry-picked. We re-measure on a random draw pinned to public randomness.
  3. 03Acceptance testing on deliveryEvery delivered batch is executed and scored before payment. We keep the vendor on schedule.
  4. 04One-month defect warrantyDefects found within a month are fixed within three business days. Tasks we cannot fix are refunded or replaced under the vendor contract.
  5. 05Continuous environment hardening (monthly subscription)As the model gets stronger, new exploits appear. Environments you bought keep getting checked, and what breaks gets fixed.

At Delphi, the Greeks built a temple to Apollo, the god of reason, light, and order. Carved at the entrance:

Know thyself.

The temple stands where Apollo slew Python, the serpent of chaos. Nietzsche called this the oldest tension: Apollonian clarity against Dionysian instinct.

Today's agents are Dionysian. They act on instinct, take unpredictable paths, and sometimes fake success: hiding test output, editing files they should not touch, gaming the metric while missing the point.

Delphik is the Apollonian response. A marketplace with honest measurement is only the first step. On the environments you buy, we run post-training trace monitoring and environment hardening against reward hacking: watching how agents behave, with Apollo's reason.

Today, the bottleneck to safe superintelligence is neither research nor engineering. It is large-scale operations infrastructure. We build that infrastructure.

The mission is agents that train predictably. That is what the name means.