Collecting the right data now
We have no models yet. We are recording the data that will train them, at the resolution you cannot add back later.
BunBunLabs is going to train its own vertical models. It has not yet. No vertical models exist yet, and training them is gated on compute and on the data infrastructure that turns a live record into a training corpus, which we do not have today. What we do have, and what is easy to start recording too late, is the part that has to come first: the data itself, captured now.
Every governed decision the fleet makes leaves a trace: what was attempted, what the world did in response, and how it was corrected. An action, an outcome, a correction. Recorded together and verified, that triple is the unit a model can actually learn from: not only the chosen answer, but the rejected one beside it and the reason the difference mattered.
The discipline is to capture it at full resolution from the first day, because resolution is the one thing you cannot add back later. If today you record only that a check “passed,” you can never recover how close it came, what exactly it caught, or what the rejected version would have done. The signal exists at the moment of the decision; if it is not written down then, it is gone, and a corpus captured too coarsely cannot be retrofitted. You would have to live the traffic again.
So the honest state is this: the pipeline that records the work is live and generating data now. Across the agents already wired, a governed decision is recorded the moment it is made, at full resolution, and the machinery that binds an action to its real outcome and its correction is built and running. The full reasoning behind those decisions flows through observability we run ourselves, and we have proven a method for linking a decision to the trace that produced it, and are now wiring it in across the agents. What has not happened yet is the loop closing: today we collect traces, not yet complete training triples, because a complete triple needs a real outcome, and the first one lands when a real deal closes. Turning what the pipeline captures into a corpus, and the corpus into models, is the trajectory, and it is gated on compute and data infrastructure.
The schema, the storage, and the measurements that ride on top of it stay private. The principle does not: collect the right thing now, at the right resolution, so that when the compute arrives there is something real to train on, built from governed first-party decisions, not scraped and not synthetic.