Companies buy an API key and start burning tokens. Context gets re-explained every session, questions that were answered last week get answered again this week, and nobody can see what any of it costs.
Illustrative. Multiply by every engineer, every day, for a year — then add the ones nobody thought to count.
Server addresses, which key goes where, how your deployment works, what you decided last month. Every new session starts from nothing and your team pays the re-explanation tax.
An issue was worked out in March. In June the same issue arrives, and the model reasons it through from scratch at full price — because nothing recorded that it was ever solved.
Someone types "hi". Someone types "thanks". Every one of those is a billed round trip to a frontier model, and there is no tool on the market that stops it.
Eighteen AI gateways, ten memory platforms, and the memory features now shipped by OpenAI, Anthropic, Google, AWS and Microsoft. Two things every company we speak to wants, and not one product delivers — not paid, not open source, not on a roadmap.
Filters can block a request before it reaches the model, but a block is a refusal, not an answer. Caches only help once a model has already answered. Routers send cheap questions to a cheaper model — still a call, still tokens, still latency.
0 of 18 productsEvery memory product retrieves context that goes into a prompt — and the prompt still goes to a model. They store statements. None of them stores an outcome: this was solved, this is what worked, and these are the conditions under which it still holds.
0 of 10 productsTeams that deploy AI with an outside partner get to production roughly twice as often as teams doing it alone. The reason is not the software. It is that somebody does the unglamorous work of finding out what is actually happening first.
We take your logs and tell you what share of your AI spend answered a question you had already paid to answer. You get the number and the report whether or not you work with us again.
We learn your products, your infrastructure and your workflows, then deploy Conductor against them — your models, your keys, your data, in your environment or ours.
It gets better with use, because every resolved task is recorded as resolved. We report the ledger monthly: what ran, what it cost, and what it would have cost without us.
The layer that sits between your team and the models you already pay for. It is not another model — it decides whether a model is needed at all, which one, and what it should already know.
A resolved task is recorded with the conditions under which its answer holds. When those conditions still hold, you get the answer without paying for it twice. When they do not, it goes to the model.
Trivial input never reaches a model. Routine work goes to cheap models. Only the hard questions reach the expensive ones — and you can see the split.
Every call is recorded with its model, its cost and its reason. Spend caps are enforced per team, not discovered at the end of the month.
Actions are tiered by risk. Reads run freely; anything that changes a production system or moves money stops and waits for a person. That tier can never be auto-approved.
Most companies in this space would show you logos and a savings percentage right now. We are new, and the honest version is more useful to you than an invented one.
Tell us what you are running and what it keeps forgetting. We are taking a small number of early engagements and we would rather they be a good fit than a big number.