For a software company that needs one AI feature in production, an AI production sprint is four to six weeks. We ship one search-and-answer system or one agent, with evaluations, monitoring and a runbook. After that you run it, or Managed AI operations keeps it healthy. Contact us for pricing.
One workflow
Internal search and answers, a customer or support agent, or an operations agent. We say whether it fits four to six weeks, and what the fixed price is, before we start.
We pick one. The next system is another sprint, or a change order after this one is live.
Scope and price are fixed before we write the code.
One system, one boundary, the people who will use it, and how we will know it is good enough. You get that in writing, with the price.
Private or virtual private cloud on Amazon Web Services, Microsoft Azure, Google Cloud or GleSYS, or on your own servers, by default. Data stays with you. Audit logs. We do not train on your data unless you ask us to, in writing.
A golden evaluation set, a continuous integration gate, monitoring hooks for quality, latency and cost, and a runbook: what it does, who to call, how to turn it off.
A written recommendation: keep it as it is, extend it under a new fixed price, or stop. Scaling is not assumed.
See Managed AI operationsA demo is not the delivery. These ship with the system.
Private model / fine-tuning can start on its own or after an AI production sprint. Contact us for pricing. We fine-tune an open model on your examples only when the evaluations show the hosted model falls short. If it is good enough, you stay on it.
You already know the workflow is worth trying. We put one slice of it into production.
The sprint is one system. Care, a coding setup, a person in the team, or a platform takeover are the other shapes. Private model / fine-tuning can start on its own or after an AI production sprint.
How we think about shipping one system and knowing if it still works.
Short answers before you book a conversation.
No. It is a build. At the end one system is in your production environment, with evaluations, monitoring and a runbook. We do not deliver a recommendation deck as the result.
A fixed price, given before we start. It depends on the variant and on where it has to run. Contact us for pricing.
No. One system. The next one is another sprint, or a fixed-price change order once the first is live and the evaluation set exists.
You run it, your team runs it from the runbook, or we keep it healthy on Managed AI operations. Scaling is a go or no-go, not an automatic next phase.
In your environment. Private or virtual private cloud on Amazon Web Services, Microsoft Azure, Google Cloud or GleSYS, or on your own servers, is the default, with audit logs. We do not train on your data unless you ask us to, in writing.
Yes. Private model / fine-tuning can start on its own or after an AI production sprint. We fine-tune an open model on your examples only when the evaluations show the hosted model falls short, and we host it on Amazon Web Services, Microsoft Azure, Google Cloud or GleSYS. Otherwise we recommend you stay on the hosted model.
Ready to scope it?
We will tell you straight if it is one sprint, if it should be care of something you already have, or if we are the wrong people.
