Development
Most generative AI projects fail at the same place: the demo works and the rollout does not. We staff engineers who have taken LLM features past that point, which means retrieval that finds the right document, an evaluation harness that proves a change is an improvement, and a cost model that survives real usage.
A 30-minute call to establish what exists today, what has to be true at the end, and what the real deadline is. No proposal is written before this.
You receive three to five profiles with the specific experience the work needs, their rates, and their availability. You interview whoever you want.
Most engagements start with a short paid trial on a real task. You pay nothing until you decide to hire, and nothing at all if the trial does not convince you.
One person owns the outcome and reports weekly against what was agreed. Scope changes are priced before they are built, not after.
When this is the wrong choice
If your data is not yet accessible or governed, the honest first step is a data engagement, not an AI one. We will say that on the call rather than sell a pilot that cannot succeed.
Tell us what you are trying to ship. You get a matched shortlist in 48 hours and pay $0 until you hire.
Claude, OpenAI, Gemini, and open-weight models running on your own infrastructure. Model choice is an engineering decision driven by cost, latency, and data residency, not a preference.
A focused feature with retrieval and evaluation typically reaches production in six to ten weeks. Prototypes take days, which is exactly why prototypes mislead teams about the real timeline.
Yes. Common constraints are no data leaving the cloud account, no training on customer data, and full audit logging, and all three are standard architecture choices rather than blockers.
Vetted engineers, matched in 48 hours. No recruitment fees, no payment until you hire.
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