Generative AI Consulting

Generative AI consulting is a delivery engagement that turns a bounded workflow into a production system in your cloud, with evaluation and handover, not a slide deck. ReinforcedX’s standard path is four weeks: discovery, environments, shadow-mode, handover. You own weights, datasets, eval suites, and runbooks. Data is not used to train shared models. Book /demo to scope a workflow, or read /faq for how delivery actually runs.

A serious firm selects one workflow, designs controls, builds in your perimeter, measures quality on a golden set, and leaves your team able to run it. We staff one process owner plus one engineer and hold a weekly 45-minute review. Inference is billed by you to Anthropic, OpenAI, Google, Mistral, or your fine-tune — no token markup. Firms that only produce roadmaps are strategy vendors; we ship the build.

Ask who owns the artifacts, where data lives, how quality is gated, and what happens after week four. You should keep weights, datasets, eval suites, and runbooks. Work should run in your cloud with zero-retention defaults. Evaluation should use golden sets, rubric judges, and CI gates. If those answers are vague, keep looking. Our /faq page states the same terms we quote before week one.

A well-bounded workflow follows the four-week standard: discovery, environments, shadow-mode, handover. Financial-services governed pilots typically take 8–12 weeks; healthcare 10–14; ecommerce 6–10, because reviews and integrations are heavier. Thirty days of on-call follow handover. A playground demo can exist in days; production is the eval suite, permissions, traces, and fallback.

Pricing is a platform subscription plus a fixed-scope implementation fee, quoted in writing before week one. A slower week does not become a change order. You pay your model provider directly; ReinforcedX does not mark up tokens. Staffing is one process owner plus one engineer. Exact numbers depend on the workflow boundary agreed at /demo — we do not publish a menu price that ignores scope.

Work runs in the client’s cloud, against your identity provider and data stores. Nothing is copied onto ReinforcedX infrastructure to “improve the product.” Zero-retention provider settings are the default, and client data is not used to train shared models. We are model-agnostic: Anthropic, OpenAI, Google, Mistral, and your fine-tunes. Private or VPC-hosted models are a config choice, not a rewrite.

No. Prompt workshops do not produce permissions, traces, eval gates, or a runbook. Consulting here means a system that takes real traffic under a quality bar your team agreed at kickoff. If you would rather build it yourselves, the same patterns are documented on /how-to. If you would not, the work runs as the four-week engagement and you still own everything that ships.

You own the weights, datasets, eval suites, and runbooks. There is no runtime lock-in that requires us to keep the system alive. Thirty days of on-call cover incidents after handover. Evaluation artifacts — golden sets, rubric judges, CI gates — stay in your repo so the next workflow reuses them. That ownership clause is the same one stated on /faq and quoted before week one.