Generative AI Consulting Services

It starts with an assessment, not a build. The first goal is to establish which of your use cases generative AI genuinely fits, given the data you have, the accuracy the task needs, and the cost of a wrong answer. You get an honest ranking of what to do now, what to pilot, and what to leave alone, with the reasoning written down. Implementation is a separate, later decision.

For anything beyond generic drafting, yes. Useful business AI is usually grounded in your own content and data through retrieval, so the quality of that data and how it is structured matters as much as the model. Part of the assessment is a data-readiness review: whether the data to ground a given use case exists, and what it would take to make it usable.

It depends on data sensitivity, cost at your expected volume, and how much control you need. Hosted models are fastest to start and often cheapest at low volume; self-hosted or private deployment makes sense when data cannot leave your environment or when volume makes hosting economical. The recommendation is made per use case, not as a blanket rule — and self-hosted deployment can be scoped as its own engagement.

By treating the model as one component and engineering the rest. That means grounding answers in your data, retrieving the right context, constraining what the system is allowed to claim, showing where an answer came from, and building an evaluation approach so quality is measured rather than assumed. Most of the reliability lives in the architecture around the model.

Often it is overkill. Agents earn their place when a task has clear tools, checkable results, and a tolerance for supervision; elsewhere a single generation or retrieval step is more reliable and cheaper. If an engagement points toward agents, they are scoped with defined tools, verifiable results, clear boundaries, and a human kept accountable.