SERVICE LINE B
The biggest obstacle to adopting generative AI is usually not the technology — it's the question "can this data leave the building?"
The biggest obstacle to adopting generative AI is usually not the technology — it's the question "can this data leave the building?" Customer lists, financials, medical records, design drawings, process parameters, legal documents: once they are sent to a public cloud model, data sovereignty, trade secrets and regulatory compliance all flash red at once.
The answer is not to avoid AI, but to match the sensitivity of each kind of data to where the model runs. That takes more than engineering. It takes judgment on which data counts as sensitive, under which regulation, and who signs off — which is why this is an advisory problem.
We map the sensitivity levels of your internal data and the regulations that govern it (personal data protection law, trade secrets law, healthcare regulations, and confidentiality and data-residency clauses in customer contracts), then set rules and review procedures for which AI uses are and aren't allowed for employees. Compliance opinions are issued by our legal advisory group.
We design a split architecture based on sensitivity: confidential data stays on-premises for inference, general tasks go to cloud models, and a gateway and audit logs control what passes between them. This includes assessing hardware specifications, model selection, cost structure and the path for scaling.
We turn internal documents, policies and past projects into a knowledge structure that models can cite correctly, with permission tiers, traceable sources and answer-quality evaluation — so AI doesn't get things wrong without anyone noticing.
For repetitive work that can be standardized (document review, data preparation, first-draft replies, cross-system syncing), we design agent workflows and define where humans review the output and who is accountable.
We set up model usage logs, output retention, risk tiers and regular review mechanisms, preparing you for audits by international customers, supply-chain security questionnaires and future regulatory requirements.
A dedicated advisor works on-site with your team, supporting project monitoring, cross-department coordination, expert meetings and consolidation of results — so the system actually runs inside your organization instead of stopping when the advisors leave.