Custom AI for the CIO: how we turn a sprawling AI portfolio into a system the board can sign.

The architecture is built around your existing stack, not the other way around. We integrate at the system-of-record layer through your warehouse and at the access layer through your identity provider, whether that is Entra, Okta, Ping, or a federated SAML. AI inference traffic respects the same row-level and attribute-level access control that already governs your warehouse, so a user only ever sees AI output drawn from data they already had permission to query. The audit log captures who, what, when, and which model version, and writes back through your existing observability stack.

The foundation-model layer is abstracted behind an interface we control, so swapping providers is a configuration change in production and a rerun of the evaluation suite. We do not pin prompts, tools, or schemas to a single vendor's API surface. In practice, a foundation-model swap takes two to four weeks of regression testing, and the result is documented in the architecture spec we deliver in week two of Build.

We use vendor-agnostic patterns wherever the abstraction cost is low: open standards for vector retrieval, queue-based decoupling for orchestration, OpenAPI-first integrations for downstream systems, and observability through OpenTelemetry. Where we use a managed component, we document the swap path and the data-portability terms. The architecture decision is yours, and the documentation supports your team to revisit it without us.

An evaluation harness is a versioned suite of test cases tied to your real workflow: ground-truth examples, edge cases, refusal cases, and adversarial inputs. It runs on every release, gates promotion from staging to production, and reports drift on production traffic. The harness grows over time as human reviewers correct outputs, and those corrections become regression tests the next release has to pass.

We model TCO across three years, including foundation-model token spend, integration build cost, evaluation overhead, change-management training, and the operate-phase retainer. The model is shared at scope sign-off and updated quarterly during Operate. Surprises are flagged before the contract is signed, and the retainer is structured so your team can take over without re-signing.