Enterprise AI Architecture Services

Architecture decides which capabilities are shared, which are federated, and how security/cost/integration work — whether you build, buy, or blend. A vendor platform can be a component; it is not a substitute for decision records and integration design.

Most enterprises get a usable target architecture, ADRs, and roadmap in 6–12 weeks. Deeper multi-region or OT-hybrid designs may extend with additional discovery.

Typical engagements range from $80,000 to $200,000 depending on business-unit count and regulatory depth. Implementation of the platform itself is scoped as follow-on engineering.

No. We design for your cloud strategy and often include model-router patterns so teams can change providers without rewriting every app.

Architecture defines the technical backbone; MLOps implements lifecycle tooling on it; governance sets risk policy. Mature programmes align all three — we show the seams clearly so owners are unambiguous.

Deliverables include reference diagrams, ADRs, interface contracts, and usually a thin reference implementation path — not slideware that cannot survive contact with engineering.

If strategy and use-case priority are unclear, start with an AI readiness assessment. If pilots already sprawl and security is blocking scale, architecture is often the right next engagement.

Yes. Token routing, caching, retrieval design, and GPU capacity planning are part of the architecture — especially when spend is already surprising finance.

We inventory them, map risk, and either bring them onto approved patterns or retire them with an alternative path — coordinated with security and procurement.

Good architecture standardises the boring, risky layers (identity, logging, gateways) and leaves product teams free on UX and domain logic. Autonomy without shared controls is what creates sprawl.

Yes. Mutual NDA is standard; multi-BU programmes often add data-sharing protocols so discovery does not leak competitive internals between units.