AI Architecture Consulting
Design the system around the decision it must support.
Who commissions this
A defined technical problem.
Enterprise technical leaders choosing an AI architecture, reviewing an existing design, or reconciling vendor, data, and deployment constraints.
Engineering scope
What the work covers.
- Map business decisions, failure costs, users, and existing systems before choosing models.
- Compare retrieval, fine-tuning, orchestration, and conventional software against an explicit baseline.
- Design data boundaries, tool permissions, human review, observability, and rollback paths.
- Review hosting options, model licensing, portability, latency budgets, and operational ownership.
Representative deliverables
Work your team can inspect and use.
- Architecture decision record with alternatives and rejection reasons
- System and data-flow diagrams with trust boundaries
- Evaluation and acceptance plan tied to business requirements
- Prioritized implementation roadmap and dependency register
A measurable outcome, scoped together.
An architecture your team can challenge, implement, and operate. Model selection follows requirements, rather than driving them.
Typical engagements begin at $50,000. Before a proposal, we agree the business objective, data access, system boundaries, deliverables, acceptance criteria, and operating owner. Timelines depend on scope and access.
Related technical evidence
Production AI architecture and inference economics · How to verify an AI performance claim
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