Target-state architecture.
We design the architecture your AI roadmap needs, from data and retrieval to models, agents, cloud and GPU capacity, and we design it knowing we may be the ones who build it.
Outcomes for your business
First tangible output: Target-state reference architecture
The short answer
What is a target-state architecture for AI?
A target-state architecture is the design of where your systems need to be to support your AI roadmap. For AI it covers the experience layer, agents and applications, models and evaluation, data and retrieval, and the compute underneath, including cloud and GPU capacity, plus the security and cost controls that span them.
Sound familiar?
You need this if…
What we do
Scope of the work.
Layered reference design
Experience, agents, models, data and compute, each with clear boundaries and owners.
GPU and compute strategy
Right-size training and inference capacity across cloud, specialty GPU providers and on-premises.
Cloud landing zones
Accounts, networking, identity and guardrails that make new AI workloads safe by default.
Data and retrieval
Pipelines, vector search and governance so every app gets the right context.
Evaluation and observability
Shared evals, tracing and cost tracking across every model and agent.
Security by design
Data boundaries, secrets, model access and audit trails designed in from day one.
Deliverables
What you walk away with.
Everything is yours to keep, whether you build with us or on your own.
Target-state reference architecture
Layered diagrams with components, flows and ownership.
Decision records
The key choices, the options considered and why we chose each one.
Cost model
Estimated run costs for training, inference and data at your expected scale.
Migration path
How to get from current state to target state in safe increments.
Platform backlog
The build work, ready for your platform team or ours.
How it works
Four steps, one team.
Baseline
Map current systems, data flows, costs and constraints.
Design
Draft the layered target state against your roadmap and non-negotiables.
Validate
Pressure-test the design on costs, security and a real use case.
Plan
Sequence the migration and write the platform backlog.
Powered by our platform CloudTeams™
Context stays on one record, not in a deck
Target-state architecture: AI, cloud & GPU runs on the CloudTeams™ Platform, the AI-powered platform we built for firms that sell expertise. Mach is its first customer.
- Contacts, notes and tasks from discovery stay on one record
- A read-only AI assistant answers questions across it, so nothing is re-explained
- One team carries the work from readout to roadmap
Keep exploring
Related services.
Platform Engineering: AWS, Azure, K8s & GPU
A reliable cloud, Kubernetes and GPU foundation for AI workloads, with security and cost controls your team can operate.
Explore →Modernization & Migration Strategy
Legacy virtualization is getting more expensive and harder to staff.
Explore →AI Readiness & Technology Assessments
Before you fund an AI project, find out whether your data, platforms, people and governance can carry it.
Explore →Field Notes
Go deeper.
Practical playbooks from the team that does this work.
The AI Readiness Assessment: 7 Questions to Answer Before You Fund Another AI Project
A practical AI readiness assessment: 7 questions across strategy, data, infrastructure, security, and people that decide whether an AI investment ships or stalls.
Read the field note →Playbooks for teams that ship.
Practical guides on AI architecture, agentic-first engineering, and technical go-to-market.
Browse all field notes →FAQ
Questions about target-state AI architecture
Straight answers about target-state architecture: AI, cloud & GPU. Still curious? Ask us on a free call.
Are you tied to a cloud or GPU vendor?
No. We design for your workloads and constraints, then recommend the mix of cloud, specialty GPU providers or on-premises capacity that fits.
What layers does the architecture cover?
Experience, agents and applications, models and evaluation, data and retrieval, and compute, plus security, cost and observability across all of them.
Do we need a target state if we only have one AI app?
If more apps are coming, yes. Designing the shared layers now is far cheaper than untangling five separate stacks later.
Will you build it too?
We can. Our Engineering & Development practice builds platforms and AI solutions on the architectures we design, with the same senior team.
How do you estimate costs?
We model training, inference, storage and data transfer at your expected usage, using current provider pricing, and show the assumptions so you can adjust them.
Project inquiry
Tell us about your project.
Tell us where you are and what you need. We will tell you honestly whether target-state architecture: AI, cloud & GPU is the right next step.
- 01Tell us where you are headedA two-minute form. Share the outcome you want and any constraints.
- 0245-minute discovery callWe listen first. No pitch deck, no sales script.
- 03Your readout within one weekWhere the gaps are and which practice will move the needle fastest.
Ready to choose a time? Schedule your free consultation →
Tell us about target-state AI architecture
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