The Right Model. The Right Place. The Right Cost.

Choose an AI architecture that fits the workload, its intended outcome, and the controls it requires.

A practical workshop for technology leaders, enterprise architects, mission leaders, data and AI leaders, and transformation teams who need to move from AI interest to a responsible deployment decision.

Request a workshop conversation See what you will leave with

01Right
ModelCapability for the task

02Right
PlaceWhere the work should run

03Right
CostCost per useful outcome

=Right Outcome

The architecture question has changed

AI architecture is more than choosing a model.

The useful question is: Which model, using which data, under which controls, should run where?

Participants work on a real workload and make the assumptions visible before selecting technology. The result is a first-pass architecture decision grounded in business outcome, data, consequence, latency, scale, capability, and economics.

A working session, not a technology tour

Leave with a decision you can take back to the organization.

Define the workload

Describe the user, task, current process, desired outcome, and measure in business terms.

Choose the placement

Evaluate device, edge, data center, cloud, and hybrid options against the workload’s requirements.

Design the architecture

Select a pattern, grounding approach, integration points, and human controls that fit the work.

Make cost visible

Consider infrastructure, integration, data movement, validation, rework, security, and operations.

Stress-test the choice

Challenge assumptions about data, network availability, model failure, scale, and information control.

Identify the next action

Name the largest people, process, data, technology, or governance barrier and assign a 30-day action.

The AI compute continuum

Placement follows the workload.

Cloud is not automatically more advanced than local AI. The right location depends on what the workload needs and what the organization must control.

DeviceLocal control

→

EdgeNear the work

→

Data centerEnterprise control

→

CloudElastic scale

What shapes the decision?

Data, privacy, latency, capability, scale, and economics.

What does the workshop prevent?

Choosing a preferred model or vendor before understanding the work it must support.

Four architecture patterns

Match the pattern to the problem.

Local AI

For bounded tasks where privacy, low latency, limited connectivity, or local interaction matter.

User → device → local model → local data

Enterprise RAG

For grounded answers that must use changing organizational knowledge with evidence and access control.

User → retrieval → authoritative data → answer

Hybrid AI

For routing work across local, enterprise, and cloud models based on sensitivity, complexity, latency, cost, and policy.

Local AI → enterprise AI → cloud AI

Agentic workflow

For multi-step work where tool access, human approval, action records, and error containment must be explicit.

User → agent → tools → approval → action

The working canvas

One real workload. One defensible first decision.

Participants carry one workload through the day, recording evidence, assumptions, open questions, and the conditions that must be true before deployment.

Ask about the workshop canvas

01OutcomeUser · task · measure

02CharacterizeData · consequence · latency

03PlaceDevice · edge · cloud · hybrid

04ControlEvidence · review · constraints

05ActBarrier · owner · next action

Designed for live delivery

Start with one workload. Build the capability to scale.

The workshop is delivered in person or virtually live and runs for one day. It is built for teams that need a shared architecture conversation tied to real work.

FormatIn person or virtual live

DurationOne day

AudienceTechnology, mission, data, AI, and transformation leaders

Request a workshop conversation