Architecture
Models, agents, APIs, data paths and the dependencies between them.
Production AI Readiness Assessment
Your AI demo works.
Now find out what will break in production.
Get an independent assessment of the architecture, platform and operating decisions between your AI prototype and a system your organization can reliably operate, secure, govern and scale.
30-minute fit call · No sales deck · A technical conversation
Former Red Hat EngineerDocker Captain8 Technical Books40K+ StudentsKubeCon SpeakerRed Hat Summit
The production gap
The prototype answered one question.
Production introduces twenty more.
Connecting an application to a model is increasingly easy. Operating it reliably across teams, workloads and organizational controls is not.
The demo
- Model responds
- Prompt works
- Team sees value
- Executives see potential
Production
IdentityReliabilityGPU capacityObservabilityCostSecurityGovernanceAuditability
The assessment identifies which questions your current architecture can answer—and which ones are becoming production risk.
A complete diagnostic
What the assessment examines
Eight connected layers. One production system. Each area is reviewed against your real operating context—not a generic maturity checklist.
Compute
GPU capacity, utilization, scheduling, tenancy and unit economics.
Reliability
Failure modes, scaling, recovery objectives and production SLOs.
Observability
Logs, traces, model telemetry, evaluation signals and ownership.
Security
Identity, access, secrets, data exposure and supply-chain controls.
Governance
Policies, approvals, audit evidence and clear accountability.
Cost
Tokens, inference, accelerators, idle capacity and cost allocation.
Platform
Developer paths, automation, shared services and operating boundaries.
The Production AI Framework
From prototype to proof.
A repeatable way to evaluate the complete operating system around AI—not only whether the application works today.
Prototype
Validate the use case and economics before the demo becomes architecture by accident.
Platform
Standardize infrastructure, access, security, data paths and developer workflows.
Production
Engineer for reliability, observability, performance, cost and recovery.
Proof
Make governance visible with controls, logs, runbooks and verifiable evidence.
Your deliverables
You leave with decisions, not another strategy deck.
01Executive findings summary
02Production readiness scorecard
03Architecture risk map
04Prioritized technical gaps
05Security and governance observations
06Cost and capacity considerations
0730 / 60 / 90-day action roadmap
08Leadership readout
Production AI Readiness
Assessment
Executive readiness
ArchitectureHigh priority
ObservabilityMedium
GovernanceHigh priority
Cost controlsMedium
Top actions
- 01 · Establish production ownership and SLOs
- 02 · Close identity and data-path controls
- 03 · Instrument cost per workload
A focused engagement
From first conversation to production priorities.
Before
Fit call, architecture questionnaire, relevant diagrams and business context.
Assessment
Stakeholder interviews, architecture review and Production AI analysis.
After
Findings, prioritized roadmap, leadership readout and next-step recommendations.
Executive outcomes
Know what to fix before scale makes it expensive.
Production priorities
Know which architectural gaps matter now and which can wait.
Platform decisions
Identify what belongs in shared capability instead of project-by-project engineering.
Operational risk
Surface reliability and observability weaknesses before real users depend on the system.
Cost visibility
See where GPU, model and inference economics need stronger controls.
Governance evidence
Connect technical controls to the proof security, risk and audit teams require.
Leadership alignment
Create a common definition of production ready across executives and engineering.
Why Luca
Engineering depth.
Executive translation.
Luca Berton has spent 15+ years across enterprise infrastructure, automation, Kubernetes, cloud and AI platforms. He focuses on the operational layer most AI conversations skip: what it takes to run AI safely, repeatedly and economically in production.
Former Red Hat engineerDocker Captain8 technical books40K+ studentsKubeCon speakerRed Hat Summit speaker
“The board sees an AI demo. The platform team sees everything the demo forgot.”
This is for you if…
- ✓You have working AI pilots
- ✓Production is approaching
- ✓Multiple teams are involved
- ✓Platform decisions are becoming strategic
- ✓Security or governance has entered the conversation
Probably not if…
- ×You are still looking for your first AI use case
- ×You only need prompt engineering
- ×You want a generic AI strategy presentation
- ×You want a specific vendor sales pitch
The engagement
Production AI Readiness Assessment
An independent review of the architecture, platform and operating model behind your production AI initiative.
✓Discovery and technical intake
✓Architecture review
✓Production readiness assessment
✓Risk and gap analysis
✓Prioritized recommendations
✓30 / 60 / 90-day roadmap
✓Leadership readout
✓Clear next-step options
Book an assessment call →Start with a 30-minute fit conversation.
Questions before you book
Frequently asked questions
What exactly will you review?+
The assessment follows the system from model and application architecture through compute, data paths, developer workflows, reliability, observability, security, governance and cost. The scope is agreed during the fit call so the review stays focused on the decisions that matter to your production path.
Do we need to be running Kubernetes?+
No. Kubernetes may be part of the environment, but the assessment is platform- and deployment-model independent. Luca can review cloud-managed services, virtual-machine platforms and hybrid architectures as well as Kubernetes-based systems.
Are you tied to a particular cloud or vendor?+
No. The goal is an independent view of your production risks and priorities, not a vendor sales recommendation. Existing technology choices are evaluated against your operating needs, constraints and economics.
How much access will you need?+
The review can usually be completed through architecture material, configuration evidence and structured conversations with the relevant stakeholders. Direct production access is not the default and is only discussed if it is genuinely useful and acceptable to your security team.
Who should participate?+
Usually the technical owner of the AI initiative plus representatives from platform or infrastructure, application engineering, security and governance. Executive sponsorship is helpful for the opening context and final readout.
How long does the assessment take?+
Timing depends on scope and stakeholder availability. The first 30-minute conversation confirms fit, boundaries and a realistic schedule before any work begins.
What does the final deliverable look like?+
You receive a concise executive summary, a production-readiness scorecard, a risk and gap map, prioritized recommendations and a 30 / 60 / 90-day roadmap, followed by a leadership readout.
Can Luca help implement the recommendations?+
Yes, when it is a good fit. Implementation or advisory support is scoped separately after the assessment, so the diagnostic remains independent and useful even if your own team completes the work.
We are still in pilot. Is it too early?+
Not necessarily. The best time is often when the use case is proven but major platform decisions are still reversible. If you are still searching for a first AI use case, this assessment is probably premature.
Your prototype already proved something.
Now prove it can survive production.
Identify the architecture, platform and operating gaps before they become production constraints.
Book your assessment →30-minute initial conversation