A practical path from assessment to production.

Private AI fails when teams skip the hard questions: where data crosses boundaries, how retrieval quality is measured, how agents get permission to act, how workloads are operated, and who owns the system after launch. Our process turns those questions into decisions, artifacts, and implementation steps.

Detailed flow

Six steps from first review to operational handoff.

Each step clarifies one part of the path: risk, readiness, architecture, implementation, operations, or ongoing advisory support.

Clients can enter at any stage. We can assess a prototype, harden an existing platform, or scope implementation work that moves the system toward production readiness.

Planning

AI Infrastructure Review

A direct conversation about your AI goal, regulated constraints, data boundaries, current platform, and main production risk. We tell you honestly whether private AI infrastructure is the right problem to solve now.

30 min · No cost

Readiness Assessment

We map model hosting options, data movement, RAG architecture, agent permissions, Kubernetes readiness, observability, cost exposure, and Day-2 operations before implementation begins.

1–2 weeks

Architecture Blueprint

We define the platform shape, service boundaries, deployment model, permission model, and ownership path. Your team walks away with ADRs, diagrams, tradeoffs, and a scoped implementation sequence.

1–2 weeks

Execution

Implementation Support

We build or guide the critical infrastructure, APIs, RAG pipeline, agent controls, deployment automation, observability, and runbooks needed before the system can be operated with confidence.

Scoped

Operational Handoff

We document how the system is secured, monitored, debugged, upgraded, and owned, including alert ownership, failure modes, deployment expectations, and cost visibility.

Included

Advisory Support

Focused optimization and retained engineering review where the platform needs refinement after production exposure. Scoped to hardening, scaling, cost controls, agent workflows, and model-hosting changes.

Optional

Artifacts

What your team can leave with.

Private AI risk memo with go / no-go recommendation and prioritized risks
Data-flow and trust-boundary diagrams showing where prompts, documents, embeddings, responses, logs, and metadata can live
Architecture Decision Records (ADRs) documenting model hosting, deployment topology, access boundaries, agent permissions, and operational ownership
Detailed architecture diagrams for RAG, model serving, agent workflows, Kubernetes platform components, and integration points
Implementation sequence with 30/60/90-day milestones, dependencies, owners, and decision checkpoints
Deployment documentation covering environments, configuration, secrets, rollback, observability, and release expectations
Runbooks for monitoring, incident response, upgrades, cost review, access changes, and Day-2 operations
Advisory recommendations for remaining risks, future hardening, and platform evolution

Next Step

Need to know if private AI is realistic for your team?

Book a 30-minute AI Infrastructure Review. We'll map your data boundaries, platform risks, agent controls, and operating model before you commit to a build path.

Book an AI Infrastructure Review View services