AI Solutions

AI Model Deployment

Putting the model into daily use safely, reversibly, and where your data governance allows.

How We Work, Step by Step
  1. 1Package and version
  2. 2Shadow test
  3. 3Staged release
  4. 4Guardrails live
  5. 5Full rollout with rollback ready

What We Do for You

  • Package the model as a versioned, reversible release.
  • Roll out in stages with a defined stop condition.
  • Put guardrails and approval gates at the boundary.
  • Set latency and cost budgets as requirements.
  • Write the runbook your operators will use.

How this is bought: Bought as a defined project: fixed scope, agreed milestones, handover and training. Build an estimate for your case.

Our Approaches Explained

Serving architecture

Real-time API, batch scoring, or embedded - chosen by how the answer is actually used.

Containerised, versioned releases

Each model shipped as a versioned artefact that can be rolled back in minutes.

Staged rollout

Shadow mode, then a small share of traffic, then full release - with a defined stop condition.

Access control and rate limiting

Only permitted systems and users may call the model, within agreed limits.

Data residency and privacy at inference

Where the request is processed, what is retained, and what is never sent.

Guardrails at the boundary

Input filtering, output checks and human approval on consequential actions.

Latency and cost budgets

Response time and per-call cost set as requirements, not discovered afterwards.

Fallback behaviour

What the system does when the model is unavailable or unsure - it must degrade, not break.

The Standards We Work To

Containerised serving and CI/CDBlue-green and canary release patternsAPI gateways and rate limitingNIST AI RMF manage function

We follow the structure and controls these standards describe. We do not claim to be certified against them - where you need a formal certificate, we prepare the evidence and an accredited body performs the audit.

What You Get

  • Deployment architecture
  • Release and rollback procedure
  • Guardrail configuration
  • Latency and cost budget
  • Runbook for operators
Where We Usually Focus
Releases with tested rollback97%
Consequential actions gated by approval100%
Deployments meeting latency budget85%

These are the areas clients most often ask us to improve. Your project sets its own targets, measured and agreed with you.

Ask AI what ARRIX does for AI Model Deployment - ARRIX

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