Flagship product family

Enterprise & OpenShift AI Assurance Platform

Take private AI from a promising pilot to a governed, observable, cost-controlled production service.

A governance, reliability and cost-control layer for organisations running AI on Red Hat OpenShift AI, Kubernetes or on-prem GPUs. It assesses production readiness, inventories models and serving, enforces entitlements and quotas, wires up evaluation and observability, attributes cost, and gates releases — so regulated enterprises can run private AI they can prove is safe, reliable and affordable.

Product concept

The Enterprise & OpenShift AI Assurance Platform is the productised form of what a senior platform/SRE engineer does when an organisation moves AI from experiment to production. It is aimed at banks, insurers, telcos, public bodies and manufacturers that must keep data in-house and run AI on Red Hat OpenShift AI, plain Kubernetes or on-prem GPUs.

It is not a model and not a chatbot. It is the assurance layer around models: does this AI service meet production, security and cost bars before it ships; which models and endpoints exist and who may call them; is it evaluated and observable; what does it cost per team; and can a release, an upgrade or a change be made without breaking the platform or the audit trail.

The unifying insight is that the hard part of enterprise AI is not building a model — it is operating it under regulation, reliability and budget constraints. That is exactly the gap most AI vendors ignore and exactly where deep platform, OpenShift and SRE experience is scarce and valuable. This family turns that scarce expertise into repeatable assessments, governance packs and managed assurance.

Customer problem

Today a platform team is told to “put the AI use-case into production on OpenShift”. There is no agreed definition of production-ready for an AI service, so readiness is argued case by case and ships late.

Nobody has a clean inventory of which models are served, on which GPUs, by whom and at what version — so security, capacity and cost questions cannot be answered quickly.

GPU spend is large and opaque. There is no per-team attribution, no quota enforcement, and no easy way to see idle or oversized allocations, so finance and platform argue about a bill nobody can break down.

Evaluation and observability for AI are ad-hoc. When an AI service degrades or a prompt regression ships, there is little evidence of what changed, and incident write-ups are reconstructed by hand.

Upgrades and changes to the platform are high-anxiety events because the blast radius on AI workloads is unclear. The economic consequence is slow delivery, unmanaged cost, and compliance risk on the very systems the business is betting on.

Before and after

5Today

1. No agreed definition of AI production-ready
2. No clean inventory of models, endpoints or GPUs
3. Opaque GPU bill with no per-team attribution
4. Ad-hoc evaluation; incidents reconstructed by hand
5. Upgrades are high-anxiety, unclear blast radius

AI in production without assurance — slow and risky.

5With the platform

1. A shared, evidenced readiness rubric and release gate
2. A live model/serving/GPU inventory
3. Per-team cost attribution with enforced quotas
4. Evaluation, SLOs and evidenced incident write-ups
5. Change-risk and upgrade intelligence before you act

Same platform team — governed, observable, affordable AI.

	Today	With the platform
Production readinessillustrative	argued case by case	evidenced rubric
GPU costillustrative	opaque bill	attributed per team
Upgrade blast radiusillustrative	discovered in prod	predicted before

Text alternative (accessible description)

1. Today: No agreed definition of AI production-ready → No clean inventory of models, endpoints or GPUs → Opaque GPU bill with no per-team attribution → Ad-hoc evaluation; incidents reconstructed by hand → Upgrades are high-anxiety, unclear blast radius
2. With the platform: A shared, evidenced readiness rubric and release gate → A live model/serving/GPU inventory → Per-team cost attribution with enforced quotas → Evaluation, SLOs and evidenced incident write-ups → Change-risk and upgrade intelligence before you act

How it fits together

1 / 6

Business systemExternal serviceData storeAIBusiness rulesStaff

Stage 1: Collect the facts — Cluster, serving and GPU state are gathered.

Text alternative (accessible description)

1. Collect the facts — Cluster, serving and GPU state are gathered.
2. Inventory & signals — Models, versions and eval/SLO signals are structured.
3. Analyse risk & cost — AI summarises findings against the rubric.
4. Gate the release — An explicit rubric passes or fails the workload.
5. Human signs off — A platform engineer decides; the record is kept.
6. Govern cost & change — Quotas attribute cost; change risk is flagged.

End-to-end workflow

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1. Scheduled OpenShift upgradeBusiness system

   Platform + GPU-operator bump together.

2. Correlate versionsAI

   Upgrade intelligence maps the AI stack.

3. Detect incompatibilityAI

   Operator vs serving runtime conflict.

4. Raise change riskBusiness rules

   Blast radius touches production inference.

5. Recommend sequencingAI

   Pin runtime, then upgrade — evidence shown.

6. Platform lead decidesStaff

   Human makes the final call.

7. Safe upgrade appliedBusiness system

   Incident avoided; endpoints healthy.

8. Audit record keptData store

   Decision and evidence stored.

Business systemAIBusiness rulesStaffData store

Step 1: Scheduled OpenShift upgrade — Platform + GPU-operator bump together.

Text alternative (accessible description)

1. Scheduled OpenShift upgrade — Platform + GPU-operator bump together.
2. Correlate versions — Upgrade intelligence maps the AI stack.
3. Detect incompatibility — Operator vs serving runtime conflict.
4. Raise change risk — Blast radius touches production inference.
5. Recommend sequencing — Pin runtime, then upgrade — evidence shown.
6. Platform lead decides — Human makes the final call.
7. Safe upgrade applied — Incident avoided; endpoints healthy.
8. Audit record kept — Decision and evidence stored.

Detailed real-world examples

Happy path

A readiness assessment turns “ship it and hope” into an evidenced go/no-go

A DACH insurer standing up its first customer-facing AI assistant on OpenShift AI. — The AI team requests production sign-off two weeks before a board demo.

1. The readiness scanner inspects the namespace: model-serving config, resource requests/limits, autoscaling, network policy, secrets handling and data-flow boundaries.
2. It builds a model inventory — which models and versions are served, on which GPUs, behind which endpoints, callable by whom.
3. It runs the evaluation pack against a held-out set and checks that observability (latency, error rate, token cost, drift signals) is actually wired.
4. It scores readiness against an agreed rubric and lists concrete gaps: missing quota, no rollback path, an over-permissioned service account.
5. A human platform engineer reviews the report — the tool evidences and recommends, it does not self-certify.
6. The gaps are fixed and the release gate flips to green with an auditable record attached to the deployment.

Outcome: The insurer ships with a documented, evidenced production bar instead of an argument — and the same rubric now applies to every future AI service.

Exception path

A change-risk signal blocks a risky upgrade before it breaks inference

The same insurer, three months later. — A routine OpenShift upgrade and a GPU-operator bump are scheduled together.

1. Upgrade intelligence correlates the planned versions with the running model-serving stack and GPU drivers.
2. It flags a known incompatibility between the new operator and the current serving runtime that would break inference for two model endpoints.
3. Because the blast radius touches production AI, the change-risk assistant raises the risk level and recommends sequencing the upgrade and pinning the runtime first.
4. It does not auto-block or auto-apply — it presents the evidence and the recommended order to the platform lead.
5. The team reschedules, pins the runtime, and upgrades safely; the incident that would have happened is avoided.
6. The decision and its evidence are recorded for the change-advisory audit trail.

Outcome: A high-anxiety upgrade becomes a sequenced, evidenced change, and a likely production incident on regulated AI is prevented — with humans making the final call.

Different market

Cost attribution ends the GPU-bill argument at a telco

A telco running shared on-prem GPUs across five product teams on OpenShift. — Finance escalates a GPU bill that no one can break down by team.

1. The usage-chargeback extension attributes GPU-hours, memory and token throughput to namespaces and teams.
2. The resource-economics assistant highlights idle reservations and oversized requests against actual utilisation.
3. Quotas and entitlements are proposed per team so the shared cluster stops being a free-for-all — enforced deterministically, not by email.
4. A platform engineer reviews the proposed quotas and the reclaim list before anything is enforced.
5. Right-sizing and quota enforcement recover a meaningful slice of capacity without buying new GPUs.
6. Each team now sees its own AI cost, and finance gets a defensible monthly breakdown.

Outcome: An opaque, contested GPU bill becomes a per-team, evidence-based cost model with enforced quotas — recovering capacity and ending the monthly argument.

Who is responsible at each step

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Platform / clusterAI analysisRubric & quotasPlatform engineerAudit & cost

Step 1 — Platform / cluster: Request production sign-off — New AI workload.

Text alternative (accessible description)

1. Platform / cluster: Request production sign-off — New AI workload.
2. Platform / cluster: Scan config & inventory — Serving, RBAC, resources.
3. AI analysis: Summarise findings — Plain-language gaps.
4. Rubric & quotas: Score against rubric — Explicit go/no-go.
5. Platform engineer: Review the report — Engineer verifies.
6. Platform / cluster: Fix gaps — Quota, rollback, RBAC.
7. Platform engineer: Sign the gate — Human decides.
8. Audit & cost: Record evidence — Attached to deploy.
9. Platform / cluster: Ship to production — Evidenced release.

Product modules

Readiness assessment & scannermvp

Inspects an AI workload against a production/security/cost rubric and produces an evidenced go/no-go with concrete gaps.

Model & serving inventorycore

A live inventory of models, versions, endpoints, GPUs and who may call them — the basis for security and capacity answers.

Model-serving governancecore

Policy for how models are served, versioned and exposed, with entitlement control over endpoints.

Entitlement & quota controlcore

Per-team quotas and entitlements on GPUs and endpoints, enforced deterministically.

Evaluation pipelinecore

Repeatable evaluation of models and prompts against held-out sets, wired into CI so regressions are caught before release.

SLO & observability packcore

Latency, error-rate, token-cost and drift signals as first-class SLOs for AI services.

Cost attribution & chargebackcore

Attributes GPU and token cost to teams and highlights idle or oversized allocations.

Release gatesextension

Automated production-readiness gates in the delivery pipeline, with an auditable record per deployment.

Incident evidence collectorextension

Gathers the state, versions and signals around an AI incident so write-ups are evidenced, not reconstructed.

Change-risk & upgrade intelligenceextension

Correlates platform upgrades with the AI stack to flag blast radius and recommend safe sequencing.

Inputs and integrations

• OpenShift / Kubernetes API and workload configuration
• Model-serving configuration (KServe / vLLM / runtimes)
• GPU operator, node and utilisation metrics
• Prometheus/observability data and logs
• IAM, RBAC and service-account definitions
• GitOps / CI-CD pipeline definitions
• Evaluation datasets and prompt/version history

Users and buyer

Daily user

Platform and SRE engineers who run assessments, watch dashboards and act on release gates and change-risk signals.

Process owner

The platform lead or head of AI enablement who owns the production, governance and cost standards.

Economic buyer

The head of platform/infrastructure or CTO who carries GPU cost, delivery speed and audit risk.

Technical administrator

The platform team itself; the product integrates with their OpenShift, IAM, GitOps and observability rather than replacing them.

Final decision-maker

CTO / head of platform, often with security, compliance and finance as co-signers on a longer, higher-value sales cycle.

AI capabilities

• Summarising configuration and risk into plain-language findings
• Correlating upgrade/version data to predict compatibility blast radius
• Classifying and clustering incidents and their likely causes
• Detecting drift and anomalies in AI-service signals
• Drafting readiness reports, runbooks and change recommendations
• Explaining cost and utilisation patterns to non-experts

Deterministic capabilities

• The cluster state, inventory and metrics are authoritative facts, not inferred
• Quotas, entitlements and RBAC are enforced deterministically, never by suggestion alone
• Release gates pass or fail against an explicit, versioned rubric
• Cost attribution is computed from real usage records
• Every gate decision and change carries an immutable audit record

Object lifecycle

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State 1: Submitted — Workload enters assurance.

Text alternative (accessible description)

1. 1. Submitted — Workload enters assurance. (→ scan)
2. 2. Scanned — State and inventory collected. (→ assess)
3. 3. Assessed — Risk, cost and gaps summarised. (→ review)
4. 4. In review — Engineer verifies the evidence. (→ gate)
5. 5. Gated — Pass/fail against the rubric. (→ approve)
6. 6. Approved — Human sign-off recorded. (→ ship)
7. 7. In production — Observed, costed, governed.

Human responsibilities

• A platform engineer reviews and signs every readiness go/no-go — the tool evidences, it does not self-certify.
• Quota reclaims and enforcement are approved by a human before they take effect.
• Upgrade sequencing and change risk are decided by the platform lead, not auto-applied.
• Compliance and security owners remain accountable for regulatory sign-off.

Economic value

• Faster, more predictable path to production for AI services via a shared, evidenced readiness bar.
• Recovered GPU capacity and lower spend through attribution, right-sizing and quotas.
• Fewer and shorter AI incidents because evaluation, SLOs and change-risk catch problems early.
• Lower audit and compliance risk from a consistent, evidenced governance trail.
• Premium, credible positioning: this is scarce platform/SRE expertise productised, with strong retainer and managed-service potential.

No market statistics or financial promises are implied. Any figures in the visuals above are illustrative examples, not measured results.

Risks, limitations and failure cases

• Over-claiming “certified compliant” is dangerous — the platform evidences readiness; humans and auditors certify.
• A false green on a readiness gate is the worst failure mode; gates are explicit, versioned and human-signed.
• Deep access to a regulated cluster demands least-privilege, on-prem operation and careful data handling.
• Enterprise sales cycles are long and multi-stakeholder; the wedge must be a fast, cheap, high-value assessment first.
• Red Hat / OpenShift AI evolve quickly — the rubric and upgrade intelligence must be maintained against current releases, not a snapshot.

Product evolution

Smallest credible first version

A fixed-scope, paid AI production-and-cost readiness assessment: a scan, an evidenced report and a prioritised gap list for one AI workload.

Professional product

A governance and assurance pack — inventory, evaluation, SLOs, quotas, cost attribution and release gates — installed into the customer’s OpenShift AI platform.

Optional extensions

• Incident evidence collector
• Upgrade & change-risk intelligence
• Virtualization-readiness assistant
• Kubernetes-to-OpenShift migration assessment

Long-term platform

A managed AI-assurance service across multiple enterprises and, via partners, regulated markets in the EU and Gulf — recurring revenue on top of scarce platform expertise.

Shared guidance

Generally applicable method — how to validate, pilot, price and keep humans accountable — lives in the shared playbook so these pages stay specific:

• Regulated-industry boundaries
• Evaluation & evidence
• Human approval & accountability
• Product integration strategy
• Measuring economic value