// Engagement models

Four ways to turn platform investment
into business outcomes.

Whether you're standing up an AI capability, scaling a stretched platform team, or building a credible business case for the board — every engagement is fixed-scope, senior-led, and accountable to outcomes you can measure.

01 / NEW
🤖

AI Infrastructure & LLMOps NEW

Move your AI initiatives from prototype to production without surprising the CFO. We design and deliver the production-grade infrastructure your AI roadmap depends on — model serving, gateways, RAG, cost controls — so engineering can ship customer-facing AI features predictably and securely.

LLMOpsModel GatewayRAG InfraAI AgentsMCP ServersGPU SchedulingPrompt Observability
Business outcomes
  • Production-grade model serving on infrastructure your team can run
  • Centralised model gateway with routing, governance & cost attribution
  • Self-hosted LLM option for GDPR-bound and regulated workloads
  • AI cost transparency: latency, throughput, and spend per team
  • RAG and vector infrastructure ready for your first production use case
Engagement: Standalone AI infra build · Add-on to IDP engagement
02 / Core
🏗️

Internal Developer Platform Build

Give your engineering organisation the leverage of a 20-person platform team — without hiring one. End-to-end design and delivery of a production-grade IDP with golden paths, self-service infrastructure, and AI-readiness from day one. Your investment translates directly into faster lead time to production and lower per-deploy cost.

BackstageArgoCD / FluxCrossplaneKubernetesGitOps
Business outcomes
  • Faster lead time to production — measurable in DORA metrics
  • Self-service infrastructure that removes the platform team from the critical path
  • One developer portal, one paved road, fewer support tickets
  • End-to-end observability for reliability and cost accountability
  • Full Infrastructure-as-Code in your repos — owned by you on day one
Engagement: Fixed-scope project · Time & materials · Milestone-based
03 / Advisory
⚙️

Cloud & Platform Advisory

Senior advisory for engineering leaders facing high-stakes decisions: cloud strategy, data centre exits, AI readiness, FinOps, and architecture under regulatory pressure. The output is a defensible plan you can take to the board — not a pile of slides.

Cloud StrategyArchitecture ReviewAI ReadinessFinOpsDC Migration
Business outcomes
  • Cloud strategy with clear vendor selection rationale and exit paths
  • Independent architecture review with written, defensible recommendations
  • AI readiness assessment with prioritised investment roadmap
  • FinOps review surfacing the cost levers that matter
  • Security & compliance posture with a remediation plan you can fund
Engagement: Fixed-scope advisory · Day-rate retainer · Architecture review sprints
04 / Training
🎓

Platform & AI Engineering Enablement

Reduce key-person risk and protect your platform investment. Hands-on enablement for your team across Kubernetes, Platform Engineering and AI infrastructure operations — tailored to the exact tools you run. The outcome is internal capability, not slide decks.

Platform Eng.KubernetesLLMOpsGitOpsAI Infra OpsBackstage
Capability tracks
  • Kubernetes fundamentals → advanced day-2 operations
  • GitOps delivery practices for engineering at scale
  • Platform Engineering principles & IDP operating model
  • LLMOps and AI infrastructure operations for your team
  • Observability engineering for reliability accountability
Engagement: Half-day / Full-day / Multi-day workshops · Custom curriculum
// What makes this different

Most platforms aren't ready
for the AI workloads coming next quarter.

❌ The typical retrofit approach

  • Platform built for yesterday's container workloads only
  • No GPU strategy — every AI project becomes a separate procurement
  • Observability stops at infrastructure metrics; AI cost is invisible
  • Model credentials managed ad-hoc, with audit risk
  • AI teams build shadow infrastructure outside governance
  • No way to attribute LLM spend to teams, products, or customers

✦ The BootupTek approach

  • Platform designed for both standard and AI workloads from day one
  • GPU capacity managed with the same controls as the rest of your fleet
  • End-to-end observability: latency, throughput, model performance, cost
  • Centrally managed model credentials with audit trail
  • AI golden paths inside your developer portal — governance by default
  • AI FinOps: spend attribution per team, per model, per service
// Next step

Not sure which engagement fits?

Start with a free 30-minute briefing. We'll map your situation to the right engagement model — no pitch, no obligation.

Book an executive briefing →