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Solutions · Cloud + AI platform

Supercharge cloud adoption, automation & ops

One governed control plane—provision infrastructure, run day-2 operations, manage FinOps, and scale AI workloads on your cloud, your residency rules, and your policies.

Cloud + AI as one platform, brick by brick.

ProvisioningDay-2 operationsFinOpsAI-native workloads
Plan a platform engagementCloud operations

Unified control plane

User portal & APIs

provision · operate · report cost

Policy & governance

RBAC · quotas · approvals

Observability & FinOps

SLOs · budgets · attribution

AI & data services

pipelines · models · events

Infrastructure

compute · network · storage · GPU

Platform lifecycle

ProvisionOperateObserveOptimizeScale AI

The gap

Infrastructure sprawl without a platform story

Teams want hyperscaler-grade self-service and FinOps—but on private, hybrid, or dedicated cloud with AI-native services baked in. Generic distributions rarely ship provisioning, operations, and cost management as one product your users can actually run.

Provisioning, ops, and FinOps live in separate tools—with no shared view of environments or spend

Hard to replicate cloud-native ergonomics (IaC, GitOps, self-service) on private or hybrid estates

AI workloads need GPU scheduling, model lifecycle, and cost attribution beyond classic IaaS

Event-driven systems glued together with scripts instead of first-class platform services

Compliance and residency requirements block lift-and-shift from public-cloud PaaS defaults

Finance and engineering lack a single source of truth for cloud and AI consumption

Control-plane capabilities

Provisioning · Operations · Cost management

One interface for platform engineers, SREs, FinOps, and product teams.

Provisioning

Self-service infrastructure, policy-bound from day one

Teams request environments, compute, data zones, and AI capacity through a unified platform—not ad-hoc tickets. IaC, GitOps, and guardrails encode security, tenancy, and approvals.

Ship new capacity in minutes, not weeks—without bypassing governance.

Capabilities

  • Environment templates & golden paths for dev, staging, and production
  • IaC + GitOps pipelines with policy checks before apply
  • GPU pools, model endpoints, and data lake zones on demand
  • Multi-tenant RBAC, quotas, and network isolation as code
  • Event-driven primitives—pub/sub, queues, serverless-style functions
Operations

Day-2 ops with observability, automation, and AI-assisted runbooks

Run your cloud and AI estate from one operations model: SLOs, incident hooks, patching, capacity planning, and agent-assisted remediation wired to your change-management process.

Less firefighting; clearer ownership and faster, safer changes.

Capabilities

  • Unified observability across compute, data pipelines, and inference
  • Automated patching, drift detection, and safe rollout patterns
  • Incident workflows with blast-radius visibility and rollback paths
  • Agent-assisted runbooks for recurring ops toil and triage
  • Change windows, approvals, and audit trails aligned to your org
Cost management

FinOps built into the platform—not bolted on after the bill arrives

See spend by team, environment, workload, and AI model. Right-size idle resources, enforce budgets, and attribute GPU and inference costs across platform and product teams.

Predictable cloud + AI spend with visibility from request to invoice.

Capabilities

  • Showback / chargeback by tenant, project, and service line
  • Budget alerts, anomaly detection, and commitment planning
  • Right-sizing recommendations for compute, storage, and GPU pools
  • AI workload cost attribution—tokens, inference hours, training jobs
  • Cost gates in provisioning: approve before expensive capacity lands
AI-native platform layers

Cloud infrastructure built for AI from the ground up

Data pipelines, model serving, and async services share the same tenancy, observability, and cost model—AI is a first-class citizen of your platform, not a sidecar on generic Kubernetes.

Data engineering on your cloud

Ingestion, transformation, and governance across batch and streaming—with lineage, quality gates, and cost tags on every pipeline run.

Lakehouse opsLineageTenant isolation

GenAI & model serving

GPU scheduling, model registries, inference routers, and guardrailed APIs so LLM workloads run next to your data with quotas and audit trails.

Model registryPrivate inferenceSafe rollout

Event-driven foundation

Managed-style pub/sub, durable queues, dead-letter handling, and function-style execution—the async patterns you know from hyperscaler PaaS, on your stack.

Pub/subQueuesFunctions

Platform intelligence

Agents that assist with provisioning requests, ops triage, and cost optimization—grounded in your policies, telemetry, and FinOps rules.

Ops agentsCost insightsPolicy-aware

Full lifecycle

One platform from first request to optimized production

ProvisionOperateObserveOptimizeScale AI

How we deliver

From blueprint to running platform

Five phases aligned to your compliance boundary, toolchain, and roadmap—whether you standardize one private region or federate hybrid estates.

Phase 01Weeks 1–4

Assess & blueprint

  • Estate inventory
  • Provisioning & ops maturity
  • FinOps baseline
Phase 02Weeks 3–8

Platform foundation

  • Control plane & RBAC
  • IaC / GitOps pipelines
  • Observability stack
Phase 03Weeks 6–12

Self-service provisioning

  • Environment catalog
  • Policy-as-code gates
  • GPU & data zone templates
Phase 04Parallel rollout

Unified operations

  • SLOs & runbooks
  • Incident automation
  • Agent-assisted triage
Phase 05From go-live

Cost intelligence

  • Tagging & attribution
  • Budgets & alerts
  • Continuous optimization
Cloud + AI platform

Design your control plane with us

Bring provisioning, operations, and cost management under one platform—so your teams run cloud + AI with confidence, not spreadsheets and siloed consoles.

Talk to an architectPlatform reference