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Cloud Computing & AI Infrastructure

Prodhee Technologies partners with global enterprises and digital-native companies to design, migrate, and scale cloud environments that are resilient, secure, and AI-ready. Whether lifting legacy workloads, optimizing multi-cloud footprints, or infusing AI/MLOps practices, we deliver operational excellence without business disruption.

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Cloud Services

Our portfolio spans migration, operations, and AI-ready architectures—all hardened for scale, security, and compliance.

Cloud Migration & Modernization

Liberate workloads. Modernize legacy. Scale infinitely.

  • What we do: Assessment workshops, cloud landing zones, migration runbooks, refactoring monoliths into microservices, cost/performance tuning.
  • Use cases: Data-center exit, app containerization, hybrid-cloud consolidation, performance-hungry workloads.
  • Deliverables: Cloud strategy brief, migration factory plan, architecture blueprints, cost governance model, runbooks.
  • KPI Impact: Infra cost ↓ 25–40%, release velocity ↑ 2–3x, downtime risk ↓ drastically.

AI/MLOps (Machine Learning Operations)

Operationalize intelligence—safely, repeatably, and at scale.

  • What we do: Data/feature stores, CI/CD pipelines for models, automated retraining triggers, drift detection, experiment tracking.
  • Use cases: Continuous AI deployment, high-stakes ML in BFSI/Healthcare, recommendation engines, predictive analytics.
  • Deliverables: ML pipeline diagrams, experiment registry, monitoring dashboards, rollback playbooks.
  • KPI Impact: Model release cycles ↓ 60%, escaped defects ↓, cost per inference ↓ 30%.

Serverless Computing

Elastic scale. Zero ops. Pay only for execution.

  • What we do: Event-driven workloads, API gateways, micro-batching, GPU-backed serverless for AI, cold-start optimization.
  • Use cases: Event stream processing, IoT ingestion, backend APIs, AI inference pipelines.
  • Deliverables: Serverless blueprints, cost models, performance tuning playbooks, IaC templates.
  • KPI Impact: OpEx ↓ 40–70%, latency compliance ↑, DevOps overhead nearly eliminated.

Data Warehouse & Data Lake Solutions

Unify, govern, and query at petabyte scale.

  • What we do: Lakehouse architectures, data ingestion pipelines, schema harmonization, real-time analytics, governance frameworks.
  • Use cases: Enterprise analytics, customer 360, IoT data unification, regulatory reporting.
  • Deliverables: Data ingestion pipelines, governance catalog, query performance dashboards, consumption layers.
  • KPI Impact: Query latency ↓ 50–80%, analytics coverage ↑, compliance breaches ↓.

Cloud Security & Compliance

Guardrails, governance, and guaranteed peace of mind.

  • What we do: Zero-trust architecture, IAM hardening, SOC2/HIPAA/GDPR compliance, KMS/HSM integration, penetration tests, anomaly detection.
  • Use cases: BFSI/Healthcare-grade compliance, regulated workloads, sensitive PII/PHI, cross-border data residency.
  • Deliverables: Security playbooks, IAM policies, compliance audit reports, automated guardrails, threat dashboards.
  • KPI Impact: Audit readiness ↑, breach probability ↓, regulatory costs ↓.

Delivery Approach

We operationalize clarity through structured, testable execution.

Discovery & Readiness

Infra audit, risk registry, ROI model, migration vs. modernization decision.

Experimentation & Prototyping

PoCs with multi-cloud tools, bake-off benchmarks.

Pilot / MVP

Thin-slice workloads, instrumented for latency/cost/compliance.

Hardening & MLOps

IaC, CI/CD pipelines, failover strategies, disaster recovery, observability.

Security, Privacy & Governance

PII scrubbing, governance templates, audit logs.

Scale & Continuous Improvement

Cost tuning, auto-scaling policies, AI-driven workload placement.

Platforms & Tooling

Platforms & Tooling

We remain vendor-neutral, choosing the right weapon for each workload.

  • Cloud Providers: AWS, Azure, GCP, Oracle, Alibaba.
  • Data Warehousing: Snowflake, BigQuery, Redshift, Databricks Lakehouse.
  • Serverless & Containers: AWS Lambda, Azure Functions, GCP Cloud Run, Kubernetes, OpenShift.
  • MLOps: MLflow, Kubeflow, Airflow, Weights & Biases, SageMaker.
  • Security & Compliance: HashiCorp Vault, Prisma Cloud, Checkov, Open Policy Agent.
  • Observability: Prometheus, Grafana, Splunk, Datadog.
Security & Compliance
  • Data: Mask, encrypt, regional residency, immutable audit logs.
  • Access: SSO/SAML/OIDC, least privilege, role-based approvals.
  • Safety: DDoS protection, anomaly detection, threat intelligence feeds.
  • Compliance: SOC 2, ISO 27001, HIPAA, GDPR, PCI DSS.
  • Observability: Security dashboards, cost/latency tracking, compliance alerts.
The Team You Get

Roles: Cloud Architect, Infra Engineer, Data Engineer, Security Engineer, MLOps Engineer, DevOps, Delivery Manager, QA/SDET, Compliance Officer.

Working Model: Agile pods, sprint demos, cost telemetry, risk logs, built-in redundancy.

Visuals: Org diagram, sprint animation timeline.

Case Studies

FAQ

Can you handle multi-cloud + hybrid setups?

 Yes—AWS, Azure, GCP, on-prem, VMware—optimized for workload placement.

How do you ensure compliance?

Pre-built controls, audit logs, and certification-ready templates.

How do you optimize costs?

Rightsizing, auto-scaling, FinOps dashboards, reserved instances, spot optimization.

Can we run entirely in our VPC?

Yes—air-gapped, no egress, customer-managed keys.

How fast is value realized?

Most orgs see tangible ROI within 4–8 weeks post migration pilot.

Ready to scale your cloud and AI infrastructure?