Cloud architecture, migration and data engineering — audit-grade, AI-ready, multi-cloud by default.

We’re cloud-agnostic and decide per workload, not per partner badge. In the two-week assessment we score each workload against AWS, GCP and Cloudflare on cost, latency, native services and your team’s skill profile, then recommend the right cloud for each system in writing. Often the answer is multi-cloud — for example, core compute and AI on AWS or GCP, with edge delivery, API protection and zero-trust security on Cloudflare.

Multi-cloud where it matters. Workloads are architected with provider-neutral patterns — containers, open standards, infrastructure-as-code — so the dependency on any single vendor stays thin. Critical capabilities (object storage, queues, identity, DNS) sit behind interfaces, not coded directly against vendor SDKs. If pricing changes or contracts go sideways, you can move a workload to AWS, GCP or Cloudflare without a six-month rewrite. We document the lock-in cost of every managed service up front so the trade-off is visible, not hidden in a wire diagram.

Three places, mainly: latency-sensitive user-facing traffic, API protection and zero-trust security, and global content delivery. Anything customers feel the speed of — auth, real-time UI, search, voice — benefits from edge. We typically run core compute, databases and AI on AWS or GCP, then push the user-facing layer to Cloudflare Workers, AI Gateway and edge KV. That cuts p95 latency by 60–80% for global users and shrinks the attack surface at the same time. For single-region traffic in one country, edge is usually overkill.

A landing zone is the pre-built foundation a cloud environment sits on — accounts, networks, IAM, encryption, KMS, audit trails, guardrails and compliance controls — set up once, before any workload moves. Yes, you need one. Without it every workload reinvents the wheel and security gaps compound silently. Ours ships in 4–6 weeks across AWS, GCP and Cloudflare with DPDP, SOC 2, ISO 27001 and PCI-DSS controls live from day one, and a single template the rest of your estate deploys on. Saves 3–6 months on every workload after the first.

Yes — designed in from day one when it’s the right shape. Some workloads stay on-prem (data sovereignty, latency, regulation), others move to AWS, GCP or Cloudflare. We connect both sides through Direct Connect, Cloud Interconnect or Cloudflare Tunnel, mirror IAM and policy across, and put unified observability over both. Common patterns: regulated data on-prem with cloud compute, on-prem residuals during multi-year migrations, or dual-region failover where one region stays on-prem. We don’t push cloud-only when on-prem is the better answer.

Yes — data engineering is one of our three cloud sub-services. We build production pipelines on Snowflake, Databricks, BigQuery, Airflow, Dagster or dbt — with lineage tracking, schema-drift handling, PII detection and redaction, and SLA monitoring from day one. Real-time and batch in the same pipeline graph. No “works in staging, breaks in production” — pipelines are engineered for production load before they ever hit it.

Yes — AI workloads are a growing chunk of our cloud work. We size GPU clusters (A100 / H100 / Trainium / TPU), set up vector DBs (Pinecone, pgvector, OpenSearch), wire in MLOps pipelines, and instrument training and serving costs for predictability. Models stay in-tenant — your AI never leaves your cloud. Often a workload starts on managed services (Bedrock, Vertex AI, Cloudflare Workers AI) and graduates to dedicated GPU once usage justifies it. Our gen-ai services team partners with cloud on this.

Cloud is where AI cost becomes controllable — managed services let you start cheap, GPU instances let you scale, and FinOps tooling lets you see every dollar. Inference on Bedrock, Vertex AI or Cloudflare Workers AI typically runs $0.001–$0.06 per 1K tokens, so a mid-sized production workload lands at $2K–$15K/month. GPU clusters (A100 / H100) for fine-tuning or training start at $3–$8/hr per GPU and can hit $40K–$120K/month at full utilization. We instrument training and inference cost from day one — per-feature attribution, batch right-sizing, spot / preemptible scheduling, and managed-to-dedicated upgrade paths only when usage justifies the move. Most clients cut their AI cloud bill 35–55% in the first quarter just from the cloud-side instrumentation.

Three layers, all wired in from day one. SLO-driven alerting that fires before users feel it — not just CPU and memory thresholds. Synthetic checks that run the actual user journey from outside the network every minute, in every region you serve. Distributed tracing so when something does slip, root-cause is minutes not hours. Customer-reported outages we count as a process failure — every one triggers a review and an instrumentation fix so it can’t happen the same way twice.