Cloud and data platforms that stay affordable at scale
Architecture, migration, lakehouse engineering and DevOps on Azure, AWS, Google Cloud and Databricks — with cost discipline and observability built in rather than retrofitted after the first alarming invoice.
The platform underneath everything else
AI and applications are only as good as the platform they run on. Most of the problems we are called in to fix are not model problems — they are pipelines nobody documented, a cloud bill growing 15% a quarter with no owner, or a data warehouse that three teams have quietly stopped trusting.
We work across Azure, AWS and Google Cloud without a house allegiance, and we implement lakehouse architectures on Databricks and Microsoft Fabric. Engagements typically begin with an assessment: current architecture, cost breakdown, security posture, data lineage and the gap between them and where you need to be. You get a prioritised roadmap with effort and saving estimates against each item.
Cost is treated as a first-class engineering constraint. Cluster policies, autoscaling, storage tiering, reserved capacity, query optimisation and tagging discipline are part of the build. Several clients have seen a mature Databricks or Azure estate drop by a third without losing a single workload — because the savings were in configuration, not in doing less.
What we deliver
From landing zone to lakehouse to production ML.
Cloud Architecture & Migration
Landing zones, network and identity design, workload migration and modernisation on Azure, AWS and Google Cloud — including in-region residency for UAE and Saudi requirements.
Databricks & Lakehouse Engineering
Medallion architecture, Delta Lake, Unity Catalog governance, streaming and batch pipelines, and the cluster policies that keep the bill predictable.
Data Platform & Analytics
Microsoft Fabric, Snowflake and warehouse modelling, dbt transformations, semantic layers and Power BI reporting your executives will actually rely on.
DevOps, SRE & Platform Engineering
Infrastructure-as-code with Terraform, Kubernetes platforms, CI/CD pipelines, observability with Grafana and Prometheus, and incident response you can actually run.
MLOps & AI Platform
Feature stores, model registries, automated retraining, evaluation gates and deployment pipelines on Azure ML, SageMaker, Vertex AI or MLflow.
Cloud Security & Cost Optimisation
Security posture reviews, identity hardening, network segmentation, plus FinOps practice — tagging, budgets, rightsizing and reserved capacity planning.
Typical engagements
What clients usually ask us to do first.
- Data centre to cloud migration with minimal downtime
- Databricks lakehouse implementation from scratch
- Cloud cost audit and optimisation programme
- Legacy data warehouse migration to Fabric or Databricks
- Real-time streaming pipelines from IoT or transactional sources
- Unity Catalog governance and data lineage rollout
- Kubernetes platform build with GitOps deployment
- MLOps pipeline for models already built but not deployed
- Multi-region architecture for data residency compliance
- Disaster recovery design and tested failover procedures
How we work on platforms
Four commitments we hold to on every engagement in this practice — and that you can hold us to.
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01
Assessment before proposal
We start by measuring what you have — cost, architecture, lineage, security — so the roadmap is grounded in evidence rather than assumption.
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02
Cloud-agnostic advice
We hold partnerships and certifications across Azure, AWS and Google Cloud, so the recommendation follows your constraints rather than our incentives.
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03
Cost as an engineering constraint
Budgets, tagging, cluster policies and rightsizing are part of the build. We report spend against forecast every month.
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04
Your team leaves capable
Documentation, runbooks and pairing sessions are part of delivery. The goal is that your engineers can operate the platform without us.
A delivery model that de-risks the unknown
Fixed-scope discovery, then iterative delivery with working software in your hands every two weeks.
Discovery & Framing
Two to three weeks. We map the process, quantify the opportunity, test data readiness and return a costed architecture — yours to keep either way.
Architecture & Design
Solution architecture, security model, data flows, integration contracts and UX design, reviewed with your technical and compliance stakeholders.
Senior-Led Build
Two-week sprints, demoable increments, automated tests and CI/CD from sprint one. You see progress in a real environment, not a slide.
Validation & Hardening
Model evaluation, load and penetration testing, guardrail tuning, UAT with your users and a documented go-live runbook.
Launch, Support & Evolve
Managed go-live, 24/7 monitoring, cost optimisation and a quarterly roadmap so the platform keeps compounding value.
The stack we build production systems on
Chosen for longevity and total cost of ownership — never for novelty.
Model choice is a routing decision, not a loyalty test. Frontier models sit behind an abstraction so a cheaper — or in-region — model can take over a step without a rewrite, and every call is logged with its prompt version, latency and cost.
Infrastructure is code from the first week — no console clicks nobody can reproduce. Terraform describes the estate, containers carry the workloads, and the pipeline that deploys to production is the same one every engineer runs locally.
A lakehouse only earns its keep with governance. Bronze, silver and gold layers with contracts on the interfaces, tests on every transform, and lineage you can put in front of an auditor without a week of preparation.
.NET and TypeScript carry most of what we ship. Both have published support horizons, deep hiring pools across the Gulf and India, and no licensing surprise waiting three years out. Python joins them wherever the work is closer to the model than to the browser.
One codebase where the product allows it, native where the hardware demands it. Right-to-left layout, offline-first sync and store compliance are designed in at the start — each one is expensive to retrofit and all three are non-negotiable for a Gulf launch.
The value is rarely in the model — it is in the writeback. We connect to the ERP, CRM and ticketing systems the business already runs, with idempotent handlers, replay on failure and a dead-letter queue somebody actually watches.
Where we deliver
A Gulf-headquartered team with an India delivery centre — overlapping working hours with the Middle East, Europe and North America.
United Arab Emirates
Our regional headquarters in Dubai serves banking, healthcare, logistics and government-linked entities, with UAE PDPL-aligned delivery and Azure UAE North residency.
Saudi Arabia
AI and automation programmes supporting Vision 2030 mandates — Arabic-first systems, SDAIA guidance and in-Kingdom data residency options.
Wider GCC
Qatar, Kuwait, Oman and Bahrain engagements delivered from Dubai, with on-site workshops and Arabic-speaking solution architects.
United States
Cloud-native product engineering and AI enablement for US startups and mid-market enterprises, with overlapping EST and PST coverage.
Canada
AI, data platform and application modernisation work for Canadian firms, with data-residency aware architectures on Azure and AWS Canada regions.
United Kingdom
GDPR-aligned AI and software delivery for UK enterprises and scale-ups, from discovery through managed run.
India
Our Bengaluru engineering centre provides depth across AI, data and full-stack development at sustainable cost, under the same delivery standards.
Everywhere Else
Remote-first engagements across Europe, Africa and APAC, structured around your working hours and governance requirements.
Questions about Cloud, Data and DevOps
It depends on your existing estate and constraints. Azure is usually the strongest fit for Microsoft-centric enterprises, for Databricks and Fabric integration, and for UAE and Saudi data residency. AWS has the broadest service catalogue and the deepest regional footprint including Bahrain. Google Cloud is compelling for analytics-heavy and Kubernetes-native workloads. We are certified across all three and will recommend against a migration that does not pay for itself.
Usually, yes — often substantially. Typical savings come from rightsizing over-provisioned compute, cluster policies and autoscaling on Databricks, storage lifecycle tiering, reserved and spot capacity, and eliminating orphaned resources. We start with a fixed-price audit that quantifies the opportunity before you commit to remediation work.
A lakehouse combines the low-cost, flexible storage of a data lake with the transactions, schema enforcement and performance of a warehouse — typically Delta Lake on Databricks or OneLake in Microsoft Fabric. You need one if you have both structured and unstructured data, need ML and BI on the same platform, or are hitting cost or scale limits on a traditional warehouse. If your data volumes are modest, a well-designed PostgreSQL or Fabric warehouse is cheaper and simpler, and we will say so.
Yes. Azure operates UAE North and UAE Central regions and Saudi Arabia regions, AWS has a Bahrain region and a UAE region, and Oracle and others offer in-country options. We design for residency requirements including sovereign controls, encryption key management and audit logging, and we can deploy entirely on-premise where regulation requires it.
Yes. Managed tiers cover monitoring and alerting, incident response with defined SLAs, patching, cost review, capacity planning and a quarterly architecture review. Many clients combine this with a small embedded team for continuous improvement.
Let's build something your competitors cannot copy
Book a free 45-minute consultation with a solution architect. We will map your highest-value use case, sanity-check feasibility and send you a written summary — no obligation, no sales theatre.