When the product is data-heavy or developer-led

You’re building data-heavy or AI-driven platforms

Analytics, machine learning or large datasets are core—not decorative—often alongside productised AI integrations rather than one-off scripts.

You want infrastructure that supports rapid development

Teams need speed, modern tooling and pipelines that do not fight the grain.

You’re scaling digital products globally

Users, traffic and data cross regions; latency and sovereignty both matter.

GCP estates that underuse data and overcomplicate delivery

Powerful managed services only help when pipelines, permissions and observability are coherent—here is how we align them.

  • Infrastructure not optimised for data and analytics

    Insight arrives late or costs too much to produce. We design data-first architectures on Google Cloud services that match query and freshness needs.

  • Slow development and deployment cycles

    Rigid environments throttle iteration. We implement cloud-native, developer-friendly paths so releases are frequent and boring.

  • Difficulty scaling modern applications

    Growth exposes architectural ceilings. We use containers and managed services so scale is a parameter, not a project.

  • Fragmented data and tooling ecosystems

    Data sits in silos without a coherent access story. We centralise and structure platforms teams can trust for analytics and products.

  • Underused AI and machine learning capability

    Opportunities for automation and models stall on plumbing. We integrate Vertex AI and related services where they earn their keep, and connect roadmaps to bespoke AI when custom models are the goal.

Why Google Cloud fits data-led and product-led teams

  • Data-first infrastructureAnalytics, warehousing and pipelines are designed in from the start.
  • Developer-friendly environmentsFaster builds, deployments and feedback loops for engineering teams.
  • Cloud-native and container-led architectureFlexible systems aligned to microservices and API-first products.
  • Practical AI and ML integrationVertex AI and related services where use cases are clear and governed.
  • Global scale and performanceRegional and edge choices that match user distribution and data rules.

Google Cloud capabilities we bring

  • Google Cloud architecture and setup

    Cloud-native environments sized for your workloads.

  • Data platforms and analytics (BigQuery)

    Warehouses and pipelines for trustworthy insight, feeding reporting and dashboards and customer analytics programmes.

  • Application hosting and scaling

    Cloud Run, App Engine and GKE where each fits best.

  • Containerisation and orchestration

    Docker and Kubernetes-based deployments with operability in mind, within wider infrastructure architecture and deployment programmes when ops load is heavy.

  • AI and machine learning integration

    Vertex AI and related services wired to real product workflows.

  • API and microservices architecture

    Distributed systems that stay observable and evolvable—including API integrations where products are API-led.

  • Monitoring and observability

    Cloud Monitoring, logging and performance tracking teams use.

  • Ongoing optimisation and support

    Platform evolution as data, models and traffic change.

Google Cloud outcomes we’re structured to repeat

Representative engagements—see more data and platform work in our portfolio.

Data platform on Google Cloud

Enterprise analytics—architecture, BigQuery, pipelines. Fragmented data and weak insight replaced by a unified platform enabling advanced analytics with clearer ownership.

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How GCP supports intelligent, data-driven products

Google Cloud native services

BigQuery, Cloud Run, GKE, Vertex AI and more—used deliberately.

Data pipelines and analytics platforms

Structured processing that matches freshness and cost goals.

Containerised and microservices architecture

Distributed systems with clear boundaries and observability.

AI and machine learning integration

Capabilities that connect to product and governance needs.

Performance and global scalability

Designed for demand spikes and multi-region realities.

Support and continuity

Ongoing platform management, tuning and evolution.

How we deliver on Google Cloud

  1. Discovery and platform assessment

    We review data, applications and infrastructure needs.

    Clear opportunities; misaligned architecture reduced.

  2. Architecture and data strategy

    We design scalable, data-first cloud environments.

    Strong technical foundation; data and performance limitations reduced.

  3. Build and deployment

    We implement infrastructure, services and pipelines.

    Operational platform; misconfiguration risk reduced.

  4. Optimisation and scaling

    We refine performance, cost and scalability.

    Efficient, high-performing system; bottlenecks reduced.

  5. Monitoring and observability

    We implement logging, alerts and tracking.

    Real-time visibility; undetected issues reduced.

  6. Ongoing support and evolution

    We continuously improve and expand the platform.

    Long-term scalability and innovation; stagnation reduced.

Google Cloud

Looking to build a smarter, data-driven platform on Google Cloud?

We’ll walk through BigQuery, containers and ML touchpoints against your real pipelines—so the next hire lands into a platform, not a science project.

Talk to the teamView our work

Google Cloud hosting FAQs

When is Google Cloud the right choice?

For data-heavy, AI-driven or cloud-native applications that benefit from BigQuery, GKE and managed ML services.

Can GCP support machine learning and AI?

Yes—Vertex AI and related services are central to many of our engagements.

How does Google Cloud handle scalability?

Managed services, containers and autoscaling—designed around workload patterns, not slogans.

Can you build data platforms on GCP?

Yes—BigQuery and data pipelines are a core strength of our practice.

Do you support container-based deployments?

Yes—Kubernetes and Cloud Run feature heavily where operability allows.

Do you provide ongoing support?

Yes—continuous optimisation and platform evolution are part of our model.