Google Cloud, built for data and machine learning first.
GCP is the cloud we reach for when a product's core problem is genuinely a data or AI one. BigQuery and Vertex AI make it the natural home for analytics-heavy platforms and machine learning that needs to run in production, not just a notebook.
Where GCP earns its place in the stack.
Cloud migration and modernisation
Moving workloads and applications onto GCP as part of a wider move away from legacy infrastructure.
Data engineering with BigQuery
BigQuery and Dataflow build the pipelines and warehousing behind products where the data itself is the product.
Machine learning with Vertex AI
Vertex AI takes a model from experiment to production endpoint without a separate MLOps platform bolted on.
Cloud-native application development
Cloud Run and Cloud Functions let us ship applications that scale to zero and back without managing servers.
Where data and AI genuinely lead.
GCP is our choice whenever a product's core problem is a data or machine learning one. It's the cloud where BigQuery and Vertex AI genuinely lead the category, rather than sitting alongside services built for something else.
That focus shows up in how fast we can move. Provisioning a warehouse in BigQuery or getting a model into production through Vertex AI usually takes noticeably less integration work than the equivalent on a more general-purpose platform.
Built around data and ML, not retrofitted
BigQuery and Vertex AI were designed for exactly this kind of work, rather than AI services added on top of a general-purpose cloud.
Analytics at genuine scale
BigQuery separates storage from compute cleanly enough that querying billions of rows doesn't mean provisioning a permanent cluster.
Strong security, global infrastructure
GCP's security model and network are built on the same infrastructure Google runs its own products on.
A shorter path from model to production
Vertex AI's tooling means less time spent on plumbing between a trained model and something users can actually call.
Data or AI is the core problem? GCP is usually the answer.
Book a 30-minute working session with a senior engineer — a real conversation about your infrastructure, not a sales call.
Where GCP made the data work.
AI HSE reporting, from a single photo.
Our own product: an AI-powered progressive web app that turns a single photo into a complete observation, incident or hazard report — with a desktop command centre for tracking, investigation and approvals. Works offline and syncs on reconnect.
Frasers Aerospace — Operations Control Tower
An operations dashboard for an AS9120B aerospace supplier, built in Microsoft Lists and Power BI — orders from Sage 200 and WooCommerce in one place, the spreadsheets they used to be raised in retired, the exceptions blocking them surfaced first, and a TV wallboard for the warehouse floor.
A 12-tab spreadsheet, rebuilt as a risk platform
Offshore cable burial risk assessment taken out of a straining Excel workbook and rebuilt as a Python engine and React application on Microsoft Azure — a hundred thousand rows of AIS vessel data, Carbon Trust anchor-strike methodology, and depth-of-lowering curves for every soil zone on a route.
Real-time carbon & compliance reporting for cold chain.
A data management platform built to manage ESG data and deliver ESG insights, analytics and reports to the business and its stakeholders.
Time-critical incident reporting, built for the field.
A realtime incident reporting application built to collect, escalate and report on incident data across the cold chain and construction business.
One whitepaper platform, styled for every client.
A template-driven, multilingual whitepaper platform for Rethink Demand's clients — brandable in minutes, synced to Hermes, and built to capture and track every lead.
What a GCP build usually includes.
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Read moreThe stack we ship on.
Pragmatic, mostly boring, and chosen because it works in production — not because it's on the front page of Hacker News.
Have an outcome in mind?
We'll help you
ship it.
- You're building a data product and need a team that can deliver.
- You want to get AI-ready — pragmatically, not theoretically.
- Your reporting is a mess and you need a real platform underneath it.