AI & Machine Learning Consulting and Solutions on Google Cloud
Most AI work stalls somewhere between a promising prototype and a system the business can actually depend on. We build the data foundation, the models and the agents that get past that point, on Google Cloud.
Book an introduction callSee what we deliver
- 20+ years on Google Cloud
- Google Cloud Premier Partner
- 50+ certifications
- ISO 27001:2022
What tends to happen
Very few organisations are starting from zero with AI. Most have three or four experiments running, some real enthusiasm, and no clear route from any of it to something the business relies on every day.
The prototype worked, the rollout didn't
A model performs well in a notebook, but nobody owns retraining, monitoring or the data contract underneath it. By the time it drifts, the person who built it has moved on to something else.
Everyone knows the data isn't ready
Customer records sit across a CRM, a webshop, an email platform and a few spreadsheets that matter more than anyone admits. Any model built on top inherits every inconsistency underneath it.
The board wants AI results this quarter
There is pressure to show something soon, and pressure not to build something that has to be replaced within a year. In practice those two rarely point in the same direction.
From experiment to everyday use
The model itself is usually only one part of the challenge; getting it into everyday use also requires clear ownership, reliable data lineage, and the right engineering to make it dependable and accessible to other systems.
Before
- Models live in notebooks on someone's laptop
- No named owner for retraining or monitoring
- Insight arrives weeks after the decision was made
- Predictions nobody trusts enough to act on
- Licence costs rising with every seat and every use case
After
- Models deployed, versioned and monitored as services
- MLOps pipelines with clear ownership inside your team
- Predictions delivered into the tools people already use
- Governed lineage behind every output, so results can be defended
- Purpose-built alternatives you own rather than rent
Built on Google Cloud, properly
We were an early adopter of BigQuery and have been building on Google Cloud since 2006, which is long enough to have watched several waves of machine learning tooling arrive and then be replaced. It gives us a reasonably strong view on which parts of the current one are worth putting a production system on.
Every model, agent and platform on this page runs on that stack, which is also why we can be specific about what will and will not work in your environment.
20+
Years building on Google Cloud, since before most of it had a name
50+
Google Cloud certifications across the team
Premier
Google Cloud Premier Partner, with specialisations in data and infrastructure
AI and ML solutions we build
Every engagement starts from the data that is already there, which is usually spread across more systems than anyone expects. The foundation work is done once and then reused across use cases, so the second and third model tend to cost considerably less than the first.
AI-assisted development has changed the economics of custom software. Platforms that cost EUR 100–500k a year in licence fees are increasingly worth rebuilding as something you own, integrated natively with the data you already hold.
Analytics & BI
Qlik and comparable licences replaced by a BigQuery-native stack, with the semantic layer under your control.
Customer data platform
Segment and similar tools replaced by a warehouse-native CDP, so customer data stops leaving the building to come back.
Integration platform
MuleSoft-style middleware replaced by services on Cloud Run, priced by what you run rather than per seat.
iPaaS
Boomi and equivalents replaced by a custom integration layer built around your own systems and their quirks.
Generative AI & conversational agents
Generative AI consulting
Working out where generative AI is worth applying, and where a simpler model or a rule will outperform it at a fraction of the cost.
Conversational AI agents
Agents that answer questions against your own data, handle routine interactions and hand over cleanly when they should.
See the service →
Conversational commerce
LLM-powered assistance in retail and service contexts, grounded in live product, stock and order data.
Google Agentspace enablement
Getting agents into the hands of the people who need them, with the integration and governance work done properly.
Customer intelligence & personalisation
Customer lifetime value
Forecast value so retention and acquisition budgets go where they return most.
Read the article →
Churn prediction
Detect the early signals of loss while there is still time to act on them.
Read the case study →
Conversion propensity
Identify who is likely to convert, and stop spending against everyone else.
Read the case study →
ML customer segmentation
Group customers by behaviour, value and intent rather than by assumption.
Read the article →
Recommendation engines
Omnichannel product recommendations
The right product at the right moment, consistent across web, app, email and in-store.
Read the article →
Similar & frequently bought together
Pairings derived from actual behaviour rather than merchandising instinct.
Personalised content recommendations
Articles, videos and offers tailored to what each visitor has shown interest in.
Read the case study →
Search, classification & content intelligence
Search optimisation
Page classification and natural language processing so people find what they came for.
Read the case study →
Content classification
Tag and categorise large content libraries automatically, and keep them consistent.
Read the case study →
Topic modelling & clustering
Surface the themes inside an archive nobody has had time to organise by hand.
MLOps & model operations
Model deployment
Models packaged as versioned services with reproducible pipelines, so a result can be rebuilt six months later.
Monitoring & drift detection
Drift, latency and cost tracked against thresholds, with alerts that reach the person who can act on them.
Retraining pipelines
A schedule and an owner, rather than a task that gets picked up once accuracy has already slipped.
Governance & lineage
Access control and lineage from source table to prediction, so outputs can be defended when they are questioned.
What this looks like in practice
Three engagements where the work moved out of a model and into something the business uses every day.
Nederlands Dagblad · publishing
Predicting which subscribers cancel
Subscriber data sat across Zeno, Pubble and Google Analytics 4, with no single view of behaviour. We consolidated it on Google Cloud, narrowed 35–40 candidate churn indicators to the 12 with the most predictive weight, and built the model in BigQuery ML with monthly updates and Looker Studio dashboards.
Read the case study →
BNA · around 1,100 member firms
A search engine that finds the right architect
BNA's find-an-architect page drew traffic but had exit rates well above the rest of the site, and the tool behind it was time-consuming to maintain. Archy is a machine learning search engine trained on roughly 2,000 examples that pulls project data from the web in real time, so architects never maintain a profile.
Read the case study →
Public broadcaster · media
Reading audience behaviour inside a broadcast
A public broadcaster running 8 websites, 2 apps and 100+ social accounts could see that viewership moved, but not why. We built an application on BigQuery and Cloud Run that plots the audience curve against the segments of each episode.
Read the case study →
Before you get in touch
Our data is a mess. Is it too early to talk to you?
Usually not. Most of our engagements start with data that is spread across systems and partly undocumented, and the first thing we do is establish what is workable and what needs fixing first. It is a better conversation to have early than after a pilot has already failed for reasons nobody diagnosed.
How long before we see something working?
It depends on how accessible the underlying data is, which is an accurate answer rather than an evasive one. Where the data is already in reasonable shape, a first use case can be running relatively quickly. Where it is not, foundation work comes first, and we would rather say so at the start than halfway through delivery.
Do we have to be on Google Cloud already?
No, but it is where we work, and it is where our depth is. If you are on another platform we will tell you whether moving is worth it for your case, and sometimes it isn't.
What happens to the work when the engagement ends?
It sits in your cloud environment, under your ownership, documented well enough for your team to maintain. Some clients keep us on to run it, but that is a choice rather than a dependency we design in.
How do you handle governance and compliance?
Access control, lineage and monitoring are built in as the work moves to production rather than added afterwards. We are ISO 27001:2022 certified, and for regulated clients we work to whatever additional framework applies.
Next step
Want to know where your data actually stands?
If you are weighing up an AI initiative and want a clear read on whether the foundation supports it, we are happy to work through that with you.
