Gen AI for Marketing: Building a Personalisation Engine on Google Cloud

Every marketing team talks about “personalisation,” but few actually deliver it. Most campaigns still rely on broad segments: “users aged 25-34 who clicked on a product page.” That is not personal. That is a bucket.

What if you could generate unique creative assets, email copy, and video content for each individual customer, based on their actual behaviour and preferences? That is the promise of Generative AI in marketing, and it is now achievable with production-ready architectures on Google Cloud.

The Core Idea

The approach is straightforward in concept, though demanding in execution:

Ingest Customer Data: Bring together signals from your CRM, website analytics, transaction history, and external sources into BigQuery.
Derive Marketing Insights: Use Dataflow pipelines to process this data and build profiles. Think demographic segments, purchasing patterns, and predicted interests.
Generate Personalised Content: Send these insights to Google’s Gemini API (via Vertex AI) with carefully crafted prompts. Gemini can then produce text, audio, or video assets tailored to each user.
Serve and Measure: Store generated assets in Cloud Storage and serve them dynamically when customers visit your web portal. Track engagement and feed results back into the model.

This is not a theoretical exercise. Google has published a detailed reference architecture outlining exactly this pattern, complete with code samples for experimentation.

Why This Matters Now

The technical barriers to this kind of system have dropped dramatically. Gemini and other frontier models can now produce high-quality creative output. BigQuery and Dataflow handle petabyte-scale data processing without breaking a sweat. Cloud Run provides serverless compute that scales to zero when idle, keeping costs manageable.

But the real unlock is speed. Traditional creative production takes weeks. Briefing, concepting, design, revisions. With a well-engineered GenAI pipeline, you can generate thousands of unique assets in minutes.

The Non-Negotiables

Before you rush to implement, consider the guardrails:

Human Oversight: Automated content generation is powerful, but brand safety is non-negotiable. Google recommends a human verification step before any generated content goes live. We agree.
Feedback Loops: A static model degrades over time. Build mechanisms to track campaign performance (click-through rates, conversions) and use that data to refine your prompts and fine-tune the model.
Data Quality: Garbage in, garbage out. If your customer data is inconsistent or incomplete, the “personalisation” will feel hollow. Data engineering is the foundation, not an afterthought.

The Technology Stack

This architecture leans heavily on Google Cloud’s managed services:

Component	Role
BigQuery	Central data warehouse for customer insights
Dataflow	Stream and batch processing for profile generation
Eventarc	Event-driven triggering of content generation
Cloud Run	Serverless execution of the orchestration logic
Vertex AI (Gemini)	Content generation via large language models
Cloud Storage	Asset storage and delivery

If you are already invested in the Google Cloud ecosystem, this is a natural extension. If not, the principles translate to other clouds, though the specific integrations will differ.

Where Alps Agility Comes In

We specialise in building these kinds of production-grade AI systems. Not demos. Not proofs of concept. Real pipelines that handle real data and generate real business value.

If your marketing team is drowning in manual creative production, or if you are curious about how GenAI could transform your customer engagement, we should talk.

Get in touch for a no-obligation conversation about what is possible.

Reference: Google Cloud Architecture Center: Generative AI for Personalised Marketing Campaigns