Cloud and infrastructure
Google Cloud
Chosen for the workload.
Bring data engineering, analytics and AI into practical business workflows on Google Cloud, connecting decision-making to a dependable data foundation.
- Source systems
- Pub/Sub
- BigQuery
- Vertex AI
- Looker
- Review
Event bus diagram from source systems through Pub/Sub and BigQuery to Looker and Vertex AI with a review gate.
Google Cloud is especially relevant when marketing, sales and operational data need a dependable analytical foundation. Emerge connects source collection, BigQuery modelling and business activation so teams can move from conflicting reports to a shared definition of a lead, order or customer. The work includes the data contracts and operating decisions behind the dashboard, not just a new destination for existing exports.
Our published anonymised work includes modular BigQuery foundations, analytics repair and lead intelligence. Those examples support a practical sequence: reconcile data first, make it usable second, and add scoring or AI where its decisions can be evaluated. We distinguish that evidence from a proposed new AI architecture; a successful warehouse implementation is not an automatic claim that every Google AI product has been deployed for a client.
Choose the right starting point
Product expertise, in detail.
BigQuery foundations
Build datasets around source ownership, business definitions and query patterns. Separate raw receipt, validated transformations and approved reporting views so errors can be traced and repaired without rebuilding every report.
Customer intelligence
Connect lifecycle events and CRM outcomes to prioritisation. Define what a score means, how it is validated, and what a sales or service team should do differently when the score changes.
Marketing data warehouse
Bring analytics, campaign costs, lead progression and order data into a reconciled reporting model. Preserve attribution assumptions and missing-signal limits instead of presenting every joined number as an exact causal result.
Google Cloud AI integration
Design retrieval and model-assisted workflows using approved business context, bounded tool interfaces and task-level evaluation. Choose the applicable Google Cloud AI service and deployment arrangement after checking current account, model and regional availability.
The starting point
What needs to change.
Marketing platforms and CRM reports disagree because they measure different stages or join records at incompatible levels.
Sales teams receive scores without a clear definition, freshness indicator or evidence that the signal predicts the intended outcome.
Analytical consumption grows without a connection between expensive queries and the teams benefiting from them.
What we deliver
From opportunity to working systems.
Reconciled commercial data
Build a warehouse model that explains the relationship between acquisition, qualification, orders and customer value.
Practical intelligence activation
Publish evaluated scores and operational signals into the workflows where teams make decisions.
Metered analytics and AI
Connect query and AI consumption to workload ownership, budgets and a transparent Emerge service model where selected.
Illustrative solution architecture
How the pieces work together.
Bring context into the workflow, connect the right solutions, and make progress visible.
01 Understand the context
Signals & knowledge
Use the context and information already in place.
- Business priorities
- Trusted knowledge
- Operational data
02 Connect the solutions
Reconciled commercial data
Practical intelligence activation
Metered analytics and AI
03 Put it to work
Teams & operations
Connect people and systems to the next useful action.
Measured outcomes
Track agreed measures, learn, and improve the workflow.
Built around your existing technology
Implementation design
How the pieces work together.
Acquisition to commercial outcome
An illustrative warehouse links analytics events to campaign taxonomy, then joins permitted CRM and order identifiers. It keeps anonymous activity distinct from known customer records and makes unmatched rows visible. Reports distinguish acquired leads, qualified opportunities, booked orders and recognised revenue rather than treating them as interchangeable conversions.
Warehouse to operational priority
A scheduled or event-triggered process calculates an approved signal from governed data and publishes a concise result to a CRM field or work queue. The receiving team sees its timestamp and meaning. Failed updates can be replayed without creating duplicate tasks, and later outcomes return to the warehouse for evaluation.
Knowledge to assisted decision
For a proposed AI workflow, retrieve only the records and documents the user is allowed to access. Provide an evidence-backed draft or recommendation through a controlled application interface. A separate business action requires its own validation and permission; model output alone is never proof that an update completed.
Decisions to make early
Analytical grain and identity
Agree the unit of each table before joining it. A person can have several sessions, enquiries and opportunities; an order can have several line items and refunds. Tests should expose multiplication of revenue or leads caused by incorrect joins. Identity rules must respect permitted use and the quality of the available identifiers.
Cost follows query design
Partitioning, retention, scheduled transformations and dashboard behaviour influence consumption. We measure representative workloads and attribute usage to teams or products. A dashboard that refreshes frequently can create a different cost profile from an occasional analyst query, even when both read the same underlying dataset.
Useful reporting ownership
Give each material metric a definition, owner and reconciliation reference. Document data freshness and expected differences from source platforms. Looker Studio is a distinct reporting option; a requirement for enterprise Looker modelling is assessed separately rather than treated as the same product.
Our approach
A clear path into delivery.
Start with the business problem. Make each stage useful, reviewable and owned.
- 01
Trace priority metrics to source records and agree the grain, permissions, expected freshness and reconciliation tolerances.
- 02
Build a representative data path and business view, then validate unmatched records, updates, refunds and historical corrections.
- 03
Introduce activation or AI only after the data baseline passes review, with monitoring and a feedback loop into subsequent decisions.
Go deeper
Build a more informed brief.
Explore product-specific implementation, architecture and operating guidance for Google Cloud.
Google Cloud AI integration for business workflows
Ground Google Cloud model workflows in approved data, retrieval, controlled tools, evaluation and transparent task consumption.
Read the guide Google BigQueryBigQuery data warehouse implementation
Build a modular BigQuery foundation with source contracts, useful business models, reconciliation and cost-aware access.
Read the guide BigQuery and connected marketing dataMarketing data warehouse and revenue intelligence
Connect GA4, media spend and CRM outcomes in BigQuery with campaign governance, transparent attribution and commercial reporting.
Read the guideRelated Agentforce integration guides for Google Cloud.
These integration guides examine specific systems, access boundaries and illustrative use cases.
Practical questions
Before we begin.
Can Google Cloud support a Microsoft or Salesforce estate?
Yes. We define which application owns customer and transaction records, then connect the necessary data through controlled interfaces. Existing identity, reporting and operating constraints inform the design. Google Cloud does not need to replace the CRM to improve its decision inputs.
Can we begin with one unreliable report?
Yes. A narrowly scoped reconciliation is often the best entry point. We trace the important measures to source records, repair the joining or collection problem and publish an owned view. That gives later automation a foundation the business can inspect.
Explore the detail
Related work and resources.
See the approach in context. Client engagement stories are anonymised; related examples may come from other sectors or platforms.
Connected expertise
Capabilities
Technologies
Platforms
Solutions
Your next move
Bring us the business problem.
Pick a time below. We will help you define a useful starting point, the expertise you need and a practical path to delivery.
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