Portfolio Accelerator · Insurance · Google Cloud Platform
Policy Customer Intelligence AI on GCP: An Implementation Blueprint
A documented GCP architecture for policy servicing and customer intelligence: churn prediction, next-best-action, and consent-governed customer data.
problem
The Problem: Insurers Can't Get a Straight Answer on Customer Intelligence AI
Most vendor conversations about policy customer intelligence AI mix churn modeling, next-best-action recommendations, and customer data platforms into one vague pitch without ever naming the actual cloud services, the data model, or how consent and PII are governed. A GCP-native insurer evaluating this space deserves a specific architecture, not a capability slide.
demo
What We're Building on GCP: Churn Prediction, Next-Best-Action, Governed Customer Data
This page shows the architecture for Kriv AI's policy customer intelligence accelerator on Google Cloud. In the interest of transparency: the reference-architecture and technology-selection phase is complete; the data model, model training, and application layers are specified but not yet implemented. We're presenting this honestly as a blueprint.
BigQuery as the Governed Customer Data Platform
The design centers on BigQuery as the policy, claims, and customer data warehouse, giving a GCP-native insurer a single governed source of truth for customer 360 analytics rather than a parallel data platform bolted on by a point-solution vendor.
Vertex AI: The Churn-Prediction and Next-Best-Action Models
Vertex AI is designed to host the churn-prediction model (identifying policyholders at risk of non-renewal) and a next-best-action recommendation model for cross-sell and retention offers, with Dialogflow CX handling the conversational layer for policy servicing inquiries and Looker providing the customer-intelligence dashboards.
Consent Management and PII Controls, Built In From Day One
Consent management and PII handling are designed into the data model from the start, not retrofitted after a pilot: the plan is for customer consent state and data-sensitivity classification to live alongside the customer record itself in BigQuery, so every downstream model and dashboard respects the same governance boundary.
status
What's Real Today, and What's Roadmap
Honestly: the technology selection and reference-architecture phase is complete (20 reference repositories reviewed across Vertex AI, Dialogflow CX, BigQuery ML, Cloud Run, Looker, and insurance cross-sell/churn modeling). The data model design, model training, and end-to-end application build are the next phases and are not yet implemented on synthetic data. We'd rather show you the real architecture than a demo that doesn't exist yet.
differentiation
Why This Beats a Big 4 Discovery Deck
A Big 4 discovery engagement for insurance customer intelligence typically produces a capability slide and a multi-quarter roadmap. This page shows the actual GCP services, the actual data model decisions, and the actual model architecture already selected, giving a GCP-native insurer a faster path from conversation to a scoped build.
audience
Who This Accelerator Is Built For
Built for CTOs, compliance leads, and insurance operations leaders at GCP-native carriers evaluating whether to build customer intelligence AI in-house, buy a point solution, or bring in an implementation partner who already has the architecture mapped.
engagement
From Blueprint to Production: How a Scoped Engagement Works
A scoped engagement builds this architecture out against your real policy and customer data in BigQuery, carrying the same consent-management and governance design through from day one.
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Straight answers
Frequently asked questions about Policy Customer Intelligence AI on GCP: An Implementation Blueprint
Is the policy customer intelligence accelerator fully built and running on GCP?
Not yet, and we want to be upfront about it. The reference-architecture and technology-selection phase is complete (20 reference repositories reviewed); the data model, model training, and application build are specified but not yet implemented on synthetic data.
What GCP services does the architecture use?
BigQuery as the governed customer data platform, Vertex AI for churn-prediction and next-best-action models, Dialogflow CX for the conversational layer, and Looker for customer-intelligence dashboards, all provisioned via Terraform.
Will this use real policyholder data?
No. This accelerator, once built, will run entirely on synthetic policyholder and claims data. No real customer or PII data is used at any stage of the demonstration.
How is consent management handled?
Consent management and PII sensitivity classification are designed into the BigQuery data model from the start, so every downstream model and dashboard respects the same governance boundary rather than treating consent as an afterthought.
Why publish an architecture page before the accelerator is built?
Because the architecture itself, real technology choices mapped to a real insurance use case, is useful for a GCP-native insurer evaluating an implementation partner, and because we'd rather be transparent about build status than overstate readiness.
Can Kriv AI build this out for our carrier's GCP environment?
Yes. A scoped engagement can build this architecture against your real policy and customer data in BigQuery. Contact us to discuss scope and timeline.
Ready to see the accelerator run against your data model?
Bring your requirements to a working session and we'll walk through the live system.
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