What challenges did Artificial Labs face before AI implementation?

Artificial Labs needed to process high volumes of complex insurance claims data at unprecedented speeds. Conscium struggled with managing and analyzing vast datasets efficiently enough to deliver timely insights. OQC required substantial computational resources to advance quantum research without prohibitive infrastructure investments.

What was the impact of the AI solution?

Artificial Labs now processes most claims without intervention, reducing errors while accelerating payments to policyholders. Conscium improved the speed and reliability of its AI evaluation pipeline. OQC accelerated its research cycle and reduced capital expenditure on computational infrastructure.

Source

GCPProductionEvidence: Medium65/100

Artificial Labs, Conscium, and Oxford Quantum Circuits (OQC) use Gemini Enterprise Agent Platform and BigQuery to automate insurance claims

Use case typeClaims automationUpdated Jun 13, 2026

Artificial Labs built an insurance claims automation system on Gemini Enterprise Agent Platform to recognize patterns in complex insurance documentation and process most claims without intervention. Conscium deployed BigQuery as the foundation of its data analytics platform to process terabytes of information in real time and support dynamic dashboards. OQC used Google Cloud high-performance computing resources to run hybrid quantum-classical algorithms and Gemini Enterprise Agent Platform for quantum machine learning pipelines.

Organization: Artificial Labs
Industry: Insurance
Location: United Kingdom
Published: Invalid Date

Reported outcomes

Strategic outcomes

New product / capabilityAutomated insurance claims processingBetter decisions & insightReal-time analytics and dashboardsSpeed & agilityImproved AI evaluation pipeline speedScale & capacityAdvanced quantum research computing capability

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks

Customer: Artificial Labs, Conscium, Oxford Quantum Circuits
Provider: GCP
Maturity: Production
Linked source: Google Cloud Customer Story

Conscium deployed BigQuery as the foundation of its data analytics platform to process terabytes of information in real time and support dynamic dashboards

Customer identity supportedSource describes one deploymentMaturity supported

Primary read

Use case focus

Showing 3 of 3

1Claims automation
2AI agents
3Data analytics

Artificial Labs needed to process high volumes of complex insurance claims data at unprecedented speeds.
Conscium struggled with managing and analyzing vast datasets efficiently enough to deliver timely insights.
OQC required substantial computational resources to advance quantum research without prohibitive infrastructure investments.

All three organizations migrated core workloads to Google Cloud.
Artificial Labs built claims automation using Gemini Enterprise Agent Platform and AI agents.
Conscium used BigQuery for real-time analytics and dynamic dashboards.
OQC used Google Cloud HPC resources and Gemini tools for quantum ML pipelines and post-run analysis.

Technologies

Gemini Enterprise Agent Platform BigQuery Gemini for Google Cloud

Artificial Labs now processes most claims without intervention, reducing errors while accelerating payments to policyholders.
Conscium improved the speed and reliability of its AI evaluation pipeline.
OQC accelerated its research cycle and reduced capital expenditure on computational infrastructure.

Architecture

Artificial Labs built an insurance claims automation system on Gemini Enterprise Agent Platform, using AI agents to recognize patterns in complex insurance documentation and process most claims without intervention. Conscium deployed BigQuery for real-time analytics and dynamic dashboards. OQC used Google Cloud high-performance computing resources to run hybrid quantum-classical algorithms and Gemini Enterprise Agent Platform to execute and manage quantum machine learning pipelines, with Gemini for Google Cloud used for post-run analysis.

Sources & evidence2

Evidence: Medium65/100Evidence strength