What challenges did New Aim face before AI implementation?

Legacy siloed data and spreadsheets made planning slow and manual. SKU and inventory calculations required exhaustive manual work by category experts. The business needed always-on planning and high uptime while maintaining accuracy and human validation.

What was the impact of the AI solution?

Service uptime increased from 97% to 99%. Infrastructure incident response dropped from hours to 15 minutes. Automated SKU performance analysis that had been infeasible manually. New technology substrate is being commercialized under the AirOxy brand as a cloud-delivered SaaS model.

Source

GCPProductionEvidence: Medium65/100

New Aim deploys 24/7 agentic operational agents with Gemini Enterprise Agent Platform

Use case typeAI agentsUpdated Jun 29, 2026

New Aim built an AI-native operating layer on Google Cloud to replace siloed spreadsheets and manual SKU/inventory calculations. It centralized transaction and logistics data in Cloud Storage and BigQuery, used BigQuery ML for demand and pricing models, and deployed 24/7 internal agents with Gemini Enterprise Agent Platform and Gemini Flash models. The system lets planners query complex metrics in plain English, keeps human-in-the-loop validation for qualitative adjustments, and runs applications on Cloud Run.

Organization: New Aim
Industry: Retail
Location: Australia
Published: June 2026

Reported outcomes

Service uptime: 97% to 99%

Risk, reliability & safety

Catalog median for risk, reliability & safety deployments: +50% across 15 reported metrics. Compare benchmarks →

Why do we believe this?Outcome claims, sources, and evidence checks

Normalized claim

Service uptime: 97-99% increase

Google Cloud Customer StoryJun 29, 2026Customer storyExplicit claimMedium evidence strength

lifting service uptime from 97% to 99%

Normalized claim

Incident response time: 97.5% decrease

Google Cloud Customer StoryJun 29, 2026Customer storyInferred claimMedium evidence strength

crashing infrastructure incident response times from hours to just 15 minutes

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

Customer: New Aim
Provider: GCP
Maturity: Production
Linked source: Google Cloud Customer Story

It centralized transaction and logistics data in Cloud Storage and BigQuery, used BigQuery ML for demand and pricing models, and deployed 24/7 internal agents with Gemini Enterprise Agent Platform and Gemini Flash models

Customer identity supportedSource describes one deploymentMaturity supported

Primary read

Use case focus

Showing 3 of 3

1AI agents
2Planning automation
3Supply chain planning

Migrated the ecosystem to Google Cloud and built AimCore as a proprietary AI-native operating layer.
Centralized data in Cloud Storage and BigQuery, used BigQuery ML for demand and pricing models, and deployed 24/7 operational agents via Gemini Enterprise Agent Platform.
Applications and transaction endpoints run on Cloud Run, and the agents support natural-language metric queries with human-in-the-loop review.

Technologies

Gemini Enterprise Agent Platform Gemini Flash BigQuery BigQuery ML Cloud Storage Cloud Run

Service uptime increased from 97% to 99%.
Infrastructure incident response dropped from hours to 15 minutes.
Automated SKU performance analysis that had been infeasible manually.
New technology substrate is being commercialized under the AirOxy brand as a cloud-delivered SaaS model.

Architecture

New Aim migrated to Google Cloud and built AimCore as an AI-native operating layer. Transaction and logistics data are centralized in Cloud Storage and BigQuery, BigQuery ML powers demand and pricing models, Gemini Enterprise Agent Platform with Gemini Flash models powers 24/7 internal agents, and Cloud Run hosts applications and transaction endpoints.

Sources & evidence1

Evidence: Medium65/100Evidence strength