What challenges did OneAssure face before AI implementation?

Manual policy document processing took about 10 minutes per file and was prone to human error. Document handling mistakes affected payout accuracy and strained relationships with advisors. The team needed a faster way to extract structured data from offline insurance documents as the business scaled.

What was the impact of the AI solution?

Policy document processing dropped from 10 minutes to under 60 seconds. Data extraction accuracy improved from 95% to 98%. Manual query volume between internal teams and partners declined by 30%. The AI-powered chatbot generated 150-200 high-quality monthly leads. The company attributed about a 20% increase in active users to the AI tools and maintained about a 90% advisor retention rate.

Source

GCPProductionEvidence: High75/100

OneAssure uses Gemini Enterprise Agent Platform and Document AI to automate health insurance policy processing

Use case typeCustomer service automationUpdated Jun 13, 2026

OneAssure is an insurtech platform in Bengaluru, India that simplifies health insurance through a digital-first, human-centric model. The company needed to process large volumes of unstructured insurance policy PDFs and images that required manual data entry across many fields and caused errors in premium and nominee details.

Organization: OneAssure
Industry: Insurance
Location: India
Published: May 2026

Reported outcomes

+90%

quantified impactCustomer experience

10 minutestime60 secondstime95-98%accuracy30%quantified impact+20%quantified impact

Strategic outcomes

Speed & agilityAutomated policy document processingNew product / capabilityBuilt AI-powered advisor support experiencesCustomer experience & trustImproved payout accuracy and advisor relationshipsScale & capacityEnabled faster processing as business scaled

Catalog median for customer experience deployments: +25% across 53 reported metrics. Compare benchmarks →

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

Normalized claim

Time: 10 minutes decrease

Google Cloud Customer StoryMay 15, 2026Customer storyInferred claimHigh evidence strength

Policy document processing dropped from 10 minutes to under 60 seconds.

Normalized claim

Time: 60 seconds decrease

Google Cloud Customer StoryMay 15, 2026Customer storyInferred claimHigh evidence strength

Policy document processing dropped from 10 minutes to under 60 seconds.

Normalized claim

Accuracy: 95-98% increase

Google Cloud Customer StoryMay 15, 2026Customer storyInferred claimHigh evidence strength

Data extraction accuracy improved from 95% to 98%.

Normalized claim

Quantified impact: 30%

Google Cloud Customer StoryMay 15, 2026Customer storyInferred claimHigh evidence strength

Manual query volume between internal teams and partners declined by 30%.

Normalized claim

Quantified impact: 20% increase

Google Cloud Customer StoryMay 15, 2026Customer storyInferred claimHigh evidence strength

The company attributed about a 20% increase in active users to the AI tools and maintained about a 90% advisor retention rate.

Normalized claim

Quantified impact: 90% increase

Google Cloud Customer StoryMay 15, 2026Customer storyInferred claimHigh evidence strength

The company attributed about a 20% increase in active users to the AI tools and maintained about a 90% advisor retention rate.

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

Customer: OneAssure
Provider: GCP
Maturity: Production
Linked source: Google Cloud Customer Story

The company deployed an automated extraction tool on Google Kubernetes Engine and Document AI to process policy documents

Customer identity supportedSource describes one deploymentMaturity supported

Primary read

Use case focus

Showing 3 of 3

1Document Processing Automation
2Customer Service Automation
3Knowledge Assistant

Manual policy document processing took about 10 minutes per file and was prone to human error.
Document handling mistakes affected payout accuracy and strained relationships with advisors.
The team needed a faster way to extract structured data from offline insurance documents as the business scaled.

OneAssure worked with partner Enhub and used Gemini Enterprise Agent Platform to tune model selection and parameters for data extraction.
The company deployed an automated extraction tool on Google Kubernetes Engine and Document AI to process policy documents.
OneAssure also built Gemini-powered conversational experiences for product questions and coverage gap analysis, extending the AI workflow into advisor and customer support.

Technologies

Gemini Enterprise Agent Platform Gemini Google Kubernetes Engine BigQuery Document AI

Policy document processing dropped from 10 minutes to under 60 seconds.
Data extraction accuracy improved from 95% to 98%.
Manual query volume between internal teams and partners declined by 30%.
The AI-powered chatbot generated 150-200 high-quality monthly leads.
The company attributed about a 20% increase in active users to the AI tools and maintained about a 90% advisor retention rate.