What challenges did Allvest Securities Private Limited face before AI implementation?

Legacy infrastructure struggled with high-concurrency demand. Investors needed simpler access to large market datasets and guidance in multiple languages. The platform needed to scale reliably during market volatility.

What was the impact of the AI solution?

Reduced support call center overhead by 90%. Lowered required human support team to one-tenth of original size. Cut deployment turnaround time by 40-60%. Sped up AI feature time-to-market by 40%. Improved system latency by 30%. Achieved 99.9% uptime during peak trading hours. Scaled to over 100,000 users.

Source

GCPScaled productionEvidence: Medium65/100

Allvest Securities Private Limited: Gemini Enterprise Agent Platform (Vertex AI) to power multilingual investor chatbot/voice bots

Use case typeCustomer support automationUpdated Jul 5, 2026

Allvest Securities Private Limited is an India-based fintech platform for stocks, bonds, and mutual funds. The company migrated to a cloud-native architecture on Google Kubernetes Engine and used Gemini Enterprise Agent Platform with the Agent Development Kit to build an AI-powered chatbot and voice bot for KYC and balance-related queries. It also implemented a "Talk to Data" capability using BigQuery, used Spanner for its transactional ledger, and served users in seven Indian languages.

Organization: Allvest Securities Private Limited
Industry: Finance
Location: India
Published: July 2026

Reported outcomes

−90%

support call center overhead reductionCost savings

−90%human support team size40-60%deployment turnaround time40%AI feature time-to-market improvement+30%system latency improvement99.9%uptime during peak trading hours

Strategic outcomes

Customer experience & trustEnabled multilingual investor supportBetter decisions & insightAdded AI-curated investment basketsScale & capacityBuilt a reliable source-of-truth investing platform

Catalog median for cost savings deployments: −40% across 177 reported metrics. Compare benchmarks →

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

Normalized claim

Support call center overhead reduction: 90% decrease

Google Cloud Customer StoriesJul 5, 2026Customer storyExplicit claimMedium evidence strength

Reduced support call center overhead by 90% using Gemini Enterprise Agent Platform

Normalized claim

Human support team size: 90% decrease

Google Cloud Customer StoriesJul 5, 2026Customer storyInferred claimMedium evidence strength

lowered required human support team to ~one-tenth

Normalized claim

Deployment turnaround time: 40-60% decrease

Google Cloud Customer StoriesJul 5, 2026Customer storyExplicit claimMedium evidence strength

Cut deployment turnaround time by 40–60%

Normalized claim

AI feature time-to-market improvement: 40%

Google Cloud Customer StoriesJul 5, 2026Customer storyExplicit claimMedium evidence strength

Sped up AI feature time-to-market by 40%

Normalized claim

System latency improvement: 30% increase

Google Cloud Customer StoriesJul 5, 2026Customer storyExplicit claimMedium evidence strength

Improved system latency by 30% for real-time Spanner workflows

Normalized claim

Uptime during peak trading hours: 99.9%

Google Cloud Customer StoriesJul 5, 2026Customer storyExplicit claimMedium evidence strength

Achieved 99.9% uptime during peak trading hours on Google Cloud

Normalized claim

User scale: 100,000 users

Google Cloud Customer StoriesJul 5, 2026Customer storyExplicit claimMedium evidence strength

scaled to over 100,000 users

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

Customer: Allvest Securities Private Limited
Provider: GCP
Maturity: Scaled Production
Linked source: Google Cloud Customer Stories

Scaled to over 100,000 users

Customer identity supportedSource describes one deploymentMaturity supported

Primary read

Use case focus

Showing 3 of 3

1Customer support automation
2Conversational assistants
3Voice automation

Legacy infrastructure struggled with high-concurrency demand.
Investors needed simpler access to large market datasets and guidance in multiple languages.
The platform needed to scale reliably during market volatility.

Migrated to Google Kubernetes Engine with infrastructure as code for agile deployment.
Used Cloud Spanner for high-concurrency transactional ledger workloads.
Built AI-powered chatbot and voice bot with Gemini Enterprise Agent Platform and Agent Development Kit.
Implemented Talk to Data with BigQuery and multilingual support for seven Indian languages.

Technologies

Gemini Enterprise Agent Platform Vertex AI Agent Development Kit Google Kubernetes Engine Cloud Spanner BigQuery Cloud SQL Datastream Cloud Storage

Reduced support call center overhead by 90%.
Lowered required human support team to one-tenth of original size.
Cut deployment turnaround time by 40-60%.
Sped up AI feature time-to-market by 40%.
Improved system latency by 30%.
Achieved 99.9% uptime during peak trading hours.
Scaled to over 100,000 users.

Architecture

Allvest migrated from legacy infrastructure to a cloud-native stack on Google Kubernetes Engine using infrastructure as code. The platform uses Cloud Spanner for its transactional ledger, BigQuery for a Talk to Data experience, and Gemini Enterprise Agent Platform with the Agent Development Kit to power chatbot and voice bot interactions. Cloud SQL, Datastream, and Cloud Storage support the broader data and application stack.

Sources & evidence1

Evidence: Medium65/100Evidence strength