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How Jupiter Money Improved Customer Resolution Rates by 88% Using Amazon Bedrock with Locobuzz
#ShapingExperiences
Locobuzz is an AWS Technology Partner
Built on Amazon BedRock | Amazon EKS | Amazon MSK | Amazon OpenSearch Service | AWS EventBridge | Amazon S3 | Amazon CloudFront
AWS Partner Case Study | Locobuzz
From Fragmented Channels to AI-Powered Resolution at Fintech Speed
Jupiter Money one of India’s fastest-growing fintech platforms serving over 5 million users across Gen Z, millennials, and traditional banking demographics built its brand on the promise of making finance simple for everyone. But behind the scenes, its customer support operations were anything but simple. Inquiries arrived through a fragmented mix of social media channels, app store reviews, and in-app support forms, with no unified system to route, categorise, or respond to them consistently.
By partnering with Locobuzz, an AWS Technology Partner specialising in unified customer experience platforms, and deploying Amazon Bedrock as the generative AI engine at the core of the solution, Jupiter Money transformed its support operations. The result: an 88% first-contact resolution rate and a first-level response time of just 20 minutes across all customer channels powered by AI-suggested responses, semantic search, intelligent routing, and real-time alerting, all built natively on AWS.
This is the story of how Locobuzz’s AWS-native architecture turned Jupiter Money’s CX from a reactive, fragmented operation into an intelligent, proactive support machine one that scales as fast as the platform itself.
IMPACT AT A GLANCE
Jupiter Money: Making Finance Easy for Every Generation
With Locobuzz, impact goes beyond dashboards. Faster responses, smarter insights, and AI-powered actions drive measurable outcomes.
First-contact resolution rate across all customer channels
First-level response time, down from highly variable legacy performance
Multi-channel
Unified visibility across social media, app reviews & in-app support
AI-Assist
Amazon Bedrock-powered response suggestions for every agent interaction
ABOUT THE CUSTOMER
Jupiter Money: Making Finance Easy for Every Generation
Jupiter Money is one of India’s leading consumer fintech brands, offering payments, savings, and investment solutions through a single, seamlessly integrated app. Initially built for Gen Z and millennials, Jupiter Money has broadened its demographic reach to serve customers aged 18 to 60 spanning digital natives who expect instant resolution, and traditional banking customers who value thoroughness and trust.
With over 5 million users and a product portfolio spanning payments, savings accounts, and investment features, Jupiter Money manages a high volume of diverse customer conversations daily. The platform’s commitment to personalised, responsive service is a core brand differentiator particularly for a demographic where slow or inconsistent support directly drives churn. Every customer interaction, handled well or poorly, is a signal that shapes the brand.
Industry
Consumer Fintech Payments, Savings & Investments |
User Base
5M+ users; Gen Z, Millennials, and Traditional Banking Customers (18–60) |
Digital Channels Covered
Twitter, Instagram, Facebook, LinkedIn, Google Play, Apple App Store, In-App Support |
Product Portfolio
Payments, Savings, Investment Features each with distinct resolution workflows |
THE CHALLENGE
When Growth Outpaces the Systems Built to Support It
Jupiter Money’s rapid growth created significant operational complexity in managing customer communications. Inquiries arrived through a fragmented mix of channels — Twitter, Instagram, Facebook, LinkedIn, app store reviews on Google Play and the Apple App Store, and in-app support forms but these conversations lived in entirely separate systems with no unified view. The result was an operation that struggled to keep pace with the platform it was supposed to support.
The core challenges ran deep across the organisation:
When Growth Outpaces the Systems Built to Support It
Complex Social Media Management
While Jupiter Money had capable internal software for managing some customer queries, handling the volume and variety of social media interactions was a persistent challenge. The gap between social channels and internal systems led to missed messages, inconsistent resolutions, and cumbersome ticket management — all of which directly impacted customer satisfaction scores.
No Visibility into Resolution Performance
Without platform-wise segmentation of customer concerns, performance analysis was opaque. Teams had no clear view of resolution rates by channel, category, or agent — making it impossible to identify where the operation was succeeding or where it was leaking quality. Strategy was based on gut, not data.
Constant Manual Monitoring Overhead
Support teams were required to log in to monitoring dashboards manually to check for every alert and actionable item — an unnecessary overhead that consumed hours of productive time each day. Without automated alerting and intelligent routing, the team couldn't focus on what mattered: resolving customer issues quickly and well.
No AI Assistance for Agents
With a product portfolio spanning payments, savings, and investment features — each with distinct resolution workflows and knowledge requirements — support agents had no automated mechanism to route inquiries appropriately or receive AI-suggested responses. Every reply was composed from scratch, even for recurring issue types. This created bottlenecks, delayed responses, and introduced unnecessary variability in how similar issues were handled across the team.
Escalation Blind Spots
Complex issues requiring immediate escalation often went undetected until customer frustration had already compounded. Without intelligent pattern detection and automated escalation triggers, the team was always reacting — never anticipating. For a fintech brand serving millions of financially engaged users, this reactive posture carried real reputational risk.
THE COST OF INACTION
Rising customer churn driven by slow, inconsistent, and fragmented support experiences
Inconsistent brand experience across digital and social channels, weakening trust and loyalty
Lack of actionable insights results in missed opportunities to improve products and preempt recurring issues
Escalating operational costs as manual workloads grow in proportion to inquiry volume
Delayed issue resolution leading to amplified customer frustration and public escalations on social platforms
Reputational risk at scale in a highly competitive fintech market where responsiveness is a key differentiator
THE AWS ADVANTAGE
Why Amazon Web Services and Why Locobuzz as the AWS Technology Partner
Solving Jupiter Money’s challenge required more than a better helpdesk. It required a managed AI platform capable of understanding customer intent in natural language, generating contextually relevant response suggestions at scale, routing inquiries intelligently across a multi-product portfolio, and doing all of this in compliance with India’s data residency requirements for regulated financial services. That is exactly what Locobuzz architected — on AWS.
Locobuzz as an AWS Technology Partner
Locobuzz’s status as an AWS Technology Partner was fundamental to delivering a security-first generative AI solution for Jupiter Money combining the agility of a high-growth fintech with the compliance standards of a Tier-1 bank.
With deep expertise in Amazon Bedrock, Amazon EKS, and Amazon OpenSearch Service, Locobuzz enabled rapid deployment of a scalable, AI-powered platform without the need to build from scratch.
Running entirely within AWS Region India, the solution ensures all customer data remains within regulatory boundaries allowing Jupiter Money to move at startup speed while maintaining enterprise-grade security and compliance.
The AWS partnership also ensured that Havells’ sensitive customer data remained within India, meeting data residency and compliance requirements through AWS’s regional infrastructure presence.
The AWS Services Powering the Solution
Amazon Bedrock
It provides generative AI capabilities for understanding customer intent, suggesting contextually relevant responses, and summarizing conversation history. When a customer inquiry arrives whether from Twitter, app store review, or in-app support form Amazon Bedrock analyzes the message text to infer the customer's intent (payment issue, account question, product information request, etc.) and suggests an appropriate response category and agent-assist language.
Amazon OpenSearch Service
The semantic retrieval layer. OpenSearch indexes all customer conversations and resolution outcomes, enabling support agents to instantly surface semantically similar historical cases and relevant knowledge base articles. When an agent responds to a new inquiry, OpenSearch retrieves contextually matching past resolutions, which Amazon Bedrock then uses to generate tailored response suggestions dramatically accelerating resolution quality and consistency.
Amazon Elastic Kubernetes Service (EKS)
The scalable microservices engine. Locobuzz deployed the platform's inquiry ingestion, categorisation, and intelligent routing microservices on Amazon EKS. Each microservice scales independently based on inquiry volume, ensuring consistent performance during traffic spikes whether from a product launch, a service disruption, or a viral social media moment without over-provisioning resources.
Amazon Managed Streaming for Apache Kafka (MSK)
The real-time messaging backbone. Amazon MSK acts as the distributed message broker between platform components, ensuring that no customer inquiry is ever lost in transit and that all microservices remain loosely coupled for operational resilience. Every incoming inquiry regardless of channel flows through MSK before entering the categorisation and AI enrichment pipeline.
AWS EventBridge
The intelligent escalation engine. EventBridge connects the platform to Jupiter Money's existing operational systems, triggering automated alerts when inquiry patterns indicate escalation needs, SLA violations, or emerging issues such as a spike in payment-related complaints. This real-time alerting capability replaced manual dashboard monitoring entirely shifting Jupiter Money's support operation from reactive problem-solving to proactive issue management.
Amazon S3 & Amazon CloudFront
The data and delivery layer. Amazon S3 serves as the centralised data lake for all customer conversations, inquiry metadata, and resolution outcomes supporting historical analysis, AI model improvement, and financial services compliance auditing. Amazon CloudFront distributes the Locobuzz platform interface to Jupiter Money's support teams across India with consistently low-latency access, regardless of location.
THE LOCOBUZZ SOLUTION
An AI-Powered, AWS-Native Support Platform Built for Fintech Scale
Locobuzz architected a unified customer experience platform that ingests every Jupiter Money customer inquiry from every channel, processes it through an AI-powered workflow built on Amazon Bedrock and Amazon OpenSearch Service, and empowers support agents with the intelligence they need to resolve issues faster, more consistently, and at greater scale than was ever possible before.
Multi-Channel Ingestion via Amazon MSK
The platform begins at the point of inquiry. Every customer message whether from Twitter, Instagram, Facebook, LinkedIn, Google Play reviews, Apple App Store reviews, or in-app support forms is ingested in real time and streamed through Amazon Managed Streaming for Apache Kafka (Amazon MSK). MSK acts as the fault-tolerant event backbone, ensuring that no inquiry is lost and that all downstream processing components remain loosely coupled and independently scalable. Each message carries full metadata: channel source, timestamp, customer profile indicators, and product context.
Semantic Search & AI-Assisted Response via Amazon OpenSearch and Amazon Bedrock
Once categorised, each inquiry is indexed in Amazon OpenSearch Service alongside all historical conversations and resolution outcomes. When an agent opens a new inquiry, Amazon OpenSearch retrieves semantically similar past cases not just keyword matches, but contextually relevant precedents surfacing how comparable issues were resolved before.
Amazon Bedrock then takes this retrieved context and generates a tailored, agent-assist response suggestion: a ready-to-review reply that accounts for the customer’s specific intent, the relevant product area, and the resolution patterns of similar cases. Agents review and personalise the suggestion rather than composing responses from scratch dramatically accelerating response time, improving consistency, and freeing skilled agents to focus their energy on genuinely complex, edge-case issues that require human judgement.
Data Lake, Analytics & Compliance via Amazon S3
All customer conversations, AI classifications, agent responses, and resolution outcomes are stored in Amazon S3, creating a comprehensive, auditable data lake. This centralised repository enables historical analysis of inquiry volumes by category, resolution rates by topic and team, and emerging product or service issues — giving leadership the intelligence to identify training needs, close product gaps, and anticipate seasonal demand. For a regulated fintech, the S3 data lake also provides the audit trail required for financial services compliance.
AI Classification & Intent Inference via Amazon EKS and Amazon Bedrock
Inquiries flow from MSK into Locobuzz’s microservices running on Amazon EKS, where they are processed through multiple intelligence layers. The first layer extracts channel metadata and inquiry context. The second powered by Amazon Bedrock performs generative AI intent inference: analysing the customer message to classify it into a structured category (payment issue, account question, savings feature, investment query, security concern, general inquiry) and extract key resolution signals.
This automated categorisation eliminates the manual triage bottleneck entirely. A payment failure report is immediately routed to the payments resolution workflow. An investment feature question is flagged for the investment support team. A security concern triggers an immediate escalation path. The right inquiry reaches the right team, every time, without human intervention at the routing layer.
Proactive Escalation & SLA Management via AWS EventBridge
AWS EventBridge monitors inquiry patterns across the entire platform and triggers automated alerts the moment significant events are detected: an SLA violation threshold approaching, a sudden spike in payment-related complaints, a security concern requiring immediate escalation, or a pattern of repeated issues indicating a potential product-level problem. These real-time alerts replace the manual dashboard monitoring that previously consumed hours of the team’s attention every day.
When an inquiry indicates a critical issue, EventBridge routes the alert to the appropriate team immediately ensuring no escalation goes unnoticed and no critical customer concern waits in a queue. Jupiter Money’s support leadership can now manage by exception rather than by constant surveillance.
RESULTS & IMPACT
From Reactive Chaos to AI-Powered Resolution Excellence
By deploying Locobuzz on AWS, Jupiter Money unified fragmented channels, automated routing, and augmented agents with Amazon Bedrock-powered intelligence — eliminating key operational bottlenecks. What was once a reactive support function is now a scalable, AI-driven system that delivers faster, more consistent, and insight-led customer resolution.
Resolution Quality & Speed
- 88% first-contact resolution rate across all customer channels enabling Jupiter Money to scale to over 5 million users without a proportional increase in support headcount, fundamentally improving operational efficiency
- Reduced dependency on manual intervention with AI-driven response suggestions and intelligent routing handling a significant share of routine queries, allowing teams to focus on high-value, complex interactions
- Significant improvement in response consistency agents working on similar issues now receive uniform, AI-suggested guidance, eliminating variability and strengthening overall resolution quality
Operational Efficiency
- Manual SLA tracking replaced entirely by automated AWS EventBridge alerts — support managers now manage by exception rather than by constant dashboard monitoring
- Reduction in manual monitoring overhead freed substantial engineering and operations time for strategic initiatives and complex customer issue resolution
- Channel-wise performance metrics now visible in structured dashboards, enabling data-driven strategy refinement and clear accountability for resolution rates by team and topic
Intelligence & Strategic Visibility
- All conversations indexed in Amazon OpenSearch and archived in Amazon S3 give leadership comprehensive analytics: inquiry volumes by category, resolution rates by topic, and emerging issues requiring product attention
- Support supervisors can now proactively identify training needs, product gaps, and seasonal patterns — enabling continuous improvement rather than reactive firefighting
- The Slack integration further streamlined internal coordination: alerts and actionable insights flow directly into team channels, enabling faster escalations and shared situational awareness across marketing, product, and support
Customer Experience & Brand Loyalty
- Customers across all channels from Gen Z users on social media to older demographics using in-app support now experience the same high-quality, rapid support, reinforcing Jupiter Money’s brand promise of simplicity and responsiveness
- For a fintech brand serving a demographic where customer service responsiveness is a key loyalty driver, the consistency and speed improvements directly strengthen retention and word-of-mouth advocacy
- Platform scalability means Jupiter Money can expand to additional channels or customer segments without rebuilding infrastructure support operations can now grow as fast as the user base itself
ABOUT LOCOBUZZ
Your AWS Technology Partner for AI-Powered Customer Experience
Locobuzz is an AWS Technology Partner and leading provider of unified customer experience platforms built natively on AWS. The company specialises in helping enterprises consolidate fragmented customer communications across social media, chat, app reviews, voice, and messaging into a single AI-powered platform that resolves issues faster, more consistently, and at greater scale.
Locobuzz leverages Amazon Bedrock for generative AI response suggestions and intent inference, Amazon EKS for scalable microservices, Amazon OpenSearch Service for intelligent semantic search, Amazon MSK for real-time data streaming, AWS EventBridge for proactive escalation, and Amazon S3 for compliant data archiving. As an AWS Technology Partner, every Locobuzz deployment is built on AWS best practices and proven architectures scalable for millions of customer conversations, compliant for regulated industries, and ready to grow as fast as the businesses it serves.
With proven implementations across fintech, e-commerce, banking, consumer goods, and hospitality, Locobuzz has helped leading Indian and global enterprises transform customer support from a cost centre into a competitive advantage.
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Ready to Transform Your Customer Support with Generative AI on AWS?
Join Jupiter Money and other leading Indian enterprises using Locobuzz’s AWS-powered platform to resolve faster, respond smarter, and scale without limits. Whether you’re a high-growth fintech managing millions of daily interactions or an enterprise looking to bring consistency and intelligence to fragmented customer channels, Locobuzz and AWS are ready to build with you.
