Company: FMEG

The intelligence core. Enables semantic vector search and real-time clustering understanding that "battery drain," "low runtime," and "won't hold charge" are the same issue. Manages millions of indexed mentions with zero operational overhead for Havells.

The real-time data backbone. Ingests feedback continuously from social platforms, review sites, and support systems. Decouples data producers from consumers so that spikes after a product launch never overwhelm downstream analysis.

The enrichment engine. Runs Locobuzz's AI microservices that classify, score, and cluster each feedback item. Auto-scales to handle surges in inbound data without manual intervention.

The AI layer. Provides access to foundation models for few-shot classification, sentiment intensity scoring, intent extraction, and semantic embedding without the overhead of building and maintaining custom ML infrastructure.

The alerting layer. Detects critical sentiment events and instantly notifies regional team leads via SMS and email with issue summaries and direct links to relevant feedback clusters.

The data lake and delivery layer. S3 stores all enriched feedback durably for historical analysis and ML model training. CloudFront accelerates dashboard access globally, ensuring sub-second response times for stakeholders across India and beyond.

The foundation begins with comprehensive data collection. Locobuzz built connectors to ingest customer feedback from product review sites (Amazon India, Flipkart, Google Shopping), social media platforms (Twitter, Facebook, Instagram), YouTube comments, customer support systems, e-commerce forums, and industry blogs all flowing into Amazon Managed Streaming for Kafka (MSK).

MSK acts as the distributed, fault-tolerant event backbone of the platform. It decouples data producers and consumers using Amazon MSK, with processing handled independently on Amazon EKS. This ensures that even during viral spikes in negative feedback, ingestion continues smoothly while dashboards remain unaffected preventing system overloads and reinforcing the Reliability Pillar of the AWS Well-Architected Framework.

Enriched feedback flows into Amazon OpenSearch Service, where it is indexed instantly and made searchable at scale forming the backbone of Havells Real-Time Digital Command Center. This unified intelligence layer transforms fragmented customer conversations into actionable insights in real time.

Leveraging Amazon OpenSearch Service’s vector engine, Locobuzz bridges the “semantic gap,” enabling the system to understand that different words and phrases can indicate the same underlying issue. This allows Havells to run semantic queries like “Show me all complaints about product quality,” while also tracking sentiment across news, blogs, discussion or complaint forums, and competitor mentions to surface emerging risks early.

The platform’s clustering engine uses semantic similarity to automatically group related complaints into issue clusters, giving analysts instant visibility into recurring problems without manual review. At the same time, its fully managed architecture removes the operational overhead of scaling and optimisation allowing Havells to focus purely on insights and action.

For strategic decision-making, Havells needs more than raw data it needs insight. Locobuzz built analytics interfaces that query Amazon OpenSearch Service to surface sentiment trends over time, product performance rankings, regional comparisons, and dealer reputation scores. All enriched data feedback text, AI classifications, semantic embeddings, and source metadata is stored durably in Amazon S3, creating a comprehensive data lake that supports historical analysis, ML model training, and integration with Havells existing BI tools. Amazon CloudFront ensures that executives in Mumbai, Delhi, Bengaluru, and beyond experience sub-second dashboard response times, regardless of concurrent load.

Raw feedback is noisy. “Fan motor not working,” “propeller stuck,” and “overheating after 10 minutes” all point to the same underlying defect but simple keyword matching would treat them as entirely separate issues. Locobuzz’s enrichment microservices, running on Amazon EKS, resolve this through multiple layers of AI intelligence powered by Amazon Bedrock.

Using few-shot prompting with foundation models, the platform classifies each feedback item into structured categories: product defect, dealer/service issue, delivery and logistics, pricing concern, competitor mention, campaign feedback, or general inquiry. It then extracts structured fields for every item — affected product SKU, severity level (critical defect versus minor inconvenience), sentiment intensity (strongly negative through to strongly positive), and actionable intent (requires response, FYI only, escalate immediately).

Critically, the enrichment layer uses semantic embeddings from Amazon Bedrock’s foundation models to cluster related feedback together. Complaints about “battery drain,” “low runtime,” and “won’t last long” are automatically identified as the same power endurance issue surfaced as a single, prioritised cluster rather than hundreds of isolated data points.

The moment critical patterns emerge a surge in negative sentiment about a specific product, a trending defect cluster, a competitor launch triggering brand perception shift AWS EventBridge detects these events from OpenSearch query results and triggers immediate alerts via Amazon SNS.

Regional team leads receive SMS and email notifications with issue summaries and direct links to the relevant feedback clusters in the command center. Alerting thresholds are configured per region and product line: a handful of critical safety defect reports trigger immediate executive escalation, while general sentiment trends generate scheduled daily summary reports. This real-time responsiveness means Havells never misses a critical issue and always moves faster than competitors to address problems.

• 6.3 million mentions of Havells and competitor brands analyzed across digital channels
• 525,000 actionable items identified and prioritised via AI enrichment
• ~90% reduction in manual workload what previously took analysts a full day of manual review now happens instantly
• Analysts moved from processing 1,000 comments to extract 10 insights, to receiving 500+ high-quality signals immediately

• 98% response rate to critical customer issues  every safety concern and widespread defect now triggers immediate regional team alerts
• Response times reduced from days to hours, dramatically reducing brand damage and preventing social media escalation
• In a critical instance, the platform identified a batch of fans exhibiting premature bearing failure within hours enabling Havells to intervene proactively before negative reviews or social amplification could escalate into a full-blown crisis. This early detection not only resolved customer issues faster but also protected brand equity, demonstrating the platform’s tangible ROI as a real-time crisis prevention engine.

• Product teams gained regional sentiment analysis revealing which product lines needed redesign — driven by customer voice, not internal assumption
• Marketing teams used competitor mention tracking to understand campaign effectiveness relative to brands like Crompton and Orient
• Dealer networks leveraged customer feedback analytics to identify top-performing and underperforming partners, enabling targeted support and retraining
• Supply chain teams correlated delivery complaints with logistics partners, measurably improving on-time performance

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