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NLP-as-a-Service Platform for Multilingual Customer Support Automation

NLP-as-a-Service Platform for Multilingual Customer Support Automation

A global BPO headquartered in Manila, Philippines, managed chat and email support for international clients across telecom, retail, travel, and finance domains. They required a scalable NLP engine that could analyze sentiment, tag intents, and classify conversations across multiple languages. The organization partnered with our engineering team to develop a high-throughput NLP-as-a-Service (NLPaaS) platform capable of processing tens of millions of messages daily.

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20M+

Messages/day processing capacity

<180

ms NLP API latency

72%

Reduction in manual tagging workload

About  

Problem Statement

  • Existing rule-based classifiers failed across multilingual support conversations.
  • Manual tagging was slow and inconsistent across teams and countries.
  • No centralized NLP API existed for real-time sentiment analysis.
  • High-volume workloads (20M+ messages/day) required highly scalable architecture.
  • Business teams lacked visibility into conversation trends and ticket themes.

Industry: BPO & Customer Support

Services: NLP-as-a-Service, AIaaS, Automation

Region: Philippines

Our Tech Stack

Tech stack we used

Solution Approach

  • Fine-tuned XLM-RoBERTa on 1.2M multilingual support transcripts.
  • Built a real-time FastAPI layer that exposed sentiment, intent, and category tagging APIs.
  • Implemented RabbitMQ batch-processing queues to handle massive incoming message bursts.
  • Used Redis caching to speed up repeated or similar sentiment queries.
  • Deployed the platform on AWS EKS with autoscaling to manage 20M+ daily messages.
  • Stored classification outputs and metadata in ElasticSearch for rapid search and analytics.
  • Added Kibana dashboards for analyzing trending topics, negative surges, and agent performance.

Benefits

  • Scaled to 20+ million messages/day with low latency.
  • Enabled consistent real-time sentiment and conversation tagging across 11 countries.
  • Reduced manual tagging workload by more than 70%.
  • Improved client reporting accuracy for enterprise customers.
  • Provided predictive insights for high-volume negative conversation spikes.

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