When we first spoke with the client, their teams were facing a few major roadblocks that were slowing down customer service and making it tough to work with data effectively:
Support reps often struggled to give accurate answers because the data they needed was scattered across multiple systems.
Important customer and operational information was split between platforms, some structured, some not, making it hard to get a full picture.
Without real-time dashboards or flexible tools to explore data, it was tough for teams to get quick insights and act fast.
They weren’t tapping into generative AI yet, which meant slower responses, no smart summaries, and missed opportunities for tracking sentiment.
To solve these challenges, our team designed and deployed a fully integrated solution on Azure, bringing together AI, automation, and live data access.
We brought in both structured data, like CRM and inventory records, as well as unstructured content such as emails and PDFs. Azure Data Factory and Event Hubs handled the ingestion, and everything was centralized in Azure Data Lake Gen2. Processing was managed using Databricks for scale and flexibility.
Our engineers fine-tuned large language models—including GPT-4 and LLaMA 3, using Azure OpenAI and Azure ML. With Retrieval-Augmented Generation (RAG) and custom NLP pipelines, the models were trained to deliver highly relevant, context-aware responses tailored to the client’s domain.
We built a scalable chatbot layer using Azure Bot Services and connected it with APIs via Azure API Management. It was seamlessly integrated into both web platforms and Microsoft Teams, using React and Angular to ensure a smooth, unified user experience across channels.
We enabled users to ask business questions in plain English and get answers instantly. This was powered by Azure Cognitive Search and integrated directly with Power BI to serve up real-time dashboards and visual insights without needing any SQL.
To analyze customer calls, we used Azure’s Speech-to-Text along with Named Entity Recognition (NER) and Sentiment Analysis. These insights were stored in SQL and Cosmos DB, making them easy to track, review, and report on.
All deployments were managed using Azure DevOps CI/CD pipelines, ensuring a stable rollout. Azure Monitor and App Insights provided real-time performance tracking and continuous model improvement post-deployment.
User Interface & Integration
By introducing our AI-powered Customer Care CoPilot, the client was able to cut average query resolution time from 10 minutes to just 4, delivering responses almost 60% faster and improving customer satisfaction in real time.
With features like live call transcription, real-time sentiment analysis, and AI-suggested replies, support agents became much more efficient. These tools helped the team deliver responses more accurately and quickly, leading to a 50% improvement in overall efficiency.
With the natural language query system we built, team members could simply ask business questions and get visual answers instantly. This not only empowered faster decision-making but also reduced the need for manual reporting work by over 80%.
Manual summarization after customer calls was a huge time drain. By automating this process with AI, extracting key entities and summarizing conversations, the team saw a 70% drop in time spent on post-call documentation, freeing up agents for higher-value tasks.
Telecommunications
Asia-Pacific
End to End Project Lifecycle Management
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