Telecommunications
Asia-Pacific
End to End Project Lifecycle Management
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.
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DataToBiz is a good choice for someone who wants a partner who understands the product journey, requirements, and delivery expectations. They have the experience and agility to understand what’s possible and deliver to the client’s expectations. They‘re really passionate people, and they know what they’re doing!
We’re impressed with DataToBiz’s professionalism, dedicated support, and consistent follow-ups. Their team maintains clear communication through online meetings, ensuring a smooth and reliable collaboration.
A solution like this is a good fit if your teams spend more time collecting and reconciling data than acting on it, if reports from different departments do not match, or if decisions depend on delayed or manual reporting. It also fits when your data is growing in volume or sources, and your current setup struggles to keep up.
DataToBiz starts with a discovery call to review your goals, data sources, and reporting gaps. You get a clear view of the expected outcomes, effort, and timeline before any commitment.
Yes. Every solution DataToBiz delivers is configured around your industry, business processes, and existing data environment. The team assesses your current systems, data quality, and reporting needs first, then designs a solution that works with what you already have instead of replacing it. This applies across industries such as manufacturing, retail, healthcare, finance, and logistics, and across setups ranging from legacy databases to cloud data platforms.
DataToBiz works across the major analytics, cloud, and data engineering stacks. For BI and visualization, this includes Power BI, Tableau, and Looker. For cloud platforms, it includes Microsoft Azure, AWS, and Google Cloud. For data warehousing and processing, it includes Snowflake, Databricks, and SQL-based databases. Data sources include ERP and CRM systems, spreadsheets, APIs, IoT and sensor data, and third-party applications.
If your stack includes a tool not listed here, the team can assess compatibility during discovery.
Most projects with DataToBiz take from a few weeks to a few months. The timeline depends on how ready your data infrastructure is for the initiative and how many systems need to connect. Smaller solutions go live quickly, while larger programs roll out in phases so you see value early. After an initial discovery call, you receive a timeline with clear milestones.
Security and governance is part of the plan from the first conversation. DataToBiz works within your security policies and follows the regulations that apply to your industry. The project usually Access stays limited to the people who need it, and confidentiality terms cover the engagement from kickoff to handover.
The work does not stop at go-live. Your team gets a proper handover and guidance on using the solution, and DataToBiz stays available for questions and improvements. The business impact of the project is measured against the goals set at the start, so you can see exactly what changed for your company.
