Analytics & AI Transformation: Real-World Success Stories
Discover how NeenOpal empowers businesses with AI & Data-driven transformation.
16 case studies
Tableau Dashboard Development for a US Insurance Organization: 4 Data Sources Unified
Vishal SharmaOur client, an insurance organization with field teams spread across every US state, partnered with NeenOpal to replace a manual, spreadsheet-driven reporting process with a consolidated Tableau dashboard environment governed by row-level security. Operational data lived in four separate systems covering Airtable, Salesforce, Google Analytics 4, and Google Search Console, and bringing them together meant exporting and stitching the data by hand, which left leadership without a single reliable view of performance. NeenOpal consolidated all four sources into an Azure SQL Server layer, reviewed and corrected the data quality issues that surfaced along the way, and delivered three Tableau dashboards across seven screens that refresh automatically every day. Row-level security was implemented across all fifty states and the independent districts, driven by an entitlement model the client maintains through a single Google Sheet, so every representative sees the states they are responsible for and nothing more.
Read case study
Microsoft Fabric Migration with Row-Level Security for Multi-Client Analytics
Aashay MehtaOur client, a B2B financial wellness and HR technology company, where we have worked on their data that delivers reporting to multiple business customers, partnered with NeenOpal to consolidate a fragmented reporting stack into a single, secure analytics platform. The client's finance and user-engagement data lived across MongoDB, AWS RDS, Google Sheets, and HubSpot, and was surfaced through standalone Power BI dashboards built on AWS. Serving each downstream client meant securely embedding a per-user filtered dashboard for external users logging into the client's portal, and app-owns-data embedding at that scale requires dedicated capacity. On shared, standalone Power BI the team had worked around this by building and maintaining a separate dashboard and semantic model for every client, which increased infrastructure cost and slowed delivery. NeenOpal designed and delivered a four-week Microsoft Fabric migration that unified every source into one governed semantic model with dynamic row-level security. By rebuilding the pipeline on a medallion architecture inside a Fabric Lakehouse and enforcing access with a single Power BI security role, the client replaced many duplicate dashboards with one unified report that automatically shows each user only the data they are permitted to see.
Read case study
How a Leading Gold Loan Provider Eliminated Retention Guesswork with ML-Powered Churn Prediction
Rohit KannanOur client is a leading financial institution based in Sri Lanka, providing specialised gold loan products across a retail network of more than 50 branches and serving an active customer base estimated in the hundreds of thousands. The business model is built around short-term, collateral-backed lending, where customer retention at loan maturity directly determines portfolio revenue. Despite years of accumulated transactional data across five core SQL Server tables, the organisation had no mechanism to convert that data into customer-level insight. NeenOpal partnered with the client to build and deploy a machine learning churn prediction system, alongside a customer scoring model and Tableau dashboards, enabling the organisation to shift from reactive, instinct-driven outreach to precision-targeted retention.
Read case study
LLM Migration from OpenAI to Amazon Bedrock
Anish GangwalA leading marketing advisory specializing in wealth management and investor communications was running a core generative AI capability for AI powered, FINRA and SEC compliant email generation on OpenAI GPT models. While the solution delivered early value, growing concerns around data security, compliance governance, escalating API costs, and single vendor dependency created an urgent need to migrate to a more enterprise grade and sovereign AI stack.
Read case study
Enterprise Data Platform on Fabric for Secure, Scalable Analytics
Rajat Moundekar
Our client, a large enterprise organization, used Dynamics 365 Finance & Operations (F&O) as its primary ERP system for managing financial and operational processes. Although F&O handled transactional workloads effectively, reporting and analytics were constrained by limited flexibility, performance concerns, and a lack of centralized data access. To enable scalable analytics, governed reporting, and self-service BI, the client adopted Microsoft Fabric as a unified data and analytics platform.
Read case study
Transforming Tax & Wealth Management Onboarding with Microsoft Copilot Studio and Power Platform
Dhruv Chowdary
Tax and wealth management firms struggle with client onboarding due to manual processes that cause delays and errors. This case study illustrates how we utilized Microsoft Copilot Studio and Power Platform to automate and streamline onboarding. The solution reduces processing time, minimizes errors, and enhances client satisfaction through intelligent document processing and AI-driven investment recommendations.
Read case study
Dynamics 365 Finance Data Integration to Snowflake using AWS ETL
A well-established enterprise leveraged Microsoft Dynamics 365 Finance as its core system for managing general ledger, accounts payable, accounts receivable, and financial reporting. While the ERP system supported day-to-day accounting processes, the finance leadership team required a scalable analytics platform to enable faster financial close, historical analysis, and audit-ready reporting.
Read case study
Building a Customer Churn Prediction Model for a Leading Gold Loan Provider
Yash KhareA leading Non-Banking Financial Company (NBFC) in Sri Lanka was facing rising customer churn and lacked the tools to effectively identify and act on early signs of risk. NeenOpal partnered with the client to develop a machine learning–driven churn prediction model that accurately identified at-risk customers, enabling timely and personalized retention strategies.
Read case study
Developing a Credit Scoring Model for a Leading NBFC in Sri Lanka
Yash KhareOur client, a leading NBFC in Sri Lanka, partnered with NeenOpal to enhance its gold loan disbursement process by building a machine learning-based customer scoring model. Instead of traditional credit checks, the model enabled smarter loan-to-value (LTV) decisions, allowing the client to confidently offer higher LTVs to trustworthy customers based on historical gold loan and transaction behavior.
Read case study
Building a Scalable Financial Wellness Platform with AWS
Subhojit DeyThe client partnered with NeenOpal to develop a modern financial wellness platform tailored for individuals, families, and small businesses. The platform aimed to simplify money management across generations through features like account aggregation, budgeting tools, and multi-role access. NeenOpal brought this vision to life by building a scalable, secure, and insight-driven SaaS platform — powered by a serverless AWS architecture and designed for enterprise-grade reliability.
Read case study
Rescuing Cloud Infrastructure & Driving Long-Term Security with AWS Well-Architected Review
Akshat Agrawal
Just a week into a new partnership, a digital lending company contacted NeenOpal on New Year’s Eve due to a critical infrastructure outage. Their production systems had been down for over two business days, disrupting operations during a peak traffic period. NeenOpal’s DevOps team promptly intervened, restoring services within hours. This immediate resolution was followed by a comprehensive AWS Well-Architected Framework Review (WAFR) to identify underlying reliability issues and deliver a clear roadmap for building a scalable and resilient cloud infrastructure.
Read case study
Personalized Financial Categorization with AI-Powered SaaS on AWS
Navya AnimoneTraditional financial management tools struggle to personalize categorization at scale, often locking users into rigid category structures. NeenOpal, leveraging AWS and OpenAI, built a FinTech SaaS platform that integrates seamlessly with services like Plaid, Stripe, and Bill.com to automate income and expense categorization. By using lightweight LLMs, we enabled user-defined financial categories with real-time AI-powered classification, creating a personalized, scalable, and cost-efficient solution.
Read case study