Case Studies

Problem

A leading financial services company was struggling with manual data entry and reconciliation processes, leading to high costs, errors, and slow turnaround times.

Solution

We developed a custom AI-powered automation solution that uses machine learning and natural language processing to extract data from various sources, validate it, and automatically reconcile it with their internal systems. The solution also includes a dashboard for monitoring and exception handling.

Results

The solution reduced manual data entry by 95%, saved over 20,000 work hours annually, and improved data accuracy to 99.9%.

Tech Stack

Problem

A rapidly growing online retailer's existing e-commerce platform could not handle the increasing traffic and sales volume, resulting in frequent crashes and lost revenue.

Solution

We re-architected and rebuilt their e-commerce platform from the ground up using a modern, scalable, and cloud-native architecture. The new platform features a microservices-based backend, a headless CMS, and a progressive web app (PWA) for the frontend.

Results

The new platform can handle 10x the previous traffic, has a 99.99% uptime, and has led to a 30% increase in conversion rates.

Tech Stack

Problem

A large financial institution's employees had difficulty finding accurate and up-to-date information from their vast internal knowledge base, leading to inconsistent customer service and compliance risks.

Solution

We implemented a Retrieval-Augmented Generation (RAG) based knowledge assistant that allows employees to ask questions in natural language and receive instant, accurate answers from the company's internal documents and policies. The assistant also provides links to the source documents for verification.

Results

The knowledge assistant reduced the time employees spend searching for information by 80%, improved first-call resolution rates by 25%, and ensured consistent and compliant answers to customer inquiries.

Tech Stack