QBurst

QBurst is a digital engineering company that builds AI-driven solutions for enterprise clients across retail, real estate, healthcare, manufacturing, and logistics. The company positions itself around a framework it calls High AI-Q, which emphasizes infusing AI into product engineering, customer experience, and operations. The source material doesn't specify when QBurst was founded, where it's headquartered, or how many people work there—those details are absent from the website.

What is clear from their case studies and service pages: they work on concrete, measurable problems. For a Middle East–based multinational retail and hospitality group, QBurst built a multi-brand loyalty platform that unified 70+ brands, onboarded millions of customers, and reduced campaign rollout time by 30%. For a US discount retailer with 3,000+ stores, they replaced a legacy AS400 system with a .NET-based buyer portal that cut buyer task time by 35% and order errors by 40%. For a fashion retailer serving 20 million e-commerce users, they deployed machine learning models (Prophet, ARIMA, SARIMA) to forecast order-picking demand and optimize warehouse staffing, reducing labor inefficiencies and improving prediction accuracy by up to 30%.

Services and capabilities

QBurst organizes its offerings into five main buckets: Digital Experience (designing interfaces and human-machine interactions); Intelligent Enterprises (AI and data solutions for decision-making); Product Engineering (building digital systems with AI-first design); Modernization (legacy system overhauls); and Managed Agents (building, deploying, and managing AI agents to automate workflows).

On the retail side specifically, they offer omnichannel commerce platforms, order management systems, supply chain optimization with real-time visibility, dynamic pricing aligned to market trends and competitor activity, workforce management, and smart store solutions using IoT and automation. They've built proprietary accelerators like SlashQ (queue management and online booking), a Retail Pricing Accelerator, and automated order replenishment tools.

Their healthcare work emphasizes HIPAA compliance and includes process efficiency improvements, smart inventory systems for medicine management, diagnostics acceleration, and wearables analytics. Manufacturing solutions span deep learning for tire performance, facility management overhauls, and AI-powered mining operations reimagining. They also work in high-tech, energy and sustainability, logistics pricing intelligence, and insurance customer acquisition.

Technically, QBurst uses microservices architecture, React and React Native for front-ends, .NET for backend systems, Drupal CMS, cloud platforms (Microsoft Azure, Google Cloud Platform), Prophet and time-series forecasting libraries (ARIMA, SARIMA), Salesforce Marketing Cloud, SAP S/4HANA and MSTR for enterprise data, MongoDB for local caching, Talend for data integration, and Python-based ML stacks (Pandas, NumPy, SciPy). They mention using GCP Vertex AI for model training and BigQuery for analytics.

Notable work

The three case studies in the source material show the scope and specificity of their delivery:

Multi-Brand Loyalty Platform. A diversified Middle East multinational managing 70+ consumer brands across retail, hospitality, and services needed to consolidate disparate loyalty programs. QBurst built a cloud-native system on Microsoft Azure with microservices, React Native mobile apps, Drupal CMS for campaign management, and integrations to POS, payment gateways, Salesforce Marketing Cloud, and analytics. Results: millions of members onboarded, 70+ brands unified, 30% faster campaign rollouts, and recognition as the region's "Best Loyalty Program."

Retail Logistics ML. A fashion retailer with 3,000+ stores and 20 million e-commerce users faced inaccurate shipping forecasts and workforce misalignment. QBurst evaluated multiple time-series models (ARIMA, SARIMA, Prophet) on GCP Vertex AI and BigQuery, ultimately deploying Prophet as the most accurate. The system incorporated seasonality, holiday effects, and promotional data. Impact: up to 30% improvement in prediction accuracy, reduced overstaffing, improved on-time delivery.

Buyer Portal Modernization. A US discount closeout retailer relied on legacy AS400 systems that couldn't sync with SAP and MSTR, creating pricing errors and poor order accuracy. QBurst replaced it with a .NET portal on Azure DevOps, using Talend to sync data every 5 minutes to hourly from SAP HANA Sidecar to MongoDB, with dedicated read and write APIs. Results: 35% faster buyer task completion, 40% reduction in order errors, better inventory visibility.

How they work

The source doesn't describe their formal engagement model, pricing, or team structure explicitly. However, the case studies indicate they take on full-cycle projects—discovery, architecture, implementation, and ongoing support. They mention mentioning a "Prototyping as a Service (PaaS) model" for testing and refining AI UX ideas, suggesting they may offer prototyping alongside larger implementations. Their CEO, Arun 'Rak' Ramchandran, is quoted in media discussing human-centric AI design, indicating leadership visibility on client work. The company also mentions "ready-to-deploy accelerators" in retail and other verticals, suggesting component-based engagement options.

Team and credentials

The website states QBurst has global locations and a skilled workforce, but exact numbers aren't disclosed in the source material. The CEO is Arun 'Rak' Ramchandran. In March 2026, QBurst appointed Shivkumar Subramaniam to lead operations from Dubai, and in April 2026 it established a strategic hub in Silicon Valley. The company was included in IDC Market Glance reports for retail and loyalty solutions. No certifications (ISO, SOC 2, GDPR), partner badges, or third-party ratings (Clutch, G2) are mentioned in the source material.