It is a Peer-to-Peer Marketplace designed to streamline direct transactions between buyers and sellers, eliminating the need for intermediaries. The platform specializes in high-end men's fashion, focusing on luxury streetwear and vintage pieces that cater to discerning tastes.

An AI-first, Gen Z–oriented fashion styling app enabling users to upload wardrobe items, receive personalized outfit suggestions using AI, and engage socially via outfit polls and feeds. The solution leverages Firebase’s scalable cloud backend and integrates OpenAI for prompt-based outfit generation. Users can manage a virtual closet, receive AI alerts on missing wardrobe items, and interact with a community through polls and comments. It also includes an admin panel for moderation, analytics, and subscription management. Monetization is driven through subscription tiers and integrated ads. The product is designed as a progressive MVP with clearly defined “must-have” and “nice-to-have” features to facilitate phased rollout. Post-launch, the client aims to validate market fit and scale with affiliate shopping and influencer integrations.

Prospective property buyers often face delays in identifying suitable suburbs and properties, limited visibility into where they stand in the buying journey, and fragmented coordination between buyer's agents, support teams, and external parties such as conveyancers and inspectors. The client required enhancements to streamline client onboarding, automate workflow tracking across the buying process, and improve real-time communication across all stakeholders.
smartData enhanced the existing Milk Chocolate Property platform by optimizing the client and agent-facing Own™ portal with automated notifications, improved Game Plan™ workflows, AI-assisted suburb and property matching, and upgraded UX for registration, recommendations, and milestone tracking from exchange through settlement. The solution introduces structured client preference reuse, simplified onboarding, intelligent agent assignment tracking, and AI-assisted decision support to help clients evaluate suburb and property recommendations.
As a result, the platform improves operational efficiency, reduces lead-to-purchase turnaround time, enhances client engagement through proactive communication, and establishes a scalable foundation for future AI-driven property personalization and automated transaction coordination.

An AI-powered Quality Assurance (QA) tool developed to enhance the customer support experience by automatically analysing customer-agent conversations( both actual human agents as well as agentic bots too). It identifies policy violations, customer sentiment, and agent performance insights in real-time. Platform integrates with CRMs and uses machine learning (ML) and natural language processing (NLP) to offer actionable insights for businesses seeking to improve service quality and operational efficiency. This will help organisations to streamline support analysis, improve agent training, and monitor customer satisfaction metrics. 

Real estate agents spend a significant portion of their day on phone calls – following up with leads, booking property visits, and confirming client preferences. The client, a mid-sized real estate agency, was struggling with missed follow-ups, delayed responses, and scattered call logs.
To address this, we built a smart inbound-outbound calling agent that acts like a virtual assistant. Integrated directly with the client’s CRM and calendar, the agent automatically places calls, captures lead details, books appointments, and updates records in real time – all while keeping the human agent in control.

An AI phone agent for restaurants, designed to automate and enhance customer interactions over the phone. It handles orders, reservations, FAQs, and customer queries while seamlessly integrating with POS systems. With the ability to manage unlimited simultaneous calls, upsell intelligently, and provide real-time analytics, it reduces missed opportunities and improves customer satisfaction.