Intelligence as a Service.
Qommerce is a full-scale autonomous retail orchestration layer. While many AI tools simply provide recommendations, Qommerce is designed to act on them with minimal human intervention — moving from "Insight as a Service" to "Intelligence as a Service."
The "secret sauce" is controlled autonomy. Most retail AI provides a "leaky" experience where the user is eventually redirected to a brittle website or app to finish a task. Qommerce keeps the entire journey — from "help me plan" to activation and proof — within a single, orchestrated AI flow.
Goes beyond traditional reporting by automatically discovering insights, predicting trends, and identifying hidden patterns. Processes spatial data and shelf-intelligence to provide a granular view of brand visibility and market share in real time. Customisable dashboards with drag-and-drop widgets, personalised KPIs, role-based views.
Visual AI feeds into Conversational AI interfaces, allowing stakeholders to query complex datasets through natural dialogue — humanising big data for faster decision-making. Customisable with your business terminology and processes.
Identifies "white spaces" — prime shelf locations or high-traffic zones where your brand is absent but projected to thrive — and "red spaces" where urgent intervention is needed. Generates a precise task list for field agents, briefings for creative agencies, and serves the resulting hyperlocal advertising on-platform.
The central nervous system of the platform. Coordinates predictive analytics, NLP, computer vision, recommendation engines, and custom models from any provider. Built-in error handling, automatic retries, fallback mechanisms, and real-time monitoring.
Common questions.
Structured and unstructured data from any source — including customer interactions, sales data, social media, web analytics, IoT sensors, and any third-party datasets. Qommerce connects to 200+ data sources, including databases, cloud platforms, APIs, and file systems, through universal connectors and real-time data pipelines.
Our AI models achieve 85–95% accuracy in most business forecasting scenarios, with continuous learning improving accuracy over time as more data becomes available.
Yes — with proper permissions, the AI Assistant can securely access and analyse your business data to provide contextual insights, generate reports, and make data-driven recommendations.
Access the AI Assistant through the web interface, mobile app, Slack/Teams integration, API, or embedded widgets within your existing business applications.
Yes — build custom workflows using a visual editor with drag-and-drop components, conditional logic, parallel processing, and integration with existing business processes. Built-in error handling, automatic retries, fallback mechanisms, and real-time monitoring keep workflows running smoothly even when individual components fail.
Real-time dashboards show workflow performance, model accuracy, resource utilisation, error rates, and business-impact metrics with detailed analytics and alerting. The platform continuously learns from performance data to optimise model selection, resource allocation, and workflow routing.