R&D

    Government-Backed Research

    TÜBİTAK-TEYDEB Supported Projects

    The following projects are carried out under the TÜBİTAK Technology and Innovation Support Programs (TEYDEB).

    01TÜBİTAK-TEYDEB

    Project No

    3255153 - 1707

    Development of an AI-Powered Pre-Season Planning Software for Demand Forecasting and Inventory Optimization

    An AI-powered planning solution for pre-season demand forecasting and inventory optimization.

    02TÜBİTAK-TEYDEB

    Project No

    7220609 - 1507

    Development of Machine Learning-Based Decision Support Mechanisms for Pricing and Campaign Management Using Forecasting and Optimization Algorithms Specific to the Retail Sector

    A machine learning-based decision support system developed for discount optimization in the retail sector.

    Disclaimer: These products have been developed with the support of the TÜBİTAK-TEYDEB Support Program. All responsibility regarding the products/services belongs to rnv.ai. The fact that TÜBİTAK has provided financial support to the project does not imply that the project has been carried out by or in collaboration with TÜBİTAK.

    Research & Development

    Pushing the Boundaries of
    Retail AI Innovation

    At RNV.ai, our R&D team continuously explores cutting-edge AI and machine learning techniques to deliver smarter, faster, and more accurate retail planning solutions. Every algorithm we ship is backed by rigorous research and real-world validation.

    Multi-Agent AI Systems

    We design autonomous AI agents that collaborate to solve complex retail planning challenges — from demand sensing to allocation optimization.

    Demand Forecasting

    Our proprietary forecasting models leverage deep learning and ensemble methods to predict consumer demand with industry-leading accuracy.

    Graph-Based Optimization

    We use graph neural networks to model store-product-customer relationships, enabling smarter inventory distribution across complex retail networks.

    Reinforcement Learning

    Our RL-driven pricing and markdown agents learn from real-time market feedback to maximize revenue while minimizing overstock waste.

    Our Technology

    Built on World-Class AI Infrastructure

    Deep Learning

    Transformer architectures adapted for time-series retail data

    Big Data Processing

    Real-time processing of millions of SKU-store combinations

    MLOps Pipeline

    Automated model training, testing, and deployment at scale

    A/B Experimentation

    Rigorous experimentation framework for continuous model improvement

    Generative AI

    LLM-powered insights and natural language reporting for retail teams

    Explainable AI

    Transparent decision rationale so retail teams trust and act on recommendations

    From Research to
    Production-Grade AI

    Our R&D pipeline ensures that every innovation goes through rigorous testing before reaching our customers. We validate models against real retail datasets, run extensive backtesting, and monitor performance continuously in production.

    Peer-reviewed research methodologies
    Backtesting against 3+ years of historical data
    Continuous model monitoring and retraining
    Privacy-first approach to data handling
    R&D Pipeline — Active
    1
    Research & Hypothesis
    2
    Data Collection & Prep
    3
    Model Development
    4
    Backtesting & Validation
    5
    A/B Testing
    6
    Production Deployment

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