Tech startups operate in fast-changing markets where customer preferences evolve quickly and competition is intense. Yet many startups struggle to continuously improve their products because they are unable to effectively capture, analyse and use customer feedback in real time. Traditional feedback systems are often slow, fragmented, and manual, making it difficult for startups to translate user insights into meaningful product innovation. As a result, many promising startups fail not because of poor ideas, but because they lack intelligent systems that can transform customer feedback into continuous innovation. This study addresses this problem by examining how an integrated Artificial Intelligence (AI) and Natural Language Processing (NLP) model can enhance product feedback loops and support continuous innovation in technology startups. Using a systematic literature review approach guided by the PRISMA framework, the study synthesizes existing research on AI-driven analytics, NLP-based text analysis, feedback systems and startup innovation between 2014 and 2023. The findings reveal that AI-driven tools enable startups to analyse user behaviour and predict customer needs, while NLP techniques help interpret large volumes of textual feedback from reviews, social media, and support channels. When integrated into structured feedback loops, these technologies allow startups to detect user concerns, identify emerging trends, and implement product improvements more efficiently. The study further shows that effective feedback loops powered by AI and NLP serve as a foundation for continuous innovation, enabling startups to adapt quickly, improve customer satisfaction, and sustain competitive advantage. The study concludes that AI- and NLP-driven feedback intelligence is no longer optional but essential for startups aiming to survive, scale and innovate continuously in today’s digital economy. This study contributes to the literature by developing an integrated conceptual f