Mobavenue, an adtech company, is transitioning from traditional ad optimisation to using AI as a decisioning layer across the marketing lifecycle. The company processes over 1.25 billion data signals daily and delivered 42.72 million outcomes in FY26, enabling its AI models to continuously learn and improve. This shift aims to better predict user intent and optimise media spending beyond clicks or installs, focusing on actual business outcomes, according to Mobavenue founder and CTO Tejas Rathod, as reported by inc42.com.
Mobavenue’s platform is built around its Neural Engine, which connects signals, user intent, and campaign outcomes in real time. Instead of treating clicks or installs as endpoints, the system learns from downstream behaviour such as whether a user completes KYC or makes a purchase. This continuous feedback loop allows the AI to inform future audience targeting, bidding, and budget decisions. Rathod highlighted that this approach addresses advertisers’ demand for clearer links between media spend and business results.
This development reflects a broader trend in digital advertising where AI is evolving from a tool for optimisation to a comprehensive decisioning system. Traditional metrics like click rates or app downloads often fail to predict meaningful business outcomes, especially in sectors like fintech. Mobavenue’s approach of integrating intent prediction and outcome learning positions it alongside other AI-driven adtech firms aiming to improve marketing efficiency and accountability.
Mobavenue’s Neural Engine processed 42.72 million outcomes in FY26, providing a substantial data base for its AI models to refine decision-making. The company’s focus on real-time decisioning and continuous learning marks a notable advancement in adtech capabilities, as detailed in the interview with Tejas Rathod on inc42.com.