AI Decisioning: Mobavenue’s Tejas Rathod Explains the Shift
Mobavenue is shifting from ad optimisation towards AI decisioning, targeting the gap between immediate media metrics and business outcomes. The company processes more than 1.25 Bn data signals a day and delivered 42.72 Mn outcomes in FY26, using those outcomes to improve future audience, bidding and budget decisions.
Why Mobavenue is building an AI decisioning layer
Founded by CTO Tejas Rathod, Mobavenue has built its advertising technology around real-time decisioning, privacy-conscious signals and continuous learning. Its Neural Engine connects signals, intent and outcomes instead of treating a bid, click or app install as the endpoint.
Rathod said the company moved its core decisioning from rule-based and traditional machine learning systems to a unified neural network architecture built around processing signals, predicting intent and producing outcomes. “The biggest shift is the movement from automation towards predictive intelligence and AI autonomy,” he said.
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The platform is designed to predict whether a user will complete the outcome an advertiser values. In a fintech journey, that could mean moving beyond an app install to KYC completion, investment or loan conversion. “The objective is not simply to identify a user who may click or install, but to predict the probability of completing the business outcome that the advertiser values,” Rathod said.
A high click rate cannot show whether a fintech user will complete KYC, just as an app download does not promise a purchase. Mobavenue uses pre-bid and post-bid invalid-traffic detection capabilities, with the objective of protecting media efficiency and the quality of outcomes.
Rathod said Mobavenue wants its intelligence layer to connect awareness, acquisition and activation, with each stage informing the next. He said he would like Mobavenue to be recognised as a technology company spanning adtech, martech and monetisation technology.
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