InMobi has introduced Mobius, a unified intelligence system designed to coordinate advertising decisions across its exchange, demand-side platform (DSP) and advertising surfaces on Glance. The system evaluates eligible advertising opportunities against advertiser-defined outcomes, determines which option should run, and feeds measured campaign results back into subsequent decisions.
The launch reflects a broader shift in programmatic advertising toward AI systems that do more than optimize individual bidding or targeting variables. The Interactive Advertising Bureau (IAB) has identified autonomous media execution, decision-making agents, measurement and attribution, and privacy governance as emerging components of agentic AI in advertising.
Market Landscape
Programmatic advertising increasingly requires systems to coordinate audience, supply, creative and outcome signals within increasingly short decision windows. InMobi says Mobius processes an average of more than 25 trillion predictions daily to support more than 200 billion advertising opportunities per day.
The system draws on several categories of signals. InMobi’s zero-party data comes from its consumer survey product, while first-party signals are generated through properties including 1Weather and Glance. Third-party inputs include data from more than 70,000 SDK-integrated publisher applications, pixel data across InMobi Exchange and DSP, licensed datasets and ecosystem partnerships.
That architecture puts data governance alongside optimization. InMobi says permissions travel with the underlying data, limiting which signals Mobius can consider for a particular advertising decision.
The approach also addresses a measurement problem facing AI-driven advertising systems. IAB’s 2026 measurement work highlights fragmented data, signal loss and inconsistent outcome definitions as challenges for advertisers attempting to connect media exposure with business results.
How Mobius Changes Advertising Decisioning
Mobius starts with the advertiser’s desired outcome and coordinates audience, supply and creative inputs around that objective. Before a campaign runs, advertisers can establish the KPI, measurement partner, attribution methodology, holdout and evidence window.
The system then compares measured results against its forecasts and feeds the resulting evidence into future decisions. This creates a decision trace showing what ran and the rationale behind the selection.
For advertisers and agencies, that architecture could consolidate planning, activation and optimization within a common decision layer rather than treating targeting, supply and measurement as separate processes.
InMobi cites a LinkedIn learning-content campaign and a Grocery Outlet Founders Day campaign as examples. In the latter, the company says the 12-day CTV and mobile campaign reached 8.6 million unique shoppers, while its interactive unit recorded a 6.6% wipe-through rate and its AI Companion generated 35% engagement. These are company-reported campaign results rather than independent performance benchmarks.
Strategic Outlook
The strategic implication extends beyond DSP optimization. Mobius is intended to operate across InMobi’s supply and demand infrastructure, with the same intelligence layer also designed to support publisher yield and traffic shaping for DSP partners.
That creates a more vertically integrated model in which decisioning can span multiple parts of the advertising transaction. The opportunity is greater coordination; the challenge is ensuring that advertisers can understand, validate and govern decisions made by increasingly autonomous systems.
IAB’s recent work similarly points toward greater transparency as AI assumes responsibility for planning, bidding, optimization and measurement across programmatic media.
Top Insights
- Unified decisioning: Mobius connects exchange, DSP and owned advertising surfaces through one intelligence layer.
- Outcome-first optimization: Campaign decisions begin with advertiser-defined objectives and measurement rules.
- Data governance: Permission constraints are incorporated into which signals the system can evaluate.
- Closed learning loop: Measured outcomes are fed back into subsequent advertising decisions.
- Full-stack implications: The architecture extends beyond advertisers to publisher yield and DSP traffic management.
Get in touch with our Adtech experts
