Mobavenue Launches Neural Engine As An AI Intelligence Layer For Marketers

Running an advertising campaign often requires marketers to switch between multiple tools for planning, creative development, execution, and performance tracking. This fragmented setup can slow down campaigns and make it difficult for teams to act on insights quickly. 

The launch of the Mobavenue Neural Engine addresses this challenge. Embedded into its advertising platforms, the AI intelligence layer is designed to help marketers move from campaign planning to execution within a single system. 

The launch comes as AI adoption grows across advertising, with marketers increasingly using AI for tasks such as creative generation and campaign analysis.

“Marketing teams lose an extraordinary amount of time to fragmented workflows. Plan in one tool, launch in another, brief the creative team separately, then dig through dashboards to understand what happened,” Tejas Rathod, founder and CTO of Mobavenue, said in the company’s announcement.

Explaining the thinking behind the platform, he added:

“We built the Mobavenue Neural Engine to close that gap, collapsing the lifecycle into a single intelligence layer. For our customers, the difference is practical: less time managing the machinery of advertising, more time on strategy, creativity and growth.”

According to the company, the Mobavenue Neural Engine can take marketers from a campaign brief to launch in under 59 seconds by bringing media planning, campaign setup, creative generation, and reporting into a unified workflow. 

Mobavenue’s broader objective is to use AI to support advertising decisions across the campaign lifecycle instead of automating isolated tasks. 

Beyond AI Assistants: Building An Intelligence Layer

The company’s decision to combine conversational AI with its proprietary advertising intelligence also shaped the architecture of the Mobavenue Neural Engine. Instead of relying solely on a foundation model, Mobavenue has developed a proprietary decision layer that works alongside its conversational AI to support advertising decisions. 

“One of our biggest learnings was understanding where foundation models create value and where proprietary advertising intelligence needs to take over,” Rathod told Inc42.

Within this architecture, the language layer interprets campaign briefs, facilitates conversational interactions, and orchestrates workflows. 

The proprietary decision layer, meanwhile, uses Mobavenue’s advertising intelligence and first-party platform data to generate recommendations across media planning, budgets, audience selection, inventory and campaign optimisation.

These recommendations are presented to marketers for approval before a campaign goes live, allowing teams to retain control over key decisions. 

This architecture evolved over nearly eight months of development, during which the company rebuilt it multiple times before arriving at its current design. 

Inside The Mobavenue Neural Engine

The Mobavenue Neural Engine begins with a campaign brief, which marketers can enter in plain language, paste from an existing document, or provide through a product URL. It then analyses the brand, product, business category, and campaign objective before generating a media plan. 

The plan includes recommendations on budgets, targeting, channels, frequency caps, and cost-per-mille (CPM) benchmarks.

Once the media plan is ready, the engine moves to campaign setup. It populates campaign settings, recommends brand-safe inventory, and generates creatives in the required formats. It also runs checks covering budget consistency, targeting parameters, campaign dates, geography, and brand safety.

After the marketer approves the media plan, inventory, and creatives, Mobavenue claims that the campaign can go live in under 59 seconds.

The transition from planning to execution takes place within Mobavenue’s demand-side platform (DSP), which handles campaign setup and delivery. The media plan generated by the Mobavenue Neural Engine becomes the campaign’s foundation, taking approved recommendations from planning to launch without manual copying, exporting, or transferring between tools.

The recommendations are informed by three layers of intelligence: Mobavenue’s proprietary advertising intelligence, the advertiser’s own historical campaign data, and real-time business context. 

This allows the engine to consider previous campaign performance and execution patterns, along with information about the brand, product, and campaign objective, when generating its recommendations. 

The decision-support system continues to operate after a campaign goes live. The engine can compare campaign delivery with the original plan, flag areas requiring attention, and recommend corrective actions. 

According to Mobavenue, early deployments have included customers in sectors such as quick commerce and fintech. The company said that while teams initially use the platform to create and launch campaigns, its ability to monitor performance and surface areas for optimisation is also becoming an important use case.

The company now plans to extend the Mobavenue Neural Engine’s continuous optimisation capabilities. Mobavenue wants the platform to identify emerging issues and opportunities, surface them to marketers, and recommend actions as campaign performance changes. 

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