Meet Juno: Jampp’s new AI-powered tech stack

Built for ROI. Fueled by AI.

Over the past months, AI has given us a major leap in decision speed, signal interpretation, and computing power—and we’ve harnessed this advantage to upgrade our growth platform. The result: a faster learning curve, better predictions, and ultimately higher ROAS.

Juno (Jampp Unified Neural Optimization) is the culmination of that work—Jampp’s most advanced tech stack to date. It knows when to move faster and when to think deeper so you can scale efficiently without sacrificing quality.

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What’s changing

Many ad stacks “learn, then act.” Juno does both concurrently,  filtering signals, inferring user intent and value, and optimizing results in parallel from install to conversion—like an air-traffic control tower sequencing arrivals and departures across multiple runways, rerouting around weather, and keeping everything on schedule—simultaneously.

This isn’t just an upgrade—it’s a re-architecture of our decision stack: new DNN models, updated training pipelines, and real-time budget reallocation. In practice, Juno focuses on two high-impact levers for scale and ROI:

• Pre-bid efficiency: Improve every decision before entering the auction by filtering noise, modeling user value, and targeting only the most promising opportunities.
• Concurrent optimization: Adapt instantly to changes in audience behavior, inventory quality, and pricing—turning every shift into an opportunity for more conversions at better budget efficiency.

Let’s dive deeper into each of these pillars 👇

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Pre-bid efficiency

Before we even bid, Juno raises signal quality and makes your budget work harder toward your goal, building an advantage where others waste spend.

• Accelerated learning phase: Deep Neural Networks (DNNs) trained on aggregated auction signals scan massive raw datasets to filter noise, fraud, and low-value users before any paid exploration occurs. Because only high-quality signals enter the system, the cost and duration of the learning phase drop—ramping campaigns up to 50% faster and with the potential to cut exploration costs by 40%. 
• Extended user lifetime value: We map your app’s funnel to consider multiple goals from day one and interpret behavioral signals to make smarter LTV predictions. As a result, we concentrate spend on higher-quality users with the potential to compound value over time. Our new models leverage high-signal events (e.g., registrations) as early indicators to act faster, and cross-reference time-to-conversion with funnel progression to make the right call at each stage of the user journey. ‍
• AI-curated supply: We’ve re-audited our exchange integrations to ensure we’re connected to the highest-quality partners. Our AI routing system scores exchanges, apps, and placements for conversion density and cost-efficiency, flagging anomalies that could indicate potential fraud or invalid traffic. For an extra layer of efficiency, we pause underperforming supply within the first hour, block wasting exploratory traffic, and resume only when early signals recover.

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Concurrent optimization

Juno learns, delivers, and optimizes simultaneously—so you can scale efficiently even when conditions are changing:

• More conversions per dollar: Our platform learns and re-adapts to auction dynamics in real time. We leverage Gradient Boosting Decision Trees (GBDTs) to instantly score each impression, while controllers grounded in historical log data bring in long-run learnings. This recalibrates our pre-bid predictions, so results hold and scale even when the market moves. When prices spike, bidding automatically re-prices at the user level and reroutes spend to higher-yield segments, identifying users 30× more likely to convert.
• Real-time budget reallocation: We’re introducing ROED++ (Realistic Opportunity with Efficiency Decay & User Potential), a patent-pending in-house metric built for real-time bidding. At its core is a confidence score that weights efficiency, saturation, user potential, and data reliability—so the system knows where the next dollar’s marginal return is highest. Juno then reallocates spend in real time—exiting saturated pockets and leaning into high-converting segments with ~10× stronger performance. ‍
• Privacy-native iOS performance: Our industry-recognized privacy-first platform is now pushing SKAN and AdAttributionKit campaigns even further with new models for iOS. We maximize every signal from aggregate postbacks by applying advanced modeling to source IDs and conversion values. This allows us to extract far more learnings from limited data inputs—accessing up to 100× more granularity—and unlock ~15% more events in the early stages of iOS campaigns.

“Juno is the result of years of productizing AI research into a system that’s robust, reliable, and proven in production. Every change we ship goes through rigorous experimentation—results have to be statistically sound and repeatable before they make it into the bidder, so we can guarantee stability with zero downtime. With concurrent optimization, the system lowers latency, adapts faster to dayparting and price shifts, and delivers more stable ROI.” —Charles Yong, Chief Architect & Technology Officer at Affle

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Wrapping up: How Juno changes the mobile growth game

Juno rewires how decisions get made—before the bid and while campaigns run:

• Faster ramp-up: Our DNN pre-bid quality gate cuts exploration costs and reaches stability up to 50% faster.
• Progression beyond installs: Campaign-level, multi-event optimization advances users from install to long-term retention.
• Budget where it performs: ROED++ reallocates in real time, leaning into high-converting windows.
• Privacy-native iOS gains: Advanced use of source IDs and conversion values delivers enhanced granularity, driving more early events.

Our new stack is already helping the leading mobile businesses across the globe unlock stronger ROI across their User Acquisition and App Retargeting campaigns. Now it’s your turn! Contact us to speak with one of our experts and learn how Juno can help you scale your mobile business.