Machine Learning-Powered Optimization Platform

Scaled Inference provides a next-generation optimization platform called fuels both growth and the productivity of growth teams.

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Easy to set up, optimizes key metrics by matching the most relevant variation to each audience segment.


Faster starts learning within seconds and deploys learned improvements with impactful actions.

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Better, delivers 10x ROI as compared to leading A/B Testing and Multivariate Testing platforms.


Web and mobile applications are frequently changing as platform leaders and marketers test features, layouts, images, new messages, and more to drive growth. is their solution for growth optimization. uses machine learning to deliver the best variant to each customer segment. The learned segments are revealed allowing the Growth Teams to use these insights to design new variants. With, both conversions and the Growth Team's performance are optimized.

How It Works

The machine learning-powered optimization platform performs the following functions:

Smart client libraries
  • Available for multiple languages and platforms (web/mobile/server-side)
  • Equipped with real-time, contextual, decision-making capability under tight latency constraints
  • Transparent model and policy configuration management (continuous policy updates sans human intervention)
  • Compatible for cross-device sessions
Big data processing pipeline
  • Data pipelines equipped to handle high data rate ingestion
  • Built-in sanitization for malformed/unusable data
Real-time analytics
  • Discover contextual segments you didn't know existed and serve them tailored experiences
Continuous optimization through reinforcement learning
  • Human-readable, contextual policies are learned and deployed continuously
  • Multi-metric optimization is supported out-of-the-box
  • Optimization is robust to missing/noisy/sparse data
  • Built-in concept drift/non-stationary detection and adaptation
Intelligent orchestration
  • Allocation control to set the percentage of sessions optimized
  • Decision points can be seamlessly added or removed in sessions
  • Metrics can be seamlessly added, altered or removed
  • Transparent data backfills for historical sessions, when new metrics are created

Management Console

The management console is the command center for configuring and monitoring your growth projects.

  • Real-time analytics: provides live tracking and data monitoring. Metrics can change continuously during the course of a session and are not restricted to be point-in-time rewards like most machine learning systems.
  • Session and metric time series views of all sessions:
    Baseline, Optimized (exploit), and Amp'd (explore + exploit)
    sessions. Data can be filtered and summarized.
  • Performance breakdown of variants
  • Contextual segments by variant
  • Advanced diagnostic tools for debugging integrations

Start Using in Three Steps is straightforward to use and integrates easily with your applications and data sources. Our experts in maching learning and autonomous optimization will work with your growth experts from design through optimization to ensure success.



Create your project and connect your application.



Hypothesize, ideate and design variants. Identify decision points, metrics, and traffic allocation.


Observe and Optimize immeidately starts to learn and deploy contextual action policies to your application.

Step 1 - Integrate

Once you create your account and project, you're ready to connect your application using the project key and client libraries. For more on integration, read these blog posts: browser client integration, mobile integration, server-side integration, and integration with A/B testing system.

Step 2 - Design

Good design is essential for all growth experimentation including optimization projects. Using our design framework, we will collaborate with you to design your project. During the design phase, you will indentify the decision point, metrics, and variants.

For more information on how to design autonomous optimization projects, read this blog post.

Step 3 - Observe and Optimize

As in life, context matters. Before starting optimization, you can choose to have observe your traffic and provide insights about the contextual segments that are under and over-performing. You can then use these insights to design better variants even before you start to optimize.

Once the decision point, metrics and variants are configured, traffic is allocated to baseline sessions for measurement, and Amped sessions for learning and optimization. As learns, it delivers the optimal variant in every optimization session. The optimization gains delivered by are displayed live on the console.
Partner in growth

Partners in Growth

We believe that expert ingenuity will continue to drive innovation and that experts will continue to make the most strategic decisions, including what AI can do for them. Therefore, the key to winning in the AI age will be for experts to think beyond automation, to ensure that AI can inspire their best ideas and inform their most critical decisions. By the same token, real-time continous optimization cannot be accomplished without delegating some decisions to machines.

That's where comes in. A complete solution for driving web and mobile application growth marries experts and machines. To ensure our customers have the support to take full advantage of our product, we provide customers with services from our own experts in growth design, machine learning, data, and data science. Our team and platform are your partners in growth.