Scaled Inference: An Innovative Autonomous Optimization Platform for Business Personalization

Artificial intelligence (AI) and machine learning are game changers for business optimization. These advanced technologies are enabling organizations to achieve the level of quality and efficiency never imagined previously. A company which leverages AI and machine learning to help businesses tackle their most complex problems, improve process effectiveness and efficiency is Scaled Inference.

Scaled Inference is the industry’s first autonomous optimization platform that delivers personalized experiences at scale to drive sustained exponential business growth. The company’s mission is to make the full power of AI accessible for all. Its flagship product is the first autonomous optimization platform that delivers personal experiences at scale to drive exponential business growth. Powered by AI, eliminates guesswork by continuously learning and improving, empowering enterprises to move beyond the limitations of experimentation.

Product and business leaders choose for its simplicity and ability to leverage scale for realizing key growth opportunities. It enables them to move beyond the limitations of experimentation. continuously learns and improves to eliminate the inhibiting guesswork that comes with A/B testing. works in two stages that are analogous to traditional analytics and A/B testing but focused on analyzing and optimizing contextual behavior and performance.

•  Analyze observer events and properties to learn the key contexts (segments) that matter to users given their key metrics. Explores contextual performance metrics to help design new and better actions that enable even faster and better optimization.

• Optimize enables software to take better actions by continuously learning and deploying ever-improving policies to the software. It explores contextual action policies for deep insights and full transparency into what the application is doing and why (i.e. better metrics).


Distinguished Leadership

Olcan Sercinoglu is the Founder and CEO of Scaled Inference. Before founding Scaled Inference, Olcan was an engineer in Google Brain, where he worked on efficient inference methods for general-purpose statistical models and task-specific adaptations that can utilize deep learning. Early on at Google, Olcan led several core platform projects that now power virtually all major Google services including Web Search. He later developed the on-demand statistical analysis system that powers Google Trends and subsequently spent many years working on web-scale statistical inference systems at Google Research while also studying AI at Stanford (MS) and UC Berkeley (Ph.D.).

Prior to joining Google, Olcan built a popular (10M+ page views/month) music search engine and a web analytics platform as side projects during his undergraduate studies at Washington University in St. Louis, where he graduated valedictorian with a BS in Computer Science. Olcan is also an avid tennis player who rates himself “a solid 4.0-4.5” on the NTRP scale.


Better Business Outcomes with AI and Machine Learning

Some of the world’s top website operators and application developers trust to optimize all their traffic and scale without limitation. Through the power of AI and machine learning, drives quality improvements & growth in products/services by optimizing key metrics that users define.

Integration is similar to setting up an A/B test, but unlike a test, it is able to guarantee results and offer performance-based pricing.’s use cases include landing pages, signups/onboarding, purchases/subscriptions, cancellations, notifications/emails, cross-promotions, engagement, retention, pricing, offers, etc. Here are some of the unique features offers:

•  Delivers continuous AI-driven optimization with the simplicity of A/B testing and convenience of a service with nothing to deploy or maintain.

•  Starts learning within seconds and can deploy learned improvements within minutes. Learns even faster with more impactful actions and relevant context.

•  Takes full advantage of the contextual wins in your work to consistently achieve metrics and trade-offs that are simply not possible with A/B testing or other traditional methods.


Keeping Clients Happy

Customer feedback is an important function of the growth process and helps Scaled Inference to serve them better. Here is what the customers have to say about the company:

“Marketing is rapidly moving towards hyper-personalization,” said Steven Wong, CMO at Ready State, a marketing agency that serves Google, Airbnb, and Cisco. “Scaled Inference’s AI capabilities take advantage of the many variant options available to automatically optimize experiences and metrics.”

“Effective optimization is crucial for success in the marketplace. We strongly recommend all developers use to complement their ASO and SEO efforts with improved on-boarding, engagement, and retention”— Aykut Karaalioglu, Founder & CEO, Mobile Action.


Overcoming Business Challenges

Olcan thinks that any time a company introduces a new concept, there is some initial resistance or inertia. “Essentially, we are making the case for enterprises to change their behavior. This process typically takes time and nurturing. The challenge for Scaled Inference is that it is not only building a cloud platform for machine learning intelligence but also trying to figure out how to distill what have up to now been very specific applications into general-purpose services. This first set,” said Ocan, will “serve as a foundation for various special-purpose services that we also plan to offer in the future.”


The Future Looks Promising

Scaled Inference believes that the power of AI is enabling a new set of technologies to existing that solve the problem of A/B testing. A/B testing has a lot of guesswork. eliminates the guesswork.

The company partners with product and growth leaders to optimize the key metrics of a business. Its core AI technology stack makes us innovative.

“AI solves the problem of A/B testing. We can use AI to drive sustainable and quality growth at a scale A/B testing cannot reach. A/B testing will become a thing of the past. The era of self-optimization is here,” comments Olcan.