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AI-Enabled Consumer Intelligence for US Technology Leader

Hi-Tech Solutions

AI-Enabled Consumer Intelligence for US Technology Leader

AI-Enabled Consumer Intelligence for US Technology Leader

Success Story

Scalefocus leveraged our AI adoption framework to quickly assess and deliver tangible results through AI that helped our customer’s consumer intelligence platform identify emerging microtrends in real-time. The mission that the AI solution helps to pursue is to give their clients – massive worldwide brands of various industries, a competitive advantage by capturing, processing, and analyzing vast volumes of social media, online, and offline consumer signals.

To be able to derive actionable insight, the platform had to reach a whole new level of agility and precision while constantly upgrading the AI models against an ever-changing data landscape.

Scalable and
efficient MLOps

Continuous improvement of AI models

Enhanced User Experience

The Client

A US technology leader that utilizes Artificial Intelligence (AI) and big data to help the world’s largest brands and public entities understand trends and narratives at scale across any source, market, and industry. Their intelligence amplification platform harnesses Machine Learning (ML) techniques and human metadata to analyze and predict consumer behaviors.

The Challenge

  • Collect and Process Vast Data Streams

Our customer’s AI solutions effectively gather and process large volumes of diverse data streams from various sources, including social media, like X and Instagram, online and offline interactions, to capture comprehensive consumer signals. They define what is becoming trendy, what the reasons are, and how likely it is to attract even more interest. All this sizeable data had to be segmented by locations and product types to recognize trends that are likely to occur in the respective regions, build efficient strategies, and facilitate tailored marketing campaigns. 

  • Data Quality and Noise Reduction

Quality control is pivotal with that many data streams from different sources and tens of millions of consumer signals involved. In a business where there is hardly any margin for error, the sheer quantity of information meant there were countless scenarios where precision could be compromised. That is why our customers and the Scalefocus team needed to address the challenge of ensuring data quality and reducing noise in the collected data, as poor data quality can lead to inaccurate insights and false positives. The sheer quantity of data assessed meant that many aspects of the analysis had to be left-shifted and automated. Some of that automation was done using AI.

  • Signal Identification and Interpretation

Developing algorithms and models capable of identifying relevant consumer signals amid the noise was also critical. The algorithms had to provide meaningful interpretations of these signals for actionable insights. As the customer’s Machine Learning models were not precise enough, the Scalefocus experts had to identify the most suitable Large Language Models (LLM) and fine-tune them for the business use case. The increased precision would help avoid misinterpreted consumer signals that could diminish data quality. 

  • Scalability and Performance

True scalability goes both ways. Team Scalefocus had to help design and maintain a scalable and high-performing system that could handle the processing and analysis of data. As the massive volumes of data could fluctuate significantly, the platform not only needed to be able to handle it but also to optimize resources, increase efficiency, and reduce cost.

  • Real-time Analysis

The ability to analyze consumer signals in real time was also necessary to respond promptly to emerging trends and consumer behavior. The earlier microtrends are recognized, the timelier decision-making is, and that always gives businesses the edge over the competition to tailor their campaigns, gain a critical advantage in a highly dynamic market, and increase the return on investment.

The Solution

  • Cross-Disciplinary Team

Scalefocus assembled a cross-functional team of data scientists, machine learning and data engineers, as well as domain specialists. This was necessary to identify and make the most out of all the possibilities and collaborate in interpreting consumer signals effectively. We also relied on the expertise of quality engineers, front-end engineers, and UI/UX designers. Only a team of this size and diversity could guarantee we could resolve numerous challenges, as the amount of data to be preprocessed and adapted by AI/ML mechanisms was significant.

  • MLOps Implementation

The most critical part of the project was to establish an end-to-end MLOps framework that streamlines the machine learning development and deployment process. It involved the setting up of a testing process, ensuring lack of degradation of the models’ veracity and performance, optimizations, tweaking of parameters and retesting after the AI solution was deployed. Establishing the MLOps process was a complex and time-consuming task that helped immensely in the maintainability and observability of the AI subsystem. The outcome fully depended on the AI learning model capturing all the microtrends and turning them into actionable insight to give our partner’s customer brands a critical competitive advantage.

  • Cloud and Scalability

The Scalefocus experts leveraged cloud computing services to ensure scalability and high-performance data processing, storage, and analysis capabilities. The sheer quantity of data made it critical to optimize the cloud and save time and funds. The team addressed all challenges, including some significant setbacks related to transfers between Google and AWS, to reach the necessary level of cloud optimization.

  • A/B Testing and Experimentation

Our team also applied A/B testing and experimentation methodologies to validate hypotheses, optimize strategies, and continually improve consumer intelligence. This way, we trained the Machine Learning mechanisms to generate conclusions based on the data streams and confirm the conclusions’ validity utilizing alternative streams .

Our experts utilized Scalefocus‘ AI adoption framework to help the customer assess their options and tradeoffs so they could take advantage of the best existing AI capabilities and approaches to utilizing AI while constantly upgrading the system capabilities. As a result, their consumer intelligence platform’s speed and precision in identifying and communicating microtrends dramatically improved. It can now not only handle massive amounts of data streams but analyze them in real-time and scale up and down without compromising quality to generate priceless actionable insight.

The Results 

  • Scalable and Efficient MLOps

Continuous monitoring and optimization made MLOps much more scalable and productive as AI/ML models typically become more precise with the increasing amounts of data and accumulated knowledge on which to base insights and trend prediction. Our team even leveraged the significant volumes of quality data from online encyclopedias to train and fine-tune the models and reach impressive levels of precision.

  • AI Models  Continuous Improvement

One of the greatest Scalefocus contributions to the project is enabling the solution’s perpetual evolution that keeps it ahead of the curve in the fiercely competitive AI market.

  • Enhanced User Experience

Scalefocus’ UI/UX experts improved the solution’s front-end overall user experience across the web. We leveraged AI/ML to fine-tune the data streams and trend-spotting to facilitate our partners in marketing their customers’ brands. Naturally, this spares time, reduces cost, and accelerates time to market to give them the edge over the competition.

Technologies

Python
Google Cloud services
MLOps
LLMs
AWS Services

Our Work

We have a global client base that includes Fortune 500 companies, innovative startups and industry leaders in Information Technology, E-Commerce, Insurance, Healthcare, Finance and Energy & Utilities.

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