Tecnology
The uniqueness of AIKE
Aike enriches real time purchase data with geolocation, demographic, behavioral, psychographic data through our proprietary AI platform, which enables mapping of merchants, categories and segments to 3rd party data and digit IDs for a variety of vertical high value solutions.
Recent research has emphasized that AI models behavior is not determined by architecture, hyperparameters, or optimizer choices, it is actually the high-quality, high- value data serves as the backbone of AI development.
It enables the creation of robust models, drives innovation, improves user experiences, facilitates effective problem-solving, reduces risks,
and builds trust in AI systems.
Ever since founded, the Aike team has been unwaveringly committed to tackling the most fundamental challenge in AI/ML development.
Cutting Edge Technology
AIKE’s merchant engine harnesses the power of machine learning through embeddings, which capture the semantic and syntactic nuances in debit/credit and merchant data.
This, combined with a high-dimensional vector database, enables the engine to effectively identify sales and assess brand health performance using structured and unstructured data. The system is built to be highly scalable and resilient. By utilizinguser-specific embeddings, the engine can tag new transactions based on past matches.
Additionally, AIKE has been exploring the integration of Large Language Models (LLMs) to enhance the search capabilities of its merchant system.
Consumer Profiling
AIKE utilizes a combination of diverse algorithms, including Random Forests, Neural Networks, and K-means Clustering, to delve into customer behavior and preferences.
This approach aims to enhance accuracy, handle intricate data, and achieve comprehensive profiling.
By employing these algorithms, AIKE effectively analyzes complex consumer data like images, text, and sequences.
It identifies significant variables and patterns, enabling detailed consumer profiling and segmenting customers based on similarities in attributes and behaviors.
AIKE utilizes a combination of diverse algorithms, including Random Forests, Neural Networks, and K-means Clustering, to delve into customer behavior and preferences.
This approach aims to enhance accuracy, handle intricate data, and achieve comprehensive profiling.
By employing these algorithms, AIKE effectively analyzes complex consumer data like images, text, and sequences.
It identifies significant variables and patterns, enabling detailed consumer profiling and segmenting customers based on similarities in attributes and behaviors.