Solving the Manual Process Bottleneck for a Unicorn Startup
Overview: A $1B evaluated startup whose focus is on providing HR solutions asked AltheonAI to improve their automated text processing model, bringing them closer to their mission of making hiring great people easy.
Academia:
Long Short-Term Memory (LSTM) networks are pivotal for studying sequential data analysis. Recent academic research has spotlighted the efficacy of Long Short-Term Memory (LSTM) networks in text summarization, underscoring their superiority in grasping subtle nuances in textual data. These studies reveal that LSTM's unique architecture, particularly its ability to retain long-term dependencies, compared to RNNs, is crucial for generating coherent and contextually relevant summaries.
Problem:
Inconsistent data accuracy in traditional market research methods
High dependency on manual processes leading to inefficiencies
Limited capital/capability to process and analyze large volumes of data
Solution:
AltheonAI advised and built an automated text classification model that utilizes Long Short-Term Memory (LSTM) networks, expanding their original data scope from 8 to 43 industries.
Impact:
A 500% boost in model accuracy from 12% accuracy to 70% through simple understandable ML models and higher quality data
Reduced the product team's research time by over 40%
Streamlined the data analysis process, aiding the startup in better decision-making and market understanding
