PH Deck

Users need to build custom text classification models but face challenges with existing solutions being not lightweight, expensive, slow to train, and less accurate for their specific datasets.

In the current situation, users struggle to extract text from images manually, which is time-consuming and prone to errors.

Architects, real estate developers, and urban planners manually create physical scale models for presentations, which is time-consuming, resource-intensive, and requires specialized craftsmanship.

Users previously relied on less accurate speech-to-text models like Whisper and limited text-to-speech customization, leading to errors in transcription and robotic voice outputs.

Tech-first organizations face challenges optimizing data quality, cost, failures, data volumes, and durations for specific Data and AI products, and consolidating tooling is difficult. Data Lineage is also a concern.

Data scientists struggle with preparing accurate and diverse training data for fine-tuning large language models (LLMs), which leads to less efficient AI model development and performance issues due to lack of effective data labeling tools.

Users struggle with inconsistent text formatting across platforms and documents, leading to inefficiencies and a lack of professionalism.

Drawbacks include the time-consuming manual effort required to ensure consistent text format, potential for human error, and the inability to quickly switch between different text styles.

Users need to process, analyze, and utilize data but face coding requirements and fragmented tools, leading to inefficiency and delayed insights.

Difficulty in accessing high-quality synthetic data for organizations

Users struggle to transform text data into visually appealing and interactive statistical visualizations

Limited access to easy-to-understand visual representations of data