Nota AI vs Scribble Data

Neither company has publicly disclosed a valuation, making a definitive size comparison difficult. Nota AI employs 1-50 people, while Scribble Data has 10-50 employees.

Nota AI has raised $10M in disclosed funding across 0 known rounds. Scribble Data's funding history is not publicly available.

Nota AI holds the higher Awaira Score at 38/100, compared to Scribble Data's 35/100. The Awaira Score is a composite metric factoring in valuation, funding, stage, team size, and market presence — a 3-point gap that reflects meaningful differences in scale or traction.

Nota AI was founded by Gwang-jin Cho in 2019. Scribble Data was founded by Arun Iyengar in 2019. Visit each company's profile on Awaira for a full founder biography.

Nota AI: Nota AI develops neural network compression and optimisation technology for deploying AI models on edge devices and embedded systems with limited compute and power resources, providing tools that compress, quantise, and prune large neural network models into lightweight versions suitable for smartphones, surveillance cameras, automotive chips, and IoT devices. The Seoul company NetsPresso platform automates the model optimisation workflow, reducing the engineering effort required to adapt research models for production hardware deployment.\n\nThe company raised approximately $10 million in venture funding from Korean AI-focused investors. Nota AI targets computer vision and NLP model deployment scenarios where inference must run locally on resource-constrained hardware rather than in the cloud, a requirement driven by latency, privacy, and connectivity considerations. The company customers include semiconductor companies integrating AI features into SoC designs and device manufacturers seeking to add on-device AI capabilities without redesigning around more powerful processors.\n\nNota AI competes in the edge AI model optimisation market against Kneron, Hailo, and embedded AI optimisation tools from semiconductor companies including ARM, Qualcomm, and MediaTek. Its software-first approach, which optimises models for deployment across diverse existing hardware rather than requiring a specific custom chip, addresses the fragmented edge hardware ecosystem where AI model developers need to support many different target processors from different vendors. The Korean semiconductor industry concentration creates a proximate customer base among chipmakers and device manufacturers seeking edge AI capabilities. Scribble Data: Scribble Data builds a feature store and ML data pipeline platform that enables data science teams to define, compute, store, and serve machine learning features in a unified platform, addressing the significant engineering overhead of building and maintaining feature infrastructure for production ML systems. The platform reduces the time data teams spend on data wrangling and feature engineering by providing reusable feature definitions and automated pipeline orchestration.\n\nThe company is in early Seed stage with limited disclosed funding and targets data science teams at financial services companies, digital consumer businesses, and enterprise analytics organizations that run large-scale ML operations. Scribble Data positions its platform as infrastructure that shortens the path from model ideation to production deployment.\n\nFeature stores have emerged as a recognized production ML infrastructure category, with vendors including Tecton, Feast, and Hopsworks competing for enterprise adoption. Scribble Data's India-based team and pricing model target a segment of the market that cannot justify the cost of US-headquartered enterprise ML platform vendors, while still requiring production-grade feature management capabilities.

Both Nota AI and Scribble Data were founded in the same year — 2019. Despite sharing a founding year, they may have launched at different times within that year, which can matter in fast-moving AI markets.

Nota AI has approximately 1-50 employees, while Scribble Data has approximately 10-50. A larger team often signals higher revenue or venture backing, but in AI, smaller teams are increasingly capable of building at scale.

Yes, Nota AI and Scribble Data are direct competitors — both operate in the ML Platform space and likely target overlapping customer segments. This comparison is especially relevant for buyers evaluating both platforms.