Scribble Data vs Improbable

Neither company has publicly disclosed a valuation, making a definitive size comparison difficult. Scribble Data employs 10-50 people, while Improbable has 500-1000 employees.

Improbable has raised more in total funding at $700M, compared to Scribble Data's $3.5M — a gap of $696.5M. Combined, the two companies have completed 3 known funding rounds.

Improbable leads with an Awaira Score of 68/100, while Scribble Data sits at 35/100. That 33-point gap reflects real differences in funding, scale, and traction — it's not a vanity metric.

Scribble Data was founded by Arun Iyengar in 2019. Improbable was founded by Herman Narula in 2012. Visit each company's profile on Awaira for a full founder biography.

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. Improbable: Improbable develops large-scale simulation technology and virtual world infrastructure, originally focused on cloud-distributed game server simulation and subsequently pivoting to AI-powered synthetic environment generation and metaverse platform development. The London company built SpatialOS, a cloud platform for running distributed simulations of large, persistent virtual worlds, and has applied simulation capabilities to defence, urban planning, and entertainment applications.\n\nThe company raised approximately $700 million including a landmark $502 million SoftBank round in 2017 that was one of the largest venture rounds in European technology history. Improbable has undergone multiple strategic pivots, moving from gaming infrastructure to defence simulation contracts with clients including the UK Ministry of Defence, which uses simulation environments for training and wargaming. The company also built MSquared, a metaverse interoperability network, before refocusing on defence and AI simulation work.\n\nImprobable operates in an AI simulation market where demand from defence agencies, autonomous vehicle companies, and robotics firms has grown substantially. The company competes with simulation platforms from Epic Games (Unreal Engine), NVIDIA (Omniverse), and specialist defence simulation vendors. Its SoftBank backing and large cash reserves have enabled it to survive multiple market pivot cycles that would have ended less well-funded companies, and its current focus on AI-powered synthetic data generation for defence represents a growing government budget category.

Improbable got there first, launching in 2012 — that's 7 years of extra runway. Scribble Data didn't arrive until 2019. In AI, that kind of head start means more training data, deeper customer relationships, and a bigger talent moat.

Scribble Data has about 10-50 employees; Improbable has about 500-1000. A bigger team usually means more revenue or heavier VC backing, but in AI, small teams can build at massive scale.

Yes — they're direct rivals. Both Scribble Data and Improbable compete in ML Platform, targeting many of the same buyers. If you're evaluating one, you should be looking at the other.