Scatter Lab vs Rasa

Neither company has publicly disclosed a valuation, making a definitive size comparison difficult. Scatter Lab employs 1-50 people, while Rasa has 100-500 employees.

Rasa has raised more in total funding at $70.2M, compared to Scatter Lab's $15M — a gap of $55.2M. Combined, the two companies have completed 7 known funding rounds.

Rasa leads with an Awaira Score of 60/100, while Scatter Lab sits at 38/100. That 22-point gap reflects real differences in funding, scale, and traction — it's not a vanity metric.

Scatter Lab was founded by Kim Jong-yoon in 2014. Rasa was founded by Alan Nichol in 2016. Visit each company's profile on Awaira for a full founder biography.

Scatter Lab: Scatter Lab develops emotional AI and conversational applications in Korean, most notably Luda, a social chatbot designed for conversational companionship that engages users in casual, emotionally warm dialogue. The Seoul company applies social dialogue modelling to create AI characters that Korean users interact with for emotional support and social connection, a use case that has attracted significant user adoption particularly among younger Koreans.\n\nThe company raised approximately $15 million in venture funding from Korean investors including Kakao Ventures and DSC Investment. Scatter Lab has experienced regulatory and public controversy following the launch of its original Luda chatbot in 2021, which was suspended after concerns about data privacy practices and the chatbot production of offensive content, prominent to a significant redesign and relaunch with improved safety controls. The company has continued to develop its emotional AI technology while navigating the governance challenges of deploying social chatbots at consumer scale.\n\nScatter Lab operates in the social AI and AI companionship market alongside Character.ai, Replika, and Nomi.ai, serving a use case where emotional resonance and conversational naturalness are the primary product quality metrics rather than factual accuracy or task completion. The Korean market context, including social pressures and loneliness among young adults, creates specific demand for AI companionship products that is driving user growth despite regulatory attention. The company challenges with its first Luda release have informed approaches to responsible deployment that the broader AI companionship market is still developing. Rasa: Rasa builds an open-source conversational AI framework and an enterprise-grade dialogue management platform used by developers and large organizations to deploy contextual AI assistants. The core open-source product has accumulated millions of downloads and serves as the foundation for production chatbots and voice assistants across industries including banking, telecom, and healthcare.\n\nThe company raised approximately 75 million USD through Series C and has enterprise customers in regulated industries that require on-premise or private cloud deployment rather than SaaS-based NLP services. Rasa competes directly with managed platforms from Dialogflow, Amazon Lex, and IBM Watson by offering full data control and model customization unavailable on those services.\n\nAs enterprises grow more cautious about sending customer conversation data to third-party cloud providers, the demand for self-hosted conversational AI infrastructure strengthens Rasa position. The platform is particularly well-suited for organizations in the EU and financial sectors operating under strict data residency requirements, giving Rasa a structural moat that pure-SaaS NLP competitors cannot easily replicate.

Scatter Lab got there first, launching in 2014 — that's 2 years of extra runway. Rasa didn't arrive until 2016. In AI, that kind of head start means more training data, deeper customer relationships, and a bigger talent moat.

Scatter Lab has about 1-50 employees; Rasa has about 100-500. 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 Scatter Lab and Rasa compete in NLP, targeting many of the same buyers. If you're evaluating one, you should be looking at the other.