Rasa vs Mihup

Neither company has publicly disclosed a valuation, making a definitive size comparison difficult. Rasa employs 100-500 people, while Mihup has 50-200 employees.

Rasa has raised more in total funding at $70.2M, compared to Mihup's $6M — a gap of $64.2M. Combined, the two companies have completed 6 known funding rounds.

Rasa leads with an Awaira Score of 60/100, while Mihup sits at 50/100. That 10-point gap reflects real differences in funding, scale, and traction — it's not a vanity metric.

Rasa was founded by Alan Nichol in 2016. Mihup was founded by Biplab Kumar Jha in 2016. Visit each company's profile on Awaira for a full founder biography.

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. Mihup: Mihup is a conversation intelligence platform specializing in speech recognition and NLP for Indian languages, offering real-time call analytics, voice search, and speech-to-text capabilities tuned for Indic accents and multilingual code-switching common in Indian call centers. The platform serves BFSI, telecom, and retail customers that operate large vernacular customer support operations.\n\nThe company raised approximately $6M in Series A funding and has built proprietary speech models for Bengali, Hindi, and other regional languages that outperform global ASR providers on Indian accent benchmarks. Mihup's products are deployed in production contact centers processing high daily call volumes.\n\nAccurate speech AI for Indian languages remains an underserved technical problem given the phonetic diversity and code-switching patterns of Indian speakers. Mihup's focus on this specific challenge, combined with years of proprietary training data collected from real call center environments, gives the company a technical moat that is difficult for English-first global vendors to replicate quickly. Mihup competes in the Indian conversation intelligence space against Enthu.ai, Convin, and global platforms like Gong and Chorus that lack deep Indic language support. The company's core advantage lies in its proprietary speech recognition models trained specifically on Indian language datasets, delivering higher accuracy on Hindi, Tamil, Bengali, and other regional languages than English-first competitors can achieve. Mihup has gained traction in BPO, banking, and insurance sectors where call center operations in regional languages represent billions of minutes of untapped analytics potential across the Indian subcontinent.

Both Rasa and Mihup launched in 2016. Same year, but even a few months' head start matters in AI — early movers lock in data, talent, and customer relationships fast.

Rasa has about 100-500 employees; Mihup has about 50-200. 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 Rasa and Mihup compete in NLP, targeting many of the same buyers. If you're evaluating one, you should be looking at the other.