Fasal vs Patsnap

Patsnap has a disclosed valuation of $1B, while Fasal's valuation is not publicly available, making a direct size comparison difficult. Patsnap employs 500-1000 people.

Patsnap has raised more in total funding at $300M, compared to Fasal's $12M — a gap of $288M. Combined, the two companies have completed 3 known funding rounds.

Patsnap leads with an Awaira Score of 80/100, while Fasal sits at 55/100. That 25-point gap reflects real differences in funding, scale, and traction — it's not a vanity metric.

Fasal was founded by Ananda Verma in 2018. Patsnap was founded by Jeffrey Tiong in 2007. Visit each company's profile on Awaira for a full founder biography.

Fasal: Fasal is a precision farming platform that deploys IoT sensors in agricultural fields to collect real-time micro-climate, soil moisture, and pest pressure data, which is then analyzed by AI models to deliver actionable crop management advisories directly to farmers' smartphones. The platform helps farmers make data-driven decisions about irrigation, fertilization, and pest management to improve yields and reduce input costs.\n\nThe company raised approximately $12M in Series A funding from investors including Omnivore, and has deployed its sensor networks and AI advisory platform across thousands of farms in India covering crops including grapes, tomatoes, pomegranate, and rice. Fasal's AI models are trained on crop science and agronomic data to translate sensor readings into specific, actionable recommendations.\n\nIndian agriculture faces significant productivity gaps relative to global benchmarks, with irrigation overuse, input inefficiency, and pest losses representing measurable economic costs at the national scale. Fasal's data-driven precision agriculture approach addresses these inefficiencies in a market where increasing farm profitability and resource efficiency are national policy priorities. Patsnap: Patsnap provides an AI-powered patent analytics and intellectual property intelligence platform that enables R&D teams, IP lawyers, and corporate strategy teams to search, analyse, and monitor global patent databases using machine learning to surface technology trends, competitor patent activity, and freedom-to-operate insights from over 170 million patent documents. The Singapore company integrates patent data with scientific literature and chemical structure databases to provide a comprehensive IP and technology intelligence environment.\n\nThe company raised approximately $300 million including a Series E round from investors including SoftBank Vision Fund 2, Tencent, and CPE Yunji, reaching a valuation exceeding $1 billion. Patsnap serves over 10,000 companies globally including R&D-intensive organisations in pharmaceuticals, semiconductors, automotive, and consumer electronics that use patent landscape analysis to inform technology strategy and investment decisions.\n\nPatsnap competes in the IP analytics market against Derwent Advancement (Clarivate), Questel Orbit, and Minesoft, as well as general enterprise search platforms that include patent data. Its AI capabilities for patent claim analysis, technology trend extraction, and cross-domain IP search differentiate it from traditional Boolean-search patent databases, enabling R&D strategy questions to be answered through AI-assisted analysis rather than manual patent review. The Singapore base provides access to both the Southeast Asian technology market and a sophisticated legal and financial services market for IP monetisation and licensing analytics.

Patsnap got there first, launching in 2007 — that's 11 years of extra runway. Fasal didn't arrive until 2018. In AI, that kind of head start means more training data, deeper customer relationships, and a bigger talent moat.

Fasal has about 50-200 employees; Patsnap 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 Fasal and Patsnap compete in AI Data, targeting many of the same buyers. If you're evaluating one, you should be looking at the other.