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Based in Mountain View, California, Corvic AI provides an enterprise generative AI platform that transforms complex multimodal data into actionable business intelligence. The company develops infrastructure that integrates text, tables, graphs, and images to help organizations in the manufacturing, finance, industrial, and life sciences sectors build custom artificial intelligence applications. By combining generative models with custom pipelines, the software enables enterprise customers to reduce application development time from several months to a few weeks while achieving reported accuracy improvements of up to 30 percent. In April 2025, the business officially emerged from stealth mode after raising $12 million in seed funding backed by lead investors M Ventures and Bosch Ventures, alongside strategic participation from Foothill Ventures and Lam Capital. Corvic AI was founded in 2023 by former Katana Graph executives Farshid Sabet, Donald Nguyen, and Gurbinder Gill.
Corvic AI has raised $12.0M across 1 funding round.
Corvic AI has raised $12.0M in total across 1 funding round.
Corvic AI has raised $12.0M across 1 funding round. Most recently, it raised $12.0M Seed in April 2025.
| Date | Round | Lead Investors | Other Investors | Status |
|---|---|---|---|---|
| Apr 1, 2025 | $12M Seed | M Ventures, Robert Bosch Venture Capital | Atlantic Bridge, Sequoia Capital, TWO Bear Capital | Announced |
Corvic AI has raised $12.0M in total across 1 funding round.
Corvic AI's investors include M Ventures, Robert Bosch Venture Capital, Atlantic Bridge, Sequoia Capital, Two Bear Capital.
# Corvic AI: Enterprise Intelligence Through Generative AI
Corvic AI builds an Intelligence Composition Platform that transforms complex enterprise data into actionable insights using generative AI.[1] Founded in 2023 and based in Mountain View, California, the company develops end-to-end GenAI infrastructure designed to help organizations move from raw data to enterprise-grade AI solutions in weeks rather than months.[1][2]
The company serves enterprises across manufacturing, pharmaceuticals, semiconductors, financial services, and the public sector.[2] Corvic solves a critical enterprise AI bottleneck: how to bridge proprietary, complex data—spanning tables, text, images, and sensor data—with large language models in ways that are trustworthy, explainable, and scalable.[4] Rather than requiring months of custom development, Corvic enables subject-matter experts and data professionals to build production-ready AI applications rapidly by combining the ease of generative AI with the rigor of bespoke analytics pipelines.[1]
Corvic was co-founded by Farshid Sabet (CEO), Dr. Donald Nguyen (CTO), and Dr. Gurbinder Gill (CPO), drawing on over a decade of combined experience at Intel, Determined AI, Movidius, Katana Graph, and academia.[4] The company emerged from a research initiative in multimodal reasoning—the ability to understand and process text, tables, graphs, and images natively—which evolved into a breakthrough platform for enterprise data intelligence.[1]
The founding team's expertise spans deep learning, graph analytics, distributed machine learning, and enterprise infrastructure, positioning them to address the specific challenges enterprises face when deploying AI at scale.[4] In October 2025, Corvic raised $12 million in seed funding, with backing from investors including Brian Long (Atlantic Bridge), Foothill Ventures, Bosch Ventures, and LAM Research Capital.[2][3] The company has already achieved notable early traction, partnering with Bosch and winning the company's 2024 Open Bosch Award, while supporting customers in pharmaceutical, semiconductor, and advanced manufacturing sectors.[2]
Corvic's competitive advantage rests on proprietary technologies purpose-built for enterprise complexity:
These differentiators address a fundamental gap: most GenAI solutions rely on simple retrieval, while Corvic's cognitive infrastructure performs intelligent, multi-step reasoning across heterogeneous data sources—delivering precision and trustworthiness critical for high-stakes enterprise decisions.[4]
Corvic operates at the intersection of two major trends reshaping enterprise AI. First, the explosion of generative AI has created enormous demand for AI-powered applications, but enterprises struggle to deploy GenAI safely and accurately over proprietary data—a problem Corvic directly addresses.[4] Second, enterprises are drowning in complex, multimodal data (sensor streams, documents, databases, images) that traditional analytics tools cannot effectively process together.[2]
The timing is critical: as enterprises move beyond GenAI experimentation toward production deployment, they need platforms that can handle real-world data complexity while maintaining explainability and governance—precisely what Corvic offers.[4] The company's early wins in regulated industries (pharmaceuticals, semiconductors, financial services) signal that enterprises are willing to adopt specialized AI infrastructure when it solves genuine operational problems and reduces risk.[2]
By positioning itself as a data-centric AI infrastructure layer rather than a consumer-facing AI tool, Corvic influences the broader ecosystem by raising the bar for enterprise AI reliability and enabling faster, more confident AI adoption across industries.[1]
Corvic is well-positioned to capture significant market share in the emerging enterprise AI infrastructure category. The company's $12 million seed round, marquee customers, and cloud marketplace presence (Google Cloud, AWS) suggest strong product-market fit and distribution momentum.[3][6] As enterprises mature their AI strategies beyond chatbots and simple automation, demand for trustworthy, explainable intelligence platforms will accelerate.
The key inflection points ahead: expanding beyond early verticals (manufacturing, pharma, semiconductors) into financial services and government; deepening integrations with major cloud providers; and potentially building a developer ecosystem around its platform. If Corvic can establish itself as the standard for enterprise multimodal AI reasoning—much as specialized infrastructure companies have done in other domains—it could become a critical layer in the enterprise AI stack, influencing how organizations think about data governance, AI safety, and operational intelligence for years to come.
