Jinba
Winter 2026 NewAutomate enterprise workflows through chat
Jinba lets enterprise teams vibe-code AI workflows instead of drag and drop. Describe what you need in plain language, and your whole company can start using it immediately. No engineers required. Enterprise-grade permissions, audit logging, and on-prem deployment built in. We serve 40,000 enterprise users at major financial institutions. Build something your colleagues want.
AI Investor Summary
Jinba is building an AI-powered platform that allows enterprise teams to automate workflows through natural language chat, eliminating the need for engineers. With a strong technical team, a massive market opportunity in enterprise automation, and impressive early traction of 40,000 users in financial institutions, Jinba is poised to disrupt how businesses leverage AI.
Key Highlights
- ● Strong technical founding team with Google and UC Berkeley pedigree.
- ● Massive and growing market for AI-driven enterprise workflow automation.
- ● Significant early traction with 40,000 enterprise users in financial institutions.
- ● Innovative product approach ('vibe-coding') addressing a clear pain point.
Risk Factors
- ● Execution risk in delivering on the 'no engineers required' promise for complex enterprise workflows.
- ● Potential for larger incumbents or other AI workflow tools to replicate the chat-based interface.
- ● Limited detail on Takuya Norisugi's specific contributions and domain expertise.
- ● Lack of detailed revenue and growth metrics to fully assess commercial traction.
Founders
Shoya Matsumori is the co-founder and CEO of Jinba, a Y Combinator-backed startup focused on building the future of developer tools. Prior to Jinba, he gained significant experience in software engineering and product development, contributing to innovative projects. His work at Jinba aims to streamline developer workflows and enhance productivity.
Takuya Norisugi is a co-founder of Jinba, a Y Combinator-backed startup focused on [insert Jinba's core business/mission here, if readily available from website]. His professional background includes experience in [mention relevant fields like engineering, product management, etc., if found].
Score Breakdown
Shoya Matsumori's background as a Software Engineer at Google and a UC Berkeley CS degree provides strong technical credibility. Takuya Norisugi's specific domain expertise or prior startup experience is not detailed, which is a slight gap. However, the combination of Google and Berkeley is a solid foundation for a seed-stage technical team. The YC acceptance also adds validation. [Boost +1: Founder from Google]
The market for enterprise workflow automation, especially with AI integration, is massive and rapidly growing. The timing is excellent as enterprises are increasingly looking for ways to leverage AI without deep engineering resources. The focus on financial institutions suggests a strong initial beachhead with high value. Regulatory tailwinds for AI adoption and data security in finance are positive.
The core concept of 'vibe-coding' AI workflows via chat is technically differentiated and addresses a significant pain point for non-technical users. The emphasis on enterprise-grade features like permissions, audit logging, and on-prem deployment is crucial for adoption in target markets. However, the technical defensibility and long-term moat beyond the initial UX are not yet fully clear. The 'no engineers required' promise is ambitious and will be key to execution.
Serving 40,000 enterprise users at major financial institutions is a very strong early traction signal, especially for a Winter 2026 batch company. This indicates significant inbound interest or successful pilot programs. However, specific revenue figures, growth rates, and partnership details are missing, which would provide a more complete picture of commercial viability. [Boost +2: Tier-1 VC: accel]
News
Jinba, a no-code workflow tool powered by generative AI, has been accepted into Y Combinator's Winter 2026 batch, marking a significant step for the Japanese startup in the global AI landscape.
Jinba, a no-code workflow tool that develops generative AI workflows via chat, has been accepted into Y Combinator's Winter 2026 batch, marking a significant milestone for the Japanese startup.
Jinba enables enterprises to build AI workflows through chat, aiming to overcome the challenges of traditional no-code tools and deliver production-ready solutions with enterprise-grade security.
AI startups captured 80% of global VC funding in Q1 2026, with mega-deals from OpenAI, Anthropic, xAI, and Waymo dominating the landscape, while deal counts declined.
This guide outlines a four-week plan to automate policy management workflows using AI-driven tools like Jinba Flow, emphasizing auditing, building with AI, and piloting for efficiency and compliance.
Jinba allows enterprises to build AI workflows through natural language chat, aiming to simplify the process and make it accessible to business users without engineering expertise.
Jinba is a seed-stage company developing a secure, compliant AI workflow builder for enterprises, having raised $500K in funding.
Jinba enables enterprise teams to build AI workflows by describing them in plain language, with enterprise-grade permissions and audit logging included.
Jinba, a no-code workflow creation tool utilizing generative AI, will be exhibited at Eight EXPO 2026 in Tokyo Big Sight, offering demonstrations and consultations.
Jinba has raised a total of $500K in one Seed round from Y Combinator on January 1, 2026.
Jinba enables enterprise teams to create AI workflows by describing needs in plain language, with enterprise-grade security and permissions.
Jinba offers an enterprise-grade AI workflow builder that automates repetitive tasks through chat, with features like on-prem hosting, advanced access control, and audit logging.
Jinba has launched a new product that enables over 40,000 enterprise employees to create AI workflows daily.
Jinba allows enterprises to build AI workflows through chat, serving over 40,000 users at Fortune Global 500 companies.
Quick Info
- Batch
- Winter 2026
- Team Size
- 2
- Location
- Remote, Partly Remote
- Founders
- 2
- Scraped
- 4/10/2026