Ur AI builds the full stack for trusted enterprise AI workflows: complex documents made AI-ready, enterprise-owned fine-tuned models, and proof in high-stakes work. Human-led, enterprise-grade.
We believe the winning enterprises of the next 2-3 years will run enterprise-specific, locally deployed, fine-tuned LLMs for roughly 80% of their AI workloads.
The remaining workloads will still need controlled access to frontier models where their capabilities matter. Ur AI is building the full stack for both paths: AI enterprises can trust, own, evaluate, and tune to their own data.
Every layer is built to be owned, audited, and tuned to your enterprise, not rented from a black box.
Charts, tables, contracts, and filings: the business knowledge trapped inside complex files.
Turns that into layout-preserved Markdown and structured JSON your AI can actually answer from. The foundational engine the rest of the stack is built on.
Run most workloads on enterprise-specific local LLMs, then route exceptional tasks to frontier models when their capabilities are needed.
Measure answerability, wire models into your systems, and keep quality auditable as workflows change.
High-stakes workflows built on the stack, with Specter for due diligence as the first proof point.
Nebula is the foundation. Specter is the first workflow application proving it in a high-stakes domain.
Integrate Nebula's document intelligence into your own pipelines via the REST API.
Specter runs on Nebula: the same foundation, applied to M&A due diligence.
"We're UrAI. Our mission is to Advance Humanity's Reach by transforming enterprise workflows with AI - unlocking the world's best talent to pursue greater ambitions, while helping enterprises transition to next-generation, AI-native workflows that deliver sustained competitive advantage in the AI era."
A diverse, international team spanning research, product, engineering, and operations β from India, Japan, Bangladesh, Indonesia, Malaysia, South Africa, Australia, the US, and Spain β united by a mission to scale AI responsibly from Japan.
Click on a team member to learn more
Ur AI products are built to meet the standards that enterprise and institutional clients require.
Information Security Management
CertifiedService Organization Controls
Type 2 ReportGeneral Data Protection Regulation
CompliantHow a shared belief in innovation and AI sparked our journey.
""The West sparks 0β1 breakthroughs, Asia scales from 1β10 and transforms 10β100+. History proves it: cars (Benz & Ford β Toyota & Honda), EVs (Tesla β BYD), and the internet & SaaS (Google & Microsoft β Sundar Pichai & Satya Nadella driving scaling). Humanity thrives when the West invents and Asia scales."
Transformers, GPT, Gemini, Claude Code - the foundations of modern AI are being laid in the West. But for AI to truly fulfill its potential, Asia must now step up and scale it responsibly. The challenge ahead is ensuring AI evolves not only in capability, but also in service of humanity's progress.
UrAI was founded on the conviction that Asia must lead AI's next chapter. Headquartered in Japan, we assemble the best from India, Japan, Bangladesh, Indonesia, Malaysia, South Africa, Australia, the US, and Spain β drawing on experience from IIT Delhi, IIT Bombay, and IESE, and institutions like McKinsey & Company, Fujitsu, Woven by Toyota, and Laboro AI.
We see AI as an instrument - like electricity or the internet. Through Nebula and Specter, we enable transparent, human-guided workflows that drive faster, better decisions. As these gains compound, humanity can pursue what once seemed impossible - from thriving companies on Earth to missions reaching beyond the Milky Way toward Andromeda.
Read how we build, validate, and deploy AI systems for enterprise workflows in Japan and beyond.
Read the blogUr AI has completed its SOC 2 Type 2 examination and its GDPR compliance program, joining the ISO 27001:2022 certification we received in January. What each framework proves, what none of them prove, and how to check any AI vendor for yourself.
6 min read
Enterprise AI is moving beyond isolated model features toward information systems that can preserve meaning, choose the right evidence, govern agent actions, and improve through measurable feedback loops.
10 min read
We measured 14 document conversion systems (commercial OCR products, open-weight models, and frontier VLM APIs) on one question: after a Japanese IR page is converted to Markdown, can an AI still answer what a human answers from the original? Scores ranged from 20.2 to 94.6, and the market separates on charts.
8 min read
