10 Team & method
A founder leads an AI team, with collaborators and advisers. Engineering, open-source product, and business experience inform decisions; repeatable work and corrections become processes.
Praxis is built by its founder leading an AI team, with collaborators and advisers in operations, product, and research.
Team and company
We entered the agent field in March 2025 and used AI to build AI from the start. People set direction and make decisions, while AI performs much of the work; this is what the plan means by an AI-native company.
Deepractice is the company and Praxis is the platform. Its entities are 南京深度实践人工智能科技有限公司 in Nanjing and Praxis Global Limited in Hong Kong; the earlier platform domain is deepraxis.ai, with product and company information in the company section.
How experience supports the work
Engineering. Founder Jiang Shan worked as a software engineer at WeBank across testing, operations, and development, experiencing large internet systems from construction through operation. That helps us judge what remains between a prototype and a lasting service.
Product. The PromptX open-source project uses the MIT license and had more than 3,700 GitHub stars as of September 2026; stars record community interest, not paying customers. Earlier records also include AtomGit G-Star graduation certification and selection for City University of Hong Kong’s HK Tech 300 seed fund in December 2025.
The paper PromptX: A Cognitive Agent Platform with Long-term Memory, developed with City University of Hong Kong, appears in the WWW 2026 Demo acceptance list. Open source tested product adoption and built community relationships that continue to introduce users to Praxis; moving the platform to cloud services brought further interest in promotion.
Business. The founder previously operated internet travel and internet solutions businesses, including a company with annual profit in the millions of RMB and a team of around 50, alongside several failed ventures. Hands-on experience in marketing, sales, operations, financing, and finance exposed the cost of labor-intensive delivery; see the Xinhua Daily Jiao Huidian profile.
Our starting advantages
Understanding agents supports the product approach, engineering experience supports platform construction, and AI working methods have been used in the initial build. Open-source relationships provide a start for long-term advantages and customer acquisition, while the paper exposes the method to outside examination.
These support continued work rather than guarantee success. We still need time and scale; the work financing would support is in Milestones & financials.
People judge, AI works
People specify what to do, what comes first, and what counts as done. AI performs much of development, testing, maintenance, documentation, and operations, serving as both product and production tool.
People and AI follow the same public working rules: how to start work, demonstrate completion, hand it over, and recognize when to stop for human judgment. Personal Agents act as employees in this work, helping build and operate customer-facing services.
Make corrections repeatable
Every correction should leave a rule, procedure, or memory rather than depend on one person remembering it. Tests handle what can be checked automatically, and programs handle repeatable work.
We want the team to work better than last month because it has retained reusable methods, not just because individuals are more practiced. This is also a source of cost improvement in Why now.
Do not depend on exhaustion
People have limits on time and attention. We do not build the business on anyone remaining overloaded or on critical judgment staying with one person.
Work that can be expressed as rules and verified goes into repeatable processes first; people handle the judgment that remains. Work can then grow without requiring the same growth in personal effort.
Use our own products first
Praxis App is our daily development tool, and the cloud services first handle our real work. We encounter and bear the cost of poor usability or unreliable operation ourselves.
The initial platform build and model service launch are existing records of this approach in use. The next chapter examines where these judgments and methods could fail.
9 Go-to-market
Start with developers and businesses not yet committed to a platform. Help their agents read the docs and integrate successfully, then grow through use, referrals, and partner shares.
11 Risks
Manage costs, procurement, quality, specialization, competition, data, people, and cash flow; accept the growth and margin tradeoffs of avoiding paid advertising and personal entry points.