Top-BOSS
Learners get immediate strategic guidance while educators cut analysis overhead and scale support across global programs.
Read case studyAbout Knobase
Building platforms where
people and AI work as one team.
Knobase builds platforms where proprietary knowledge, human judgment, and AI execution can work together. We think the future of AI is not better chat windows. It is better collaboration.
We work with schools, research teams, and enterprises where context is sensitive and generic AI is not enough. The work is grounded in real deployments, not slide decks.
Our philosophy
Innovation moves fastest when people and AI share the work
Our core belief is that the most meaningful progress does not come from automating people away, or from treating models as oracles on the side of the screen. It comes from putting human judgment and machine capability in the same loop: shared context, clear intent, and room to refine together.
When teams can think with AI the way they think with colleagues — iteratively, visibly, and with accountability — ideas compound faster and fail more usefully. That is the bar we design toward: not novelty for its own sake, but collaboration that earns trust over time.
Today
What we focus on
Knobase exists to help organizations use AI on their own knowledge and workflows: with clear governance, human judgment in the loop, and systems that can earn trust in high-stakes environments. Product details live on the homepage; here, the point is the mission and the proof.
Focus
Proprietary context & human–AI collaboration
Where
Research, education, enterprise
Traction
1,100+ users · 17,000+ messages
Recognition
Alibaba Jumpstarter Top 30
Insights — use cases and case studies as we publish them. Contact for pilots and partnerships.
Where this is already working
What we learn from the field
Real deployments in education, research, and high-context workflows where generic AI falls short.
The Harbour School
Leaders saw clearer logistics demand, deeper MUN engagement, and actionable signals to support students faster.
Read case study
The University of Hong Kong
The programme scales supportive, research-grade engagement while keeping oversight, privacy, and local context at the center.
Read case studyBroader proof
Also active across research, healthcare, and proprietary-data workflows where context and governance matter.
Talos Medical
Healthcare / MedTech
Doctor co-pilot proof of concept across MRI, CT, and clinical data.
MyMasters
Retail / Forecasting
Turning ten years of proprietary sales data into AI-supported demand forecasting.
Asia-Pacific education pilot
Public sector / Education
Institutional pilot work where governance, policy, and domain knowledge matter as much as model capability.
Internal policy workflows
Internal knowledge
Workflows where proprietary documents and policies define what "correct" looks like.
The knowledge argument
Proprietary knowledge is an unrealized asset
Proprietary knowledge is fragmented across tools, people, and time — hard to find, harder to compound. What if it was collaborative?
- Humans contribute vision and judgment
- Agents contribute speed and scale
- Together, they turn expertise into living infrastructure
Not a database
Collaborative brain humans and AI improve.
Not delegation
Work alongside, shape in real time.
Living infrastructure
Expertise compounds with every edit, conversation, and run.
From the founder
Our belief is simple: human insight should set the direction, and AI should expand what is possible. The goal is not to automate people out of the loop. It is to help experts, teams, and organizations do more with the judgment, context, and originality they already have.
Too much of today's AI still treats knowledge as something to extract, instead of something to develop, protect, and compound. We think the future looks different: people and AI working in shared systems, with visibility, control, and trust built in from the start.
That is why Knobase is building an ecosystem: a privacy-first workspace for real-time collaboration, a way to turn proprietary knowledge into deployable agents, and the infrastructure for that knowledge to remain owned, governed, and valuable.
If we get this right, AI will not feel like a detached utility. It will feel like a true extension of human capability: secure, collaborative, and designed to amplify people rather than replace them.
Our commitments
What We Believe.
01
Human-led, AI-amplified
Human judgment stays in charge while AI expands speed, scale, and execution.
02
Transparency by default
Teams need visibility into how AI is being used before they can trust it in real work.
03
Bring your own intelligence
The platform should adapt to the models and systems an organization chooses to use.
04
Your knowledge, your control
Proprietary knowledge remains governed, protected, and aligned to the organization that owns it.
05
Collaboration is built in
People and agents should work in the same system, with shared context and visible actions.
Build your AI advantage on what only you know.
Your frameworks and experience shouldn’t sit idle while generic models fill the gap.