Chinoba Case Studies — How Chinoba Makes Possible What Conventional AI Could Not

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The Future of AI Is Not Larger Models — It Is Better Relationships

Introduction

Generative AI is being adopted at an unprecedented pace.

Today, AI is widely used across many business functions, including content generation, search, summarization, analysis, and code generation.

However, despite this rapid adoption, many organizations struggle to move beyond the Proof of Concept (PoC) stage.

The challenge is not simply AI accuracy.

The real challenge lies in:

• How AI integrates with organizations
• How multiple AI systems collaborate
• How decisions are explained
• How knowledge is preserved and inherited
• How AI is governed across society

In other words, the challenge is AI architecture.

Chinoba Research explores new intelligence infrastructure through fourteen research themes that address these challenges.

In this series, we introduce practical case studies demonstrating what each research area can enable.

01 Decision Trace Model

Case 1: AI-Assisted Medical Diagnosis

Conventional Approach

AI can generate diagnostic recommendations.

However, it is often difficult to determine:

• Why the diagnosis was made
• What modifications the physician made
• Who provided the final approval

Chinoba

With the Decision Trace Model,

the decision history of:

• AI
• Physician
• Specialist
• Patient

can be preserved chronologically.

What Becomes Possible

• Explainable medical decisions
• Medical auditing
• Continuous AI learning

Case 2: Smart Factory

Conventional Approach

AI can detect equipment anomalies.

However, it is often difficult to track:

• Who decided to stop the equipment
• When the maintenance team was notified
• Who approved restarting operations

Chinoba

Using the Decision Trace Model,

the decision history of:

• AI
• Equipment management system
• Maintenance engineer
• Site supervisor

can be preserved in chronological order.

What Becomes Possible

• Explainable shutdown and restart decisions
• Transparent maintenance and audit records
• Continuous improvement and safer AI operations

Case 3: Financial Approval

Conventional Approach

AI can assist with loan approvals and credit evaluations.

However, it is often difficult to understand:

• Why an application was approved or rejected
• How reviewers modified AI recommendations
• Who made the final approval

Chinoba

With the Decision Trace Model,

the decision history of:

• AI
• Credit reviewer
• Risk management department
• Final approver

can be preserved chronologically.

What Becomes Possible

• Explainable financial decision-making
• Regulatory compliance and auditing
• Continuous improvement of AI-assisted evaluations

02 Runtime Society

Case 1: Smart City

Conventional Approach

Transportation, energy, disaster management, and public transit are typically operated as independent systems.

As a result:

• Information sharing between systems is limited
• Local optimization often takes precedence over city-wide optimization
• Real-time coordination across systems is difficult

Chinoba

In Runtime Society,

• Traffic AI
• Energy AI
• Public transportation systems
• Disaster management systems

operate together on a shared Runtime platform, allowing the city to function as a coordinated intelligent system.

What Becomes Possible

• Real-time optimization across the entire city
• Continuous collaboration between AI systems
• Flexible and resilient urban operations

Case 2: Disaster Response

Conventional Approach

During disasters,

• Local governments
• Fire departments
• Hospitals
• Police agencies

often manage information independently.

This creates challenges such as:

• Delayed information sharing
• Fragmented decision-making
• Difficulty responding quickly to changing situations

Chinoba

Within Runtime Society,

• Local governments
• Fire departments
• Hospitals
• Drones
• Robots
• AI systems

share the same Runtime environment, dynamically coordinating and changing roles as conditions evolve.

What Becomes Possible

• Real-time situational awareness
• Flexible collaboration between humans and AI
• Faster and more effective disaster response

Case 3: Energy Management

Conventional Approach

Power plants, battery storage systems, electric vehicles, homes, and factories are often controlled independently.

As a result:

• Grid-wide optimization is difficult
• Renewable energy cannot be utilized efficiently
• Responding flexibly to power shortages and surplus energy is challenging

Chinoba

Within Runtime Society,

• Power generation facilities
• Battery storage systems
• Electric vehicles
• Homes
• Factories

coordinate through a shared Runtime platform to optimize energy distribution in real time.

What Becomes Possible

• Real-time optimization of energy supply and demand
• Efficient utilization of renewable energy
• Flexible and sustainable energy management

03 Knowledge Flow

Case 1: Knowledge Transfer from Expert Engineers

Conventional Approach

The expertise of experienced engineers is typically documented in manuals and reports.

However, several challenges remain:

• Tacit knowledge is not fully captured
• Knowledge is difficult to apply in real-world operations
• New experiences are not continuously incorporated

Chinoba

In Knowledge Flow,

• Expert engineers
• Frontline workers
• AI
• The organization’s knowledge infrastructure

continuously learn by circulating knowledge.

Knowledge is not simply stored—it is actively used in daily operations and continuously updated through new experiences.

What Becomes Possible

• Continuous transfer of expert knowledge
• Ongoing growth of organizational intelligence
• AI-powered knowledge utilization and continuous learning

Case 2: Enterprise Knowledge Network

Conventional Approach

Enterprise knowledge is often managed separately by departments such as:

• Sales
• Engineering
• Manufacturing
• Customer Support

As a result:

• Knowledge sharing across departments is limited
• The same problems are repeatedly solved
• Organizational learning is inefficient

Chinoba

With Knowledge Flow,

• Business departments
• AI
• Knowledge bases
• Operational systems

share knowledge across the organization.

Knowledge is no longer confined to individual departments but is continuously reused, enriched, and updated.

What Becomes Possible

• Cross-functional knowledge sharing
• Improved organizational learning efficiency
• Continuous growth of knowledge assets

Case 3: Education

Conventional Approach

Educational materials are traditionally created by teachers and consumed by students in a one-way learning process.

However:

• Learning outcomes are rarely reflected back into teaching materials
• Knowledge sharing among educators is limited
• AI cannot continuously participate in the learning process

Chinoba

With Knowledge Flow,

• Teachers
• Students
• AI

collaboratively improve educational content and learning resources over time.

Learning outcomes become part of future educational materials, allowing knowledge to evolve through continuous circulation.

What Becomes Possible

• Continuously evolving educational content
• Collaborative learning between humans and AI
• Ongoing circulation and inheritance of educational knowledge

04 Trust Infrastructure

Case 1: Supply Chain

Conventional Approach

Supply chains typically manage:

• Component procurement
• Manufacturing
• Inspection
• Transportation
• Quality assurance

through separate systems.

As a result:

• Data authenticity is difficult to verify
• Traceability is limited
• Root-cause analysis takes time when problems occur

Chinoba

With Trust Infrastructure,

• Component suppliers
• Manufacturing plants
• Inspection organizations
• Logistics providers
• Quality assurance systems

share operational histories on a common trust infrastructure, making every process fully traceable.

What Becomes Possible

• End-to-end traceability
• Greater trust across the entire supply chain
• More efficient quality assurance and auditing

Case 2: Medical Data Sharing

Conventional Approach

Patient records are often managed independently by individual hospitals.

This creates challenges such as:

• Limited information sharing between healthcare providers
• Difficulty verifying data authenticity
• Limited visibility into who accessed information and when

Chinoba

With Trust Infrastructure,

• Hospitals
• Clinics
• Patients
• AI

share medical data on a common trust infrastructure, enabling secure and fully traceable information management.

What Becomes Possible

• Secure and trustworthy medical data sharing
• Transparent data access histories
• Patient-centered data management

Case 3: AI Service Evaluation

Conventional Approach

AI services are typically evaluated based on performance metrics such as:

• Accuracy
• Processing speed
• Cost

However, it is much harder to evaluate:

• Whether the AI is trustworthy
• Whether it has sufficient operational experience
• Whether its decisions are explainable

Chinoba

With Trust Infrastructure,

• AI services
• Operational history
• Decision history
• User feedback

are managed as an integrated trust framework, enabling continuous evaluation of AI reliability.

What Becomes Possible

• Reliable evaluation of AI services
• Continuous improvement based on operational history
• Explainable and trustworthy AI infrastructure

05 AI Coordination

Case 1: Hospital

Conventional Approach

Hospitals often deploy separate systems such as:

• Diagnostic AI
• Medication AI
• Appointment systems
• Nursing support systems

As a result:

• AI systems cannot effectively collaborate
• Information remains fragmented across systems
• Medical staff must coordinate everything manually

Chinoba

With AI Coordination,

• Diagnostic AI
• Medication AI
• Scheduling AI
• Nursing AI
• Physicians
• Nurses

work together to deliver the most appropriate care for each patient.

What Becomes Possible

• Real-time collaboration among AI systems
• Decision support for the entire healthcare team
• Patient-centered healthcare services

Case 2: Airport

Conventional Approach

Airport systems for:

• Security
• Boarding
• Baggage handling
• Passenger guidance

are typically operated independently.

As a result:

• Information sharing is slow
• Responses to delays and congestion become locally optimized
• Passenger guidance lacks consistency

Chinoba

With AI Coordination,

• Security systems
• Boarding management systems
• Baggage management systems
• Guidance AI

share information in real time and coordinate dynamically according to changing conditions.

What Becomes Possible

• Real-time optimization across the entire airport
• Improved passenger experience
• Faster responses to delays and congestion

Case 3: Enterprise Copilot

Conventional Approach

Organizations often deploy separate AI assistants for functions such as:

• Sales AI
• Legal AI
• HR AI
• Development AI

However:

• AI systems cannot effectively share knowledge
• Cross-functional decision-making is difficult
• Humans must perform the final coordination

Chinoba

With AI Coordination,

• Sales AI
• Legal AI
• HR AI
• Development AI
• Executive management

share a common knowledge infrastructure and decision infrastructure, enabling coordinated decision support across the organization.

What Becomes Possible

• Cross-functional AI collaboration
• Organization-wide decision support
• Collaborative workflows between humans and AI

06 Multi-Agent Systems

Case 1: AI for Research Assistance

Conventional Approach

Generative AI can assist with literature reviews and document writing.

However, several challenges remain:

• A single AI must handle every task, limiting domain expertise
• Literature review, analysis, hypothesis generation, and experimental design remain disconnected
• It is difficult to support the entire research process continuously

Chinoba

With Multi-Agent Systems,

• Literature Review AI
• Hypothesis Generation AI
• Experimental Design AI
• Peer Review Support AI

each performs a specialized role while collaborating as a unified research team.

What Becomes Possible

• Specialized AI agents with clearly defined roles
• Greater efficiency throughout the research process
• Collaborative research support through multiple AI agents

Case 2: Customer Support

Conventional Approach

Customer inquiries are typically handled by a single AI chatbot.

However:

• It is difficult to answer inquiries requiring specialized expertise
• Cases spanning multiple departments require manual coordination
• Complex cases often need to be escalated to human staff

Chinoba

With Multi-Agent Systems,

• Reception AI
• Product Support AI
• Billing AI
• Shipping AI

collaborate by dividing responsibilities according to the customer’s request.

What Becomes Possible

• High-quality support through specialized AI agents
• Cross-functional collaborative customer service
• Improved customer experience

Case 3: Logistics Robotics

Conventional Approach

Logistics operations often manage:

• Warehouse management
• Delivery management
• Inventory management
• Route optimization

through separate systems.

As a result:

• Coordination between systems is limited
• End-to-end optimization is difficult
• Responding flexibly to changing conditions is challenging

Chinoba

With Multi-Agent Systems,

• Warehouse AI
• Delivery AI
• Inventory Management AI
• Route Optimization AI

collaborate continuously to optimize the entire logistics operation.

What Becomes Possible

• Real-time collaboration among AI agents
• End-to-end logistics optimization
• Flexible and autonomous logistics systems

07 AI Economics & Intelligence Field Economics

Case 1: Knowledge Marketplace

Conventional Approach

Within organizations and research institutions,

• Knowledge
• Expertise
• Experience
• Ideas

are often shared without contributors receiving appropriate recognition.

As a result:

• Incentives for knowledge sharing are weak
• Tacit knowledge remains hidden within organizations
• Continuous knowledge creation is difficult to sustain

Chinoba

With AI Economics,

• Knowledge contributors
• Users
• AI
• Organizations

share knowledge distribution and usage histories, enabling fair evaluation and reward for knowledge contributions.

What Becomes Possible

• Fair value recognition for knowledge contributors
• A sustainable economy that encourages knowledge sharing
• Continuous creation of new knowledge

Case 2: Open Research

Conventional Approach

Researchers are typically evaluated based on:

• Number of publications
• Citation counts
• Journal prestige

However, important contributions such as:

• Publishing datasets
• Developing software
• Contributing ideas and discussions

are often undervalued.

Chinoba

With AI Economics,

all contributions to:

• Research
• AI
• Data
• Software
• Knowledge infrastructure

are made visible and become part of the overall evaluation of knowledge creation.

What Becomes Possible

• Recognition of diverse research contributions
• Promotion of open research
• Sustainable research communities

Case 3: AI Contribution Economy

Conventional Approach

The value of AI is typically measured by:

• Processing performance
• Business impact
• Cost reduction

However, it is difficult to evaluate:

• How humans and AI collaborated
• How much AI contributed to knowledge creation
• What value was generated for the organization as a whole

Chinoba

With AI Economics,

the contributions of:

• AI
• Humans
• Organizations

are made visible, allowing knowledge creation and value generation to be evaluated as economic activities.

What Becomes Possible

• Transparent evaluation of human and AI contributions
• A new economic model centered on knowledge creation
• The realization of Intelligence Field Economics

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08 Governance & Boundary Systems

Case 1: Enterprise AI Governance

Conventional Approach

Organizations are increasingly deploying multiple AI systems.

However:

• AI permissions become difficult to manage
• Responsibility for final decisions becomes unclear
• Defining the appropriate scope of AI usage is challenging

Chinoba

With Governance & Boundary Systems,

the roles and permissions of:

• AI
• Employees
• Administrators
• Business systems

are clearly defined to ensure safe and accountable AI operations.

What Becomes Possible

• Clear AI permission management and accountability
• Stronger organizational governance
• Safe and trustworthy AI operations

Case 2: Inter-Enterprise Collaboration

Conventional Approach

When multiple organizations collaborate using AI,

they often face challenges such as:

• Difficulty controlling the sharing of confidential information
• Different data governance policies
• Unclear security and responsibility boundaries

Chinoba

With Governance & Boundary Systems,

the boundaries between:

• Company A
• Company B
• AI services
• Data infrastructure

are clearly maintained, enabling secure collaboration between AI systems across organizations.

What Becomes Possible

• Safer inter-organizational data sharing
• AI collaboration while maintaining clear boundaries
• Enterprise collaboration with built-in governance

Case 3: Personal AI

Conventional Approach

With personal AI services,

users often have limited visibility into how their information is used.

As a result:

• Privacy management becomes difficult
• Users cannot precisely control what information is shared with AI
• Access control based on usage purpose is limited

Chinoba

With Governance & Boundary Systems,

clear boundaries are established between:

• The user
• Personal AI
• Various AI services

allowing users to precisely control what information is shared for each intended purpose.

What Becomes Possible

• Fine-grained access control for personal data
• Privacy-aware AI utilization
• A personal AI infrastructure fully controlled by the user

09 Physical AI & Cyber-Physical Systems

Case 1: Smart Factory

Conventional Approach

In manufacturing environments,

• Robots
• Industrial equipment
• Sensors
• AI

are often controlled independently.

As a result:

• Coordination between systems is limited
• Optimizing the entire production line is difficult
• Responding flexibly to unexpected events is challenging

Chinoba

With Physical AI & Cyber-Physical Systems,

• Robots
• Industrial equipment
• AI
• Sensors

share information in real time and coordinate the entire production line as an integrated system.

What Becomes Possible

• Real-time optimization of the entire production line
• Coordinated control between AI and industrial equipment
• Flexible and autonomous manufacturing systems

Case 2: Automated Warehouse

Conventional Approach

Automated warehouses often manage:

• Transport robots
• Inventory management
• Delivery planning

through separate systems.

As a result:

• Responding to inventory fluctuations takes time
• Collaboration between robots is limited
• Warehouse-wide optimization is difficult

Chinoba

With Physical AI & Cyber-Physical Systems,

• Transport robots
• Inventory Management AI
• Delivery systems
• Equipment Control AI

collaborate in real time to optimize warehouse operations.

What Becomes Possible

• Real-time optimization across the entire warehouse
• Coordinated operation between robots and AI
• Flexible and efficient logistics systems

Case 3: Smart Agriculture

Conventional Approach

Agricultural operations often manage:

• Drones
• Weather information
• Irrigation systems
• Agricultural machinery

independently.

As a result:

• Data becomes fragmented
• Rapid responses to changing conditions are difficult
• Optimizing the entire farming operation is challenging

Chinoba

With Physical AI & Cyber-Physical Systems,

• Drones
• Weather AI
• Irrigation systems
• Agricultural machinery

collaborate in real time to optimize the entire farm.

What Becomes Possible

• Real-time optimization across agricultural operations
• AI-powered precision agriculture
• Sustainable and efficient farm management

10 Public AI & Algorithmic Governance

Case 1: Policy Simulation

Conventional Approach

Public policies are typically designed based on:

• Statistical data
• Expert knowledge
• Historical case studies

However:

• Predicting policy impacts in advance is difficult
• Evaluating interactions between multiple policies is challenging
• Understanding broader social impacts is limited

Chinoba

With Public AI & Algorithmic Governance,

• Government agencies
• AI
• Statistical data
• Simulation models

work together to evaluate policy impacts from multiple perspectives before implementation.

What Becomes Possible

• Pre-deployment policy simulation
• Evidence-based policymaking
• More transparent and rational public decision-making

Case 2: Urban Planning

Conventional Approach

Areas such as:

• Transportation
• Healthcare
• Education
• Infrastructure

are often planned independently.

As a result:

• Cross-department collaboration is difficult
• City-wide optimization is limited
• Responding to future demographic and societal changes is challenging

Chinoba

With Public AI & Algorithmic Governance,

• Transportation systems
• Healthcare providers
• Educational institutions
• Infrastructure Management AI

utilize shared knowledge infrastructure and AI to optimize the city as a unified system.

What Becomes Possible

• City-wide optimization
• More efficient public services
• Sustainable urban planning

Case 3: AI for Public Administration

Conventional Approach

When AI is introduced into government services,

several challenges arise:

• Decision-making processes are difficult to understand
• Citizens have limited visibility into how results are produced
• Governments struggle to fulfill accountability requirements

Chinoba

With Public AI & Algorithmic Governance,

• Government officials
• AI
• Citizens
• Administrative systems

collaborate to provide explainable public services with transparent decision-making.

What Becomes Possible

• Explainable AI-powered public services
• Fair and transparent administrative decisions
• Digital government trusted by citizens

11 AI Infrastructure & Hardware

Case 1: Enterprise AI Platform

Conventional Approach

Organizations often deploy:

• Large Language Models (LLMs)
• Search systems
• Databases
• Business applications

as separate systems.

As a result:

• AI systems are managed independently
• Selecting the appropriate model for each task is difficult
• Operational costs and governance become increasingly complex

Chinoba

With AI Infrastructure & Hardware,

• Multiple LLMs
• Search infrastructure
• Business systems
• AI Gateway

are integrated into a common platform, allowing the most appropriate AI model to be selected for each use case.

What Becomes Possible

• Unified operation of multiple LLMs
• Optimized AI utilization
• Secure and scalable enterprise AI infrastructure

Case 2: Edge AI

Conventional Approach

In environments such as factories and hospitals,

communication delays and network failures with cloud-based AI create challenges such as:

• Difficulty making real-time decisions
• Dependence on network connectivity
• The need to transmit sensitive data to external cloud services

Chinoba

With AI Infrastructure & Hardware,

• Edge devices
• AI models
• Sensors
• Cloud infrastructure

work together to perform real-time inference directly at the edge.

What Becomes Possible

• Low-latency AI inference
• Real-time decision-making on-site
• Secure and reliable AI operations

Case 3: AI Data Center

Conventional Approach

As AI adoption grows,

managing resources such as:

• GPUs
• Power
• Networks
• Storage

becomes increasingly complex.

As a result:

• GPU utilization becomes imbalanced
• Power consumption increases
• Operational costs continue to rise

Chinoba

With AI Infrastructure & Hardware,

• GPU resources
• Power management systems
• AI workloads
• Data center infrastructure

are managed as an integrated environment to create highly efficient AI execution platforms.

What Becomes Possible

• Optimization of GPU and power resources
• Reduced AI operational costs
• Sustainable AI infrastructure

13 Graph Intelligence & Relational Systems

Case 1: Supply Chain Analysis

Conventional Approach

In supply chains, information about:

• Companies
• Components
• Logistics
• Manufacturing
• Sales

is often managed separately.

As a result:

• Relationships between organizations are difficult to understand
• Bottlenecks are difficult to identify
• End-to-end optimization is challenging

Chinoba

With Graph Intelligence,

the relationships among:

• Companies
• Components
• Logistics
• Manufacturing sites
• Sales locations

are represented as a graph, enabling visualization and analysis of the entire supply chain.

What Becomes Possible

• End-to-end supply chain visualization
• Relationship-based risk analysis
• Network-wide optimization

Case 2: Fraud Detection

Conventional Approach

Fraud detection is typically based on factors such as:

• Transaction amounts
• Transaction frequency
• Rule-based detection

However, it is difficult to detect:

• Fraud spanning multiple organizations
• Hidden relationships between individuals
• Abnormal patterns across an entire network

Chinoba

With Graph Intelligence,

• Money transfer histories
• Corporate relationships
• Human relationship networks
• Transaction histories

are analyzed as interconnected graphs to detect fraud through complex relationship patterns.

What Becomes Possible

• Relationship-based fraud detection
• Discovery of hidden networks
• Advanced risk analysis

Case 3: Enterprise Knowledge Graph

Conventional Approach

Enterprise knowledge is often distributed across:

• Documents
• Databases
• Files
• Individual employees

As a result:

• Finding the right knowledge is difficult
• Relationships between people and knowledge remain unclear
• The overall knowledge structure of the organization is difficult to understand

Chinoba

With Graph Intelligence,

• People
• Knowledge
• Projects
• Decisions

are connected as a graph, creating an enterprise-wide knowledge network.

What Becomes Possible

• Visualization of organizational knowledge
• Advanced knowledge discovery
• Relationship-driven decision support

14 Emergent & Distributed Intelligence

Case 1: Collaborative Research AI

Conventional Approach

Generative AI can assist with tasks such as:

• Literature search
• Summarization
• Document writing

However:

• A single AI cannot cover diverse areas of expertise
• Coordinating the entire research process is difficult
• There is no mechanism for multiple AI systems to create new knowledge together

Chinoba

With Emergent & Distributed Intelligence,

• Literature Review AI
• Hypothesis Generation AI
• Experimental Support AI
• Researchers

collaborate throughout the research process to co-create new knowledge.

What Becomes Possible

• Knowledge emergence through collaboration among AI systems
• Human-AI collaborative research
• A continuously evolving research infrastructure

Case 2: Collective Intelligence

Conventional Approach

Within communities,

even when individuals actively share knowledge,

several challenges remain:

• Information becomes fragmented
• Discussions are difficult to accumulate over time
• Knowledge is not effectively inherited by the community

Chinoba

With Emergent & Distributed Intelligence,

• Communities
• AI
• Researchers
• Users

continuously share knowledge, learn together, and cultivate collective intelligence.

Knowledge is no longer owned by individuals alone—it grows as the intelligence of the community itself.

What Becomes Possible

• Collective intelligence created by humans and AI
• Continuous growth of shared knowledge
• Organization-wide and community-wide learning

Case 3: Autonomous Organization

Conventional Approach

Within organizations,

humans remain responsible for decision-making,

while AI primarily supports individual business tasks.

However:

• Organizations struggle to learn continuously as a whole
• Knowledge becomes fragmented across departments
• AI learning is not effectively reflected throughout the organization

Chinoba

With Emergent & Distributed Intelligence,

• Humans
• AI
• Organizations
• Knowledge infrastructure

continuously learn and collaborate, allowing the organization itself to function as an intelligent system.

Knowledge and experience are shared across the organization and continuously flow into future decision-making.

What Becomes Possible

• An intelligence infrastructure that continuously learns
• Ongoing collaboration between humans and AI
• Self-evolving autonomous organizations driven by emergence

Conclusion

Until now, the evolution of AI has largely focused on building smarter models.

However, the next stage of AI is not defined by model performance alone.

What matters is an intelligence architecture that enables AI to collaborate with people, integrate with organizations, and operate as a trusted part of society.

At Chinoba Research, we explore next-generation AI infrastructure through new perspectives such as:

• Recording decisions
• Enabling the continuous flow of knowledge
• Coordinating multiple AI systems
• Designing trust
• Viewing society itself as a Runtime

The future of AI is not about building larger models.

It is about designing better relationships.

That is the vision pursued by Chinoba Research.