REQUEST FOR EXPRESSIONS OF INTEREST
(CONSULTING SERVICES – FIRMS SELECTION)

 

Country: Republic of Uzbekistan

Project name: Institutional Capacity Building Project

Credit No.: IDA 6431-UZ

 

Assignment Title: Developing Software based on AI Solutions to Detect Bid-Rigging in Public Procurement Procedures.

 

Reference No.: AMC-AIPROCURE-CQS

 

The Ministry of Economy and Finance of the Republic of Uzbekistan has received financing from the World Bank toward the cost of the Institutional Capacity Building Project and intends to apply part of the proceeds for Developing software based on AI solutions to detect bid-rigging in public procurement procedures (“Services”) for the Competition Promotion and Consumer Protection of the Republic of Uzbekistan (“Committee”).

 

The Committee seeks a company or consortium of companies (hereinafter – the Contractor) to develop and implement a sophisticated and reliable AI-powered software system designed to detect patterns of bid-rigging and collusive conduct in public procurement procedures. The system will automatically scan and analyze tender data to generate risk assessments and identify suspicious indicators that may suggest possible coordinated behaviour or other forms of fraud. By leveraging advanced machine learning algorithms and data analytics, this system aims to detect subtleties and anomalies in bidding data that might otherwise go unnoticed by human auditors.

 

The Ministry of Economy and Finance of the Republic of Uzbekistan now invites eligible consulting firms (“Consultants”) to indicate their interest in providing the Services. Interested Consultants should provide information demonstrating that they have the required qualifications and relevant experience to perform the Services by submitting directly to the piu_mof@mail.ru.

 

The Qualification requirements for the Consultants are:

A. Technical Competencies

1. Relevant experience

The Contractor must demonstrate at least two (2) years of experience and a proven track record of at least two (2) to three (3) successfully implemented AI or data-driven projects. The Contractor will propose a dedicated team, assigned to the task, led by an experienced IT Project Manager, and that shall meet the following requirements, as confirmed by relevant CVs and other applicable documents:

1. Programming Proficiency

Demonstrated expertise in one or more programming languages, such as Python, Java, C#, JavaScript, or Ruby. Experience in implementing machine learning algorithms is highly preferred.

1. Frameworks and Libraries

Proficiency in widely used AI and software development frameworks, including but not limited to TensorFlow, PyTorch, Scikit-learn for AI development, and React, Angular, Django, or Spring for front-end and back-end programming.

1. Database Management

Solid experience with both relational (SQL) and non-relational (NoSQL) databases. The candidate should demonstrate the ability to manage large datasets, perform real-time data analysis, and implement effective data modeling and optimization techniques.

1. Software Development Methodologies

Familiarity with collaborative development approaches such as Agile, Scrum, or DevOps, ensuring alignment with iterative delivery schedules and team-based workflows.

1. Testing, Validation, Quality assessment

Strong skills in unit testing, integration testing, and AI model validation techniques to guarantee system reliability, accuracy, and performance standards. Quality criteria include achieving predefined metrics (e.g., Precision, Recall, RR-AUC, and others) and passing a formal acceptance testing procedure involving the customer.

B. Software Architecture and Integration

1. Design Patterns

Knowledge of scalable architectural design patterns such as Model-View-Controller (MVC), Microservices, and Event-Driven Architecture, enabling maintainable and modular software solutions.

1. API Development and Integration

Proven ability to design, develop, and integrate RESTful or GraphQL APIs, ensuring interoperability with existing government procurement systems and platforms.

The software must be deployed on servers designated by the Committee, with all data stored within the territory of the Republic of Uzbekistan, in full compliance with national legislation on personal data protection. The system shall include mechanisms for daily backup, disaster recovery, and secure access control with role-based authorization.

C. Domain Knowledge: Procurement and Anti-Corruption

1. Government Procurement Regulations

Familiarity with public procurement frameworks, especially in the context of anti-corruption, transparency, and fair competition. Experience identifying schemes involving collusion, falsification, or conflict of interest is strongly preferred as confirmed by the relevant implemented projects/assignments.

1. Risk and Fraud Analysis

Practical experience in designing AI solutions for risk assessment and fraud detection, with a focus on identifying suspicious patterns and anomalies in tender bidding processes that may indicate possible collusion among participants. The final decision is made by a human based on the results of the analysis performed by the artificial intelligence system.

D. AI-Specific Expertise

1. AI Solution Development

Demonstrated success in developing AI-based systems for anomaly detection, fraud prevention, or related domains. Experience in working with real-time, large-scale datasets is required.

1. Client References

Documented track record of successful delivery of similar AI solutions in public or government-related projects, supported by client endorsements or references.

1. Artificial Intelligence Knowledge

Neural Networks: Expertise in designing and training models for pattern recognition and predictive analytics.

Unsupervised Learning: Deep understanding of clustering methods
(e.g., DBSCAN, K-Means) and anomaly detection algorithms (e.g., Isolation Forest).

Algorithmic Design: deep understanding of fraud detection methods, including the use of natural language processing (NLP) for text analysis
(e.g., document authentication) and automatically identifying signs that may indicate document forgery, and using advanced artificial intelligence tools to detect anomalous relationships and indicators of possible collusion in procurement.

1. Non-functional requirements:

The developed system must comply with the following non-functional requirements:

1. processing capacity: analysis of at least 10,000 bid submissions within
   5 minutes;
2. accuracy: detection models should achieve a minimum of 90% precision and 85% recall in pilot testing;
3. scalability: ability to handle a tenfold increase in data volume without complete redesign;

d. multilingual user interface: Uzbek (state language) and Russian must be fully supported.

E. Soft Skills and Work Ethics

1. Collaborative Mindset

Ability to work closely with government counterparts, domain experts, and regulatory bodies, contributing effectively in multidisciplinary environments.

1. Problem-Solving Abilities

Strong analytical skills to resolve technical challenges, optimize algorithmic performance, and adapt models based on evolving data patterns.

1. Time and Task Management

Demonstrated ability to manage multiple priorities, meet deadlines, and adhere to project milestones within defined timelines.

 

The attention of interested Consultants is drawn to Section III of the World Bank’s “Procurement Regulations for IPF Borrowers” July 2016 (“Procurement Regulations”), setting forth the World Bank’s policy on conflict of interest. In addition, please refer to the following specific information on conflict of interest related to this assignment: Consultants shall not be hired for any assignment that would be in conflict with their prior or current obligations to other clients, or that may place them in a position of being unable to carry out the assignment in the best interests of the Ministry of Finance of the Republic of Uzbekistan.

 

A Consultant will be selected in accordance with the Consultant Qualification Selection (CQS) method set out in the Procurement Regulations.

 

Further information can be obtained at the address below during office hours from 9:00 a.m. to 18:00 p.m. Tashkent time.

 

Expressions of interest must be delivered in a written form in English, Russian or Uzbek languages to the address below (in person, or by mail, or by e-mail) by 12^nd January 2026.

 

Project Implementation Unit under the
Ministry of Economy and finance of the Republic of Uzbekistan

Attn: Sirojiddin Imanov, Manager of the Project

Uzbekistan, 100017, Tashkent city, 29, Istiklol St.

Tel: +998 90331662/ 712023113 (05493)

E-mail: piu_mof@mail.ru

 

 

 

As part of the EOI, the consultant should include the following information:

 

• Consulting Firm Information

 

Date:	Country of Incorporation:
Consultant Name:	Acronym:
EOI Submission Authorized by:	Position

 

• Associations (Joint Venture or Sub-consultancy)

 

Consultant	Acronym	Country of Incorporation	Joint Venture (JV) or Sub-consultant	EOI Submission Authorized By	Position

 

Present the rationale for and benefits of working in association (JV or Sub-consultant) with others rather than undertaking the assignment independently (as appropriate). Describe the proposed management and coordination approach of the association and the role of each firm.

 

• Technical competence

Project References - highlight the technical qualifications of your entity/consortium in undertaking similar assignments. Provide details of past experiences working with similar project authorities

 

• Geographical experience

 

Project References, present experiences in similar geographic areas.

 

• Management Competence

• Describe standard policies, procedures, and practices that your entity has to assure quality interaction with clients and outputs. Please state if your company is ISO certified.
• How your firm/consortium handles complaints concerning the performance of experts or quality of the reports submitted for previous and future assignments? What internal controls are in place to address and resolve complaints.
• How you ensure the quality of your firm’s/consortium’s performance over the life of assignments.

• Projects references

Present relevant projects to demonstrate the firm’s technical qualifications and geographical experience (maximum 10 projects).

N	Project	Period	Client	Country	Firm
1
2
3
4

 

• Summary for the Projects listed above

 

Project Title
Country / Region
Start Date
Completion Date
Continuous / Intermittent
Client
Funding Source
Description	(indicate your role and input in man-months)

 

• Eligibility

 

We, the undersigned, certify to the best of our knowledge and belief:

 

• We have read the advertisement, including the terms of reference (TOR), for this assignment (YES/NO)

 

• Neither the consulting firm nor its JV member or sub-consultant or any of its experts prepared the TOR for this activity (YES/NO)

 

• We confirm that the project references submitted as part of this EOI accurately reflect the experience of the specified firm/consortium. (YES/NO)

 

• Ensuing assignment resulting from our work product under this assignment, our firm, JV member or sub-consultant, and the expert(s) will be disqualified from short-listing and participation in the assignment.

 

• The lead entity and JV member or sub-consultant are NOT currently sanctioned by or other MDBs. Neither the consulting firm nor the JV member or sub-consultant has ever been convicted of an integrity-related offense or crime related to theft, corruption, fraud, collusion or coercion.

 

 

 

TERMS OF REFERENCE

for the Developing Software based on AI Solutions to Detect Bid-Rigging
in Public Procurement Procedures

1. Background

The project is designed to enhance the capacity of the Competition Promotion and Consumer Protection of the Republic of Uzbekistan (hereinafter- Committee) in the timely and proper detection of bid-rigging, collusive conduct, and other anti-competitive actions and behaviors in the public procurement process, through the deployment of advanced AI-based software solutions. These solutions are specifically tailored to detect and counteract instances of bid-rigging and collusive conduct in public procurement procedures — that is, coordinated anti-competitive behaviour among bidders that distorts competition and procurement outcomes. The AI-based tool will operate as an automated screening and risk-analysis instrument, designed to flag potentially suspicious patterns in tender data for subsequent expert review, without replacing the formal investigative and evidentiary procedures established under the legislation of the Republic of Uzbekistan on public procurement and competition. By leveraging state-of-the-art AI technologies for the surveillance and in-depth analysis of procurement activities, the initiative aims to significantly bolster the Committee's ability to identify and address such corrupt practices more effectively and efficiently.

In recent years, the Committee’s monitoring and investigative capacity has been strengthened by the development of the digital portal “Fair Tech”, which integrates 4 core procurement platforms: xarid.uzex.uz, xt.xarid.uz, tender.ms.uz, and newcooperation.uz. These systems provide structured data that can be checked for irregularities. In the near future, the integration of AI solutions will enable the automated identification of violations; however, for now, the process remains heavily dependent on human judgment.

The integration of these AI tools will significantly improve the Committee's oversight and enforcement capabilities, ensuring that procurement processes are carried out with heightened transparency and integrity. This is crucial for maintaining public trust and for the fair distribution of public resources. Furthermore, the use of AI will enable the Committee to perform its regulatory functions with greater precision, reducing the likelihood of human error and bias in the detection of bid-rigging, collusive conduct, and other anti-competitive practices.

The project not only supports the Committee in its efforts to uphold standards of fairness and legality in public procurement but also empowers it to serve as a model of innovative governance. As the Committee enhances its operational efficiencies and adopts more advanced technological tools, it sets a precedent for other regulatory bodies, showcasing the transformative power of technology in enhancing public sector accountability and effectiveness. This initiative is expected to lead to broader reforms in procurement practices, fostering a culture of integrity and transparency that aligns with international best practices in public administration.

Similar AI-driven solutions have been successfully developed and introduced in countries like the UK, Brazil, and Russia, among others. These systems have proven effective in enhancing the transparency and integrity of public procurement processes by detecting and preventing bid-rigging and collusive conduct activities. By leveraging advanced analytics and machine learning algorithms, these tools have provided regulatory bodies with the capability to scrutinize bid submissions in real-time, identify patterns indicative of collusion, and take preemptive actions to safeguard fair competition. The positive outcomes from these implementations underscore the potential benefits of adopting similar technologies in Uzbekistan, promising significant improvements in the efficiency and reliability of procurement practices.

2. Objective

The Committee seeks a company or consortium of companies (hereinafter – the Contractor) to develop and implement a sophisticated and reliable AI-powered software system designed to detect patterns of bid-rigging and collusive conduct in public procurement procedures. The system will automatically scan and analyze tender data to generate risk assessments and identify suspicious indicators that may suggest possible coordinated behaviour or other forms of fraud. By leveraging advanced machine learning algorithms and data analytics, this system aims to detect subtleties and anomalies in bidding data that might otherwise go unnoticed by human auditors.

This AI system is intended to serve as a critical screening and suspicion-raising tool for the Committee and other relevant government agencies, enhancing their ability to assess the risk of collusive behaviors and identify suspicious patterns that may indicate possible integrity issues in public procurement procedures. The tool will support the Committee in identifying high‑risk tenders for further manual review. Its deployment will allow these agencies to proactively monitor and scrutinize every phase of the bidding process, thereby preventing collusion and bid rigging before it can affect outcomes. The software enables the identification and assessment of fraud likelihood by providing detailed analytics on probability factors and anomalies, supporting informed decisions on additional checks and thereby contributing to the development of more effective regulatory policies and procedures.

Additionally, this system will facilitate a more transparent bidding environment by providing all stakeholders with insights into the fairness and competitiveness of the process, thereby boosting confidence in public procurement. The ultimate goal is to ensure that public resources are allocated efficiently and ethically, fostering a climate of trust and reliability in government transactions and services. This, in turn, will contribute to a more stable and predictable market environment, encouraging greater participation and competition among potential vendors and Contractors.

3. Scope of work

The scope of this project encompasses a comprehensive suite of tasks necessary for the successful implementation of the AI software designed to examine and assess unusual patterns and behaviors in submitted applications, with the purpose of producing risk evaluations and highlighting suspicious indicators that could suggest potential collusion or other fraudulent activities. These tasks are divided into several key areas:

1. Contractor shall, in consultation with the designated stakeholders, prepare a detailed Implementation Plan that will define the precise functional and non-functional requirements, quality metrics, integration standards, and implementation procedures of the AI solution. The deployment and operation of the software shall be carried out strictly in accordance with the applicable legislation of the Republic of Uzbekistan. The work on the assignment shall be implemented in line with the Implementation Plan, which will serve as the primary guiding document for development and implementation.
2. Contractor shall conduct a detailed assessment of the target databases to evaluate the availability, format, consistency, quality, level of integration and interoperability, and other parameters of data and databases to be used for learning algorithms, as well as the IT infrastructure of the Committee.

• Data sources and data handling requirements:

The Contractor shall utilise datasets made available by the Committee and other authorised public sources, including but not limited to procurement notices, bid submissions, contract award records, firm and beneficial ownership registries, and anonymised data from past enforcement or investigation cases, where applicable.

The Contractor shall propose detailed procedures for data cleaning, integration, and anonymization to ensure the integrity, consistency, and confidentiality of inputs. All procedures must fully comply with the Law of the Republic of Uzbekistan ‘On Personal Data’ and other relevant information security regulations. Sensitive or confidential information must be handled in accordance with the authorization provided by the Committee, and shall not be accessed or disclosed beyond the purposes of this project.

• Data access and authorisation:

Access to procurement and related datasets required for system development and testing shall be arranged and authorised exclusively by the Competition Promotion and Consumer Protection Committee (CPCPC). The Contractor shall not independently obtain or request access to confidential or restricted information from third parties. The Committee will facilitate all necessary permissions and data transfers in accordance with the applicable legislation of the Republic of Uzbekistan, including regulations on public procurement, competition, and personal data protection. Any use of such data by the Contractor shall remain strictly within the authorised scope and purpose of this project.

• System interoperability and data integration:

The Contractor shall ensure that the system is fully interoperable with existing e-procurement platforms, contract databases, and the Committee’s internal case-management systems. Data exchange shall be implemented through secure APIs or other authorised interfaces, in compliance with the information security and data governance regulations of the Republic of Uzbekistan.

 

• Technical requirements and analytical capabilities:

The Contractor shall specify the analytical methods and algorithms to be used — including machine learning, anomaly-detection and network-analysis techniques — and incorporate explainability features so that results are understandable to non-technical Committee staff. The system shall meet defined performance metrics (precision, recall, false-positive rate) and be capable of flagging classic bid-rigging and collusion patterns such as bid rotation, identical bids, market allocation, unusual bid timing, and price clustering.

• User interface and visualization:

The Contractor shall design a user-friendly interface and interactive dashboard that visualises suspicious patterns, trends, and company linkages detected by the system. The interface shall be adapted for non-technical users, such as procurement officers and auditors, and include filtering and search functionalities by region, sector, contract value, and other relevant parameters.

1. Designing the foundational architecture of the AI system, ensuring interoperability with existing procurement platforms and online databases (via API) used by the Committee and other relevant government agencies. The minimum functional requirements of the AI system must include:

1. detection of abnormal price deviations between bids;
2. identification of repeated use of identical contact details, IP addresses, or email domains by different participants;
3. recognition of coordinated participation patterns among companies across multiple tenders;
4. detection of suspicious timing anomalies (e.g., bids submitted within identical short intervals);
5. generation of automatic alerts and analytical reports for the Committee staff.

1. Aggregating and harmonizing data from various sources within the procurement ecosystem to ensure comprehensive analysis capabilities.
2. Developing a phased deployment plan for piloting and scaling up the AI software across selected public procurement procedures, with potential for broader application based on performance results.
3. Contractor shall provide at least three in-person training sessions for Committee staff and guarantee post-implementation technical support for a period of 12 months after final acceptance of the system.
4. Implementing feedback systems to gather user input on software performance and areas for improvement and regularly updating the software to incorporate new features, security patches, and improvements based on user feedback and technological advancements.

 

4. Requirements for Contractor

A. Technical Competencies

1. Relevant experience

The Contractor must demonstrate at least two (2) years of experience and a proven track record of at least two (2) to three (3) successfully implemented AI or data-driven projects. The Contractor will propose a dedicated team, assigned to the task, led by an experienced IT Project Manager, and that shall meet the following requirements, as confirmed by relevant CVs and other applicable documents:

1. Programming Proficiency

Demonstrated expertise in one or more programming languages, such as Python, Java, C#, JavaScript, or Ruby. Experience in implementing machine learning algorithms is highly preferred.

1. Frameworks and Libraries

Proficiency in widely used AI and software development frameworks, including but not limited to TensorFlow, PyTorch, Scikit-learn for AI development, and React, Angular, Django, or Spring for front-end and back-end programming.

1. Database Management

Solid experience with both relational (SQL) and non-relational (NoSQL) databases. The candidate should demonstrate the ability to manage large datasets, perform real-time data analysis, and implement effective data modeling and optimization techniques.

1. Software Development Methodologies

Familiarity with collaborative development approaches such as Agile, Scrum, or DevOps, ensuring alignment with iterative delivery schedules and team-based workflows.

1. Testing, Validation, Quality assessment

Strong skills in unit testing, integration testing, and AI model validation techniques to guarantee system reliability, accuracy, and performance standards. Quality criteria include achieving predefined metrics (e.g., Precision, Recall, RR-AUC, and others) and passing a formal acceptance testing procedure involving the customer.

B. Software Architecture and Integration

1. Design Patterns

Knowledge of scalable architectural design patterns such as Model-View-Controller (MVC), Microservices, and Event-Driven Architecture, enabling maintainable and modular software solutions.

1. API Development and Integration

Proven ability to design, develop, and integrate RESTful or GraphQL APIs, ensuring interoperability with existing government procurement systems and platforms.

The software must be deployed on servers designated by the Committee, with all data stored within the territory of the Republic of Uzbekistan, in full compliance with national legislation on personal data protection. The system shall include mechanisms for daily backup, disaster recovery, and secure access control with role-based authorization.

C. Domain Knowledge: Procurement and Anti-Corruption

1. Government Procurement Regulations

Familiarity with public procurement frameworks, especially in the context of anti-corruption, transparency, and fair competition. Experience identifying schemes involving collusion, falsification, or conflict of interest is strongly preferred as confirmed by the relevant implemented projects/assignments.

1. Risk and Fraud Analysis

Practical experience in designing AI solutions for risk assessment and fraud detection, with a focus on identifying suspicious patterns and anomalies in tender bidding processes that may indicate possible collusion among participants. The final decision is made by a human based on the results of the analysis performed by the artificial intelligence system.

D. AI-Specific Expertise

1. AI Solution Development

Demonstrated success in developing AI-based systems for anomaly detection, fraud prevention, or related domains. Experience in working with real-time, large-scale datasets is required.

1. Client References

Documented track record of successful delivery of similar AI solutions in public or government-related projects, supported by client endorsements or references.

1. Artificial Intelligence Knowledge

Neural Networks: Expertise in designing and training models for pattern recognition and predictive analytics.

Unsupervised Learning: Deep understanding of clustering methods
(e.g., DBSCAN, K-Means) and anomaly detection algorithms (e.g., Isolation Forest).

Algorithmic Design: deep understanding of fraud detection methods, including the use of natural language processing (NLP) for text analysis
(e.g., document authentication) and automatically identifying signs that may indicate document forgery, and using advanced artificial intelligence tools to detect anomalous relationships and indicators of possible collusion in procurement.

1. Non-functional requirements:

The developed system must comply with the following non-functional requirements:

1. processing capacity: analysis of at least 10,000 bid submissions within
   5 minutes;
2. accuracy: detection models should achieve a minimum of 90% precision and 85% recall in pilot testing;
3. scalability: ability to handle a tenfold increase in data volume without complete redesign;

d. multilingual user interface: Uzbek (state language) and Russian must be fully supported.

E. Soft Skills and Work Ethics

1. Collaborative Mindset

Ability to work closely with government counterparts, domain experts, and regulatory bodies, contributing effectively in multidisciplinary environments.

1. Problem-Solving Abilities

Strong analytical skills to resolve technical challenges, optimize algorithmic performance, and adapt models based on evolving data patterns.

1. Time and Task Management

Demonstrated ability to manage multiple priorities, meet deadlines, and adhere to project milestones within defined timelines.

5. Deliverables and timelines

The Developer will be responsible for the following deliverables within the stipulated timeframe:

Phase 1: Inception and Design (Months 1-3):

Detailed Inception Report, including: i) a comprehensive data and IT infrastructure diagnostics, ii) a detailed implementation plan with key milestones and a clear description of functions and responsibilities of the involved parties/personnel, including those of the Committee as relevant and needed, and iii) System Architecture Design Document and phased deployment plan.

Phase 2: Development and Testing (Months 4-9):

1. Alpha and Beta versions of the AI software for pilot testing;
2. interim progress reports outlining methodology and implementation steps.

Phase 3: Implementation and Finalization (Months 10-12):

1. a fully functional and tested AI software system that provides risk assessments and analytical signals;
2. comprehensive user manuals and training materials for end-users;
3. delivery of at least three training sessions for Committee staff;
4. Final Report detailing the project outcomes and performance metrics;
5. transfer of all intellectual property, source code, and documentation to the Committee.

6. Terms of Payment

Payments shall be milestone-based:

25% – upon delivery of Phase I and approval of deliverables by the Committee;

50% – upon delivery of Phase II and approval of deliverables by the Committee;

25% – upon delivery of Phase III and approval of deliverables by the Committee.

Payment conditions:

Within 10 working days of submitting the invoice, subject to acceptance by the Committee. Up to 10% retention may be withheld until final acceptance.

7. Confidentiality and non-disclosure

The Contractor shall treat as confidential all information, data, documents, and materials received or generated in connection with the execution of this Contract. Such information shall not be disclosed to any third party without the prior written consent of the Committee.

If the Contractor intends to customize or use any existing third-party software or systems, they must inform the Committee in advance of any potential data confidentiality risks. Any use of third-party solutions will require explicit approval by the Committee and must fully comply with all applicable data protection and confidentiality requirements.

The Contractor shall ensure that the AI system fully complies with applicable data privacy laws and public procurement regulations.

The AI system shall be designed to avoid “black box” concerns, with a documented model architecture, algorithms, and decision-making logic that are understandable by non-technical staff.

The AI tool is intended to flag suspicious patterns and generate alerts only; it does not determine legal guilt or enforce decisions. The Committee retains full responsibility for interpreting alerts and taking any subsequent action, and any misuse or misinterpretation of flagged data is the responsibility of the Committee.

The Contractor shall ensure that all its employees, subcontractors, and agents involved in performing the Contract are bound by equivalent confidentiality obligations. These obligations shall remain in force both during the term of the Contract and for a period of five (5) years after its termination or completion, unless otherwise agreed in writing by the Parties.

8. Intellectual Property and Copyright

Upon completion of the project and formal acceptance of the final deliverables, the Contractor shall transfer all intellectual property rights, including but not limited to copyright, source code, documentation, reports, training materials, and any other outputs developed under this Contract, to the Committee.

The transfer of all deliverables shall be confirmed by a signed acceptance act.

From the moment the acceptance act is signed, the Committee shall obtain full ownership and unrestricted "unlimited rights" to use, modify, further develop, and distribute the software and related materials. This includes the right to use, disclose, reproduce, prepare derivative works, distribute to the public, perform publicly, and display publicly, in any manner and for any purpose, and to have or permit others to do so. The Committee's ownership and rights will also extend to the underlying algorithms, models, and data schemas developed as part of the solution.

The Contractor shall ensure full compliance with all applicable data privacy laws, cybersecurity standards, and public procurement regulations of the Republic of Uzbekistan. The Contractor shall not retain any rights to the developed solution, except where otherwise explicitly agreed upon in writing by the Parties. and also warrants that the delivered software and all its components do not infringe on any third-party intellectual property rights.

It is expressly agreed that the AI software and its outputs are intended only to flag patterns and anomalies for further manual review. The tool does not constitute evidence or determine guilt. The Committee retains full responsibility for interpreting analytical results and for any subsequent administrative or legal decisions based on the system’s outputs.

This clause ensures that the Committee is not locked into an ongoing relationship with the Contractor for future modifications or use of the system. It aligns with international best practices for government procurement of IT systems, where the government requires full ownership of custom-developed software to ensure long-term usability, maintenance, and the ability to share the technology with other government agencies or third parties for government purposes.