AI-Powered Research Assistance Platform
Budget / SalaryHourly project
TypeFreelance project
LocationRemote
Posted1 hour ago
Project Overview
We are looking for an experienced full-stack/AI developer to build a web-based research assistance platform focused on helping users conduct structured academic and professional research.
The platform should be capable of taking a user’s research topic, understanding the requirements of the project, identifying relevant academic sources and datasets, performing appropriate analytical workflows, generating visualizations, and producing a professionally structured final document.
The system should be designed with a modular architecture so that additional research disciplines, analytical methods, data sources, and document formats can be added later.
Core Requirements
1. Research Topic Analysis
The user should be able to enter a research topic or research question.
The system should analyze the topic and identify:
* Relevant research areas
* Required datasets
* Potential variables
* Appropriate research methodologies
* Relevant statistical or analytical techniques
* Existing literature related to the subject
2. Academic Literature Integration
The platform should be capable of retrieving relevant academic literature through appropriate APIs and legal/open-access sources.
The system should extract structured information from relevant publications, such as:
* Research methodology
* Variables
* Dataset characteristics
* Statistical methods
* Research objectives
* Main findings
* Limitations
* Referencing information
The system should maintain source traceability so that generated content can be connected back to its underlying sources.
3. Dataset Management
Users should be able to upload datasets in common formats such as:
* CSV
* Excel
* JSON
* Other structured formats
The system should inspect uploaded data and identify:
* Variables
* Data types
* Missing observations
* Potential inconsistencies
* Descriptive statistics
* Possible transformations
* Whether the dataset is suitable for the proposed research methodology
Where appropriate, the platform should also be able to identify publicly available datasets through integrated data sources/APIs.
4. Statistical & Analytical Engine
The platform should support automated analytical workflows based on the research subject.
Depending on the project, this may include:
* Descriptive statistics
* Correlation analysis
* Regression analysis
* Panel-data analysis
* Hypothesis testing
* Time-series analysis
* Financial analysis
* Econometric methods
* Other discipline-specific calculations
Python/R should preferably be used as the underlying analytical environment.
The architecture should allow additional statistical models to be added later.
5. Visualization
The system should automatically generate appropriate research-quality visualizations from the analysis.
Examples include:
* Regression plots
* Correlation matrices
* Distribution charts
* Time-series charts
* Tables
* Comparative figures
* Statistical diagrams
Figures should be exportable and suitable for inclusion in a professional research document.
6. Document Generation
The platform should generate a structured research document based on a predefined academic framework.
The system should support sections such as:
* Abstract
* Introduction
* Literature Review
* Theoretical Framework
* Methodology
* Data
* Analysis
* Results
* Discussion
* Conclusion
* References
* Appendices
The structure should be configurable rather than hard-coded.
The generated document should be capable of reaching at least 30 pages, depending on the amount of source material and research content available.
7. Referencing
Users should be able to select different citation styles, including:
* APA
* Harvard
* Other configurable citation formats
References should be automatically generated and consistently formatted.
The system must avoid fabricated references and should maintain a connection between citations and the underlying source metadata.
8. AI Writing Layer
The generated content should be coherent, academically appropriate, and customized to the user’s research project.
The system should avoid simply producing generic template text. It should use the retrieved literature, uploaded datasets, analytical results, and generated figures as inputs when constructing the document.
9. User Interface
The platform should have a modern and simple interface.
A potential workflow could be:
Create Project → Define Topic → Literature Analysis → Data → Methodology → Analysis → Results → Document Generation → Export
Users should be able to return to previous stages and modify their project without starting over.
10. Export
The completed research project should be exportable into professional formats such as:
* DOCX
* PDF
* Possibly LaTeX
Technical Requirements
Preferred experience with:
* Python
* R
* FastAPI or similar backend framework
* React/Next.js or equivalent frontend
* PostgreSQL/Supabase
* LLM APIs
* Academic/public data APIs
* Data-processing libraries
* Statistical libraries
* Document-generation systems
Experience working with academic, financial, statistical, or data-analysis applications is a strong advantage.
Important Development Requirement
The system should be built as a research automation and analysis platform, not simply as a text-generation application.
The analytical engine, source-management system, dataset processing, citation management, visualization system, and document-generation system should be independent modules that communicate through a well-designed backend.
The developer will receive the detailed methodology, workflow, UI requirements, and proprietary product specifications after the initial screening stage.
Deliverables
The selected developer/team will be responsible for:
1. Full frontend
2. Backend/API
3. AI integration
4. Literature/source integration
5. Dataset processing
6. Statistical analysis engine
7. Visualization engine
8. Citation/reference system
9. Automated document generation
10. User authentication
11. Project management system
12. Export functionality
13. Admin panel
14. Database architecture
15. Deployment
16. Technical documentation
17. Testing and bug fixing
Ideal Developer
We are looking for someone who can understand both software engineering and quantitative research.
A developer who has experience with Python/R, statistics, econometrics, AI APIs, academic workflows, and document automation would be particularly suitable.
Please include examples of previous projects involving AI, data analysis, statistical software, research tools, or document-generation systems when applying.
We are looking for an experienced full-stack/AI developer to build a web-based research assistance platform focused on helping users conduct structured academic and professional research.
The platform should be capable of taking a user’s research topic, understanding the requirements of the project, identifying relevant academic sources and datasets, performing appropriate analytical workflows, generating visualizations, and producing a professionally structured final document.
The system should be designed with a modular architecture so that additional research disciplines, analytical methods, data sources, and document formats can be added later.
Core Requirements
1. Research Topic Analysis
The user should be able to enter a research topic or research question.
The system should analyze the topic and identify:
* Relevant research areas
* Required datasets
* Potential variables
* Appropriate research methodologies
* Relevant statistical or analytical techniques
* Existing literature related to the subject
2. Academic Literature Integration
The platform should be capable of retrieving relevant academic literature through appropriate APIs and legal/open-access sources.
The system should extract structured information from relevant publications, such as:
* Research methodology
* Variables
* Dataset characteristics
* Statistical methods
* Research objectives
* Main findings
* Limitations
* Referencing information
The system should maintain source traceability so that generated content can be connected back to its underlying sources.
3. Dataset Management
Users should be able to upload datasets in common formats such as:
* CSV
* Excel
* JSON
* Other structured formats
The system should inspect uploaded data and identify:
* Variables
* Data types
* Missing observations
* Potential inconsistencies
* Descriptive statistics
* Possible transformations
* Whether the dataset is suitable for the proposed research methodology
Where appropriate, the platform should also be able to identify publicly available datasets through integrated data sources/APIs.
4. Statistical & Analytical Engine
The platform should support automated analytical workflows based on the research subject.
Depending on the project, this may include:
* Descriptive statistics
* Correlation analysis
* Regression analysis
* Panel-data analysis
* Hypothesis testing
* Time-series analysis
* Financial analysis
* Econometric methods
* Other discipline-specific calculations
Python/R should preferably be used as the underlying analytical environment.
The architecture should allow additional statistical models to be added later.
5. Visualization
The system should automatically generate appropriate research-quality visualizations from the analysis.
Examples include:
* Regression plots
* Correlation matrices
* Distribution charts
* Time-series charts
* Tables
* Comparative figures
* Statistical diagrams
Figures should be exportable and suitable for inclusion in a professional research document.
6. Document Generation
The platform should generate a structured research document based on a predefined academic framework.
The system should support sections such as:
* Abstract
* Introduction
* Literature Review
* Theoretical Framework
* Methodology
* Data
* Analysis
* Results
* Discussion
* Conclusion
* References
* Appendices
The structure should be configurable rather than hard-coded.
The generated document should be capable of reaching at least 30 pages, depending on the amount of source material and research content available.
7. Referencing
Users should be able to select different citation styles, including:
* APA
* Harvard
* Other configurable citation formats
References should be automatically generated and consistently formatted.
The system must avoid fabricated references and should maintain a connection between citations and the underlying source metadata.
8. AI Writing Layer
The generated content should be coherent, academically appropriate, and customized to the user’s research project.
The system should avoid simply producing generic template text. It should use the retrieved literature, uploaded datasets, analytical results, and generated figures as inputs when constructing the document.
9. User Interface
The platform should have a modern and simple interface.
A potential workflow could be:
Create Project → Define Topic → Literature Analysis → Data → Methodology → Analysis → Results → Document Generation → Export
Users should be able to return to previous stages and modify their project without starting over.
10. Export
The completed research project should be exportable into professional formats such as:
* DOCX
* Possibly LaTeX
Technical Requirements
Preferred experience with:
* Python
* R
* FastAPI or similar backend framework
* React/Next.js or equivalent frontend
* PostgreSQL/Supabase
* LLM APIs
* Academic/public data APIs
* Data-processing libraries
* Statistical libraries
* Document-generation systems
Experience working with academic, financial, statistical, or data-analysis applications is a strong advantage.
Important Development Requirement
The system should be built as a research automation and analysis platform, not simply as a text-generation application.
The analytical engine, source-management system, dataset processing, citation management, visualization system, and document-generation system should be independent modules that communicate through a well-designed backend.
The developer will receive the detailed methodology, workflow, UI requirements, and proprietary product specifications after the initial screening stage.
Deliverables
The selected developer/team will be responsible for:
1. Full frontend
2. Backend/API
3. AI integration
4. Literature/source integration
5. Dataset processing
6. Statistical analysis engine
7. Visualization engine
8. Citation/reference system
9. Automated document generation
10. User authentication
11. Project management system
12. Export functionality
13. Admin panel
14. Database architecture
15. Deployment
16. Technical documentation
17. Testing and bug fixing
Ideal Developer
We are looking for someone who can understand both software engineering and quantitative research.
A developer who has experience with Python/R, statistics, econometrics, AI APIs, academic workflows, and document automation would be particularly suitable.
Please include examples of previous projects involving AI, data analysis, statistical software, research tools, or document-generation systems when applying.
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