Development of Afrovest: A B2B Market Intelligence Platform

Client: Akinia Industry: Fintech & SaaS Duration: 1 week Role: Full-Stack Engineer & SaaS Architect Technologies: Next.js, TypeScript, Tailwind CSS, Shadcn UI, Supabase (PostgreSQL), TanStack Table, Vercel

Afrovest is a B2B SaaS platform that centralizes structured market intelligence on Africa's public and private companies, investors, funding rounds, acquisitions, advisors, and financial ecosystem. Built for professionals working in venture capital, private equity, investment banking, consulting, corporate strategy, and business development, the platform transforms fragmented market information into a searchable, actionable source of truth.

It provides a unified workspace where users can discover opportunities, perform market research, evaluate companies, and export structured datasets for deeper analysis, wihtout worryong about gathering information across countless reports, spreadsheets, news articles, and company websites,

The objective was to build a scalable intelligence platform that makes navigating Africa's rapidly growing investment landscape faster, more reliable, and significantly more data-driven.

Information about companies, investors, funding rounds, advisors, and acquisitions is typically scattered across multiple databases, press releases, news articles, government registries, and independent research reports. This fragmented landscape makes market research, due diligence, and deal sourcing both time-consuming and inconsistent.

Professionals working in finance require far more than simple company directories. They need a platform capable of organizing interconnected datasets while allowing them to quickly discover relationships between companies, investors, transactions, industries, and markets.

I designed, architected, and developed the platform from concept to deployment.

My responsibilities included:

Product research

Information architecture

User interface design

Frontend engineering

Backend development

Database architecture

Authentication implementation

Data modeling

Search implementation

Table architecture

Data export functionality

Performance optimization

Afrovest organizes business intelligence around relationships, not isolated datasets. Companies connect to investors, investors connect to funds, funds connect to transactions, and advisors connect to deals, allowing users to explore business ecosystem for Africa easily rather than navigating disconnected information.

The interface prioritizes speed and clarity, enabling professionals to filter, search, compare, and export information with minimal friction while maintaining the depth expected from enterprise research tools.

Interactive Business Intelligence Dashboard: Afrovest provides a centralized dashboard where users can explore structured datasets covering multiple aspects of Africa's business ecosystem.

The platform brings together information on:

Public companies

Private companies

Investors

Venture capital firms

Private equity funds

Funding rounds

Advisors

Secure User Authentication: Afrovest includes a complete authentication system supporting:

Secure user registration

Email verification

Session management

Password recovery

Protected application routes

Dynamic Data Tables: Using TanStack Table, it delivers highly interactive tables that support:

Sorting

Multi-column filtering

Pagination

Column customization

Responsive layouts

Users can refine datasets using multiple business attributes, including:

Industry sector

Founding year

Company valuation

Investor type

Fund size

Geographic market

Intelligent Search Experience: Finding relevant information quickly is critical when working with large datasets. This patform implements fuzzy search, allowing users to locate information even when search terms are incomplete or only partially match stored records.

Search is available across multiple datasets including:

Companies

Investors

Funds

Advisors

Contacts

Multi-Format Data Export: Users can export filtered datasets into several commonly used formats that integrate seamlessly with spreadsheet software and financial modeling tools such as:

CSV

TSV

JSON

XLSX

African Technology & Investment News: Includes a curated news section highlighting important developments across Africa's technology and investment ecosystem. Users receive ongoing updates covering:

Startup funding

Venture capital activity

Acquisitions

New investments

Industry trends

Market developments

Modern SaaS Architecture: Afrovest was developed using Next.js and TypeScript, creating a scalable full-stack architecture capable of supporting growing datasets while maintaining excellent performance and developer productivity.

Relational Database Design: Supabase PostgreSQL stores structured relationships between:

Companies

Investors

Investment funds

Funding rounds

Advisors

Transactions

Carefully designed relational schemas allow users to traverse connected datasets while maintaining data consistency and query performance.

Secure Backend Infrastructure: Supabase serves as both the relational database and authentication provider.

The backend manages:

User authentication

Email verification

Session handling

Secure database access

Protected resources

High-Performance Data Presentation: TanStack Table provides a highly optimized data grid capable of handling complex filtering, sorting, pagination, and responsive rendering without sacrificing performance. This enables users to work with large volumes of business data while maintaining a smooth user experience.

Designing a database capable of representing these interconnected relationships between Companies, investors, funding rounds, advisors, and acquisitions while supporting fast cross-querying.

Enterprise datasets can quickly overwhelm users if interfaces prioritize raw information over usability.

Designed normalized PostgreSQL schemas with clearly defined relationships and optimized indexing strategies that allow complex queries to execute efficiently across multiple datasets.

Implemented efficient filtering, pagination, sorting, and client-side interactions using TanStack Table that reduce cognitive load ensuring information remained easy to scan regardless of dataset size.

Fast navigation between related business entities while maintaining data integrity and long-term scalability.

Users can explore large collections of business intelligence quickly while maintaining an intuitive experience suitable for daily research workflows.