Natural Language Search for Market Intelligence
A production-grade semantic search system that enables non-technical stakeholders to query complex financial data using natural language queries instead of rigid database filters. Built with OpenAI embeddings, vector similarity, and hybrid search algorithms.
Role
Full-Stack & AI Engineer
Timeline
3 Weeks Execution
Status
Live in Production
Platform
African Intelligence Dash
10,000+
Searchable Entities
Companies, investors, funds, & transaction records
1,536
Vector Dimensions
High-density OpenAI semantic embedding vectors
<200ms
Search Latency
Sub-200ms instant query execution speed
95%+
Query Precision
Accurate plain-English intent matching
Unlocking Complex Databases for Business Users
Finding investors in "agritech sector with investments in the last 5 years" previously required navigating multiple dropdown menus, selecting date ranges, and manually applying industry filters.
The core challenge was communication: while the underlying database required rigid SQL queries, business users naturally think in conversational plain English.
Semantic Vector Matching & Hybrid Search Architecture
I engineered a semantic search layer directly integrated with PostgreSQL using OpenAI vector embeddings. Every entity in the database is converted into a 1,536-dimensional vector representing its semantic meaning.
Product Screenshots & Technical Evidence
Interested in AI-powered search or custom data systems?
Let's discuss how semantic vector search and automated data pipelines can simplify workflows for your team.