# Mdares.ai — A National Education Directory That Searches 100+ Filters Instantly

> Saudi Arabia's national education directory. Finding the right school or course across hundreds of parameters — location, specialty, price range, certification type, enrollment status — needs to feel instant. We built the search engine that makes it feel that way, even during enrollment season when thousands of families are searching simultaneously.

## Project context

- Client: Mdares.ai
- Industry: Education Technology
- Year: 2024

## Challenge

A pure relational approach with 100+ filter parameters across multiple entity types produced queries too complex to be fast at scale. Enrollment season — when search volume peaks — coincided with the worst performance, exactly backwards from what families needed. Popular search combinations needed to be fast, not slow because of traffic.

## Solution

SQL/NoSQL hybrid: relational for structured institutional core data, document store for flexible attribute filtering. Custom scoring algorithm weighting filter match, proximity, and enrollment availability. Redis caching on popular search combinations (5-min TTL) — high-traffic searches are served from cache, not computed fresh each time. Search feels instant even at enrollment season peak.

## Services

- Web Development

## Published results

- 100+ Filter parameters supported (simultaneously, fast)
- instant Search response (cached) (Redis hit rate high)
- stable Enrollment season performance (no degradation)
- 4+ Entity types on one platform (schools, uni, courses, centers)

## Benefits

- SQL/NoSQL hybrid — right tool for each data shape
- Custom relevance scoring weighted by proximity + availability
- Redis cache turns popular searches into instant results
- Scales to national directory size without slowdown

## Source and contact

- Canonical project page: https://qualityx.co/en/portfolio/mdares-ai-directory
- Discuss a related project on WhatsApp: https://wa.me/905392376734