Fayvo Backend, API & Search Work

Backend APIs, search integrations, Elasticsearch optimization, scraping, Redis caching, and AWS deployment support for a social media product used in Arab markets.

Project summary

Worked at Ilsa Interactive on Fayvo backend features across Node.js, Express, Laravel, Elasticsearch, Redis, Docker, and AWS-hosted services.

API, search, caching, scraping, and AWS deployment experience on a live social product.

Project summary

Product context

Social app for Arab markets

Backend mix

Node.js + Laravel services

Worked at Ilsa Interactive on Fayvo backend features across Node.js, Express, Laravel, Elasticsearch, Redis, Docker, and AWS-hosted services.

Buyer-facing summary

Client problem

The product needed backend APIs, search suggestions, external data integrations, caching, and deployment support across a mixed backend stack.

What I delivered

I worked on suggested-search APIs, external API integrations, Elasticsearch optimization and migration, scrapers, Redis caching, Laravel APIs, and AWS/Docker workflows.

Business result

The product had broader API/search coverage and stronger backend support for real user-facing features.

Problem

Fayvo was a social media application focused on Arab markets. My work sat in the backend layer, where search, APIs, external integrations, and deployment support affected the product experience directly.

The stack crossed Node.js, Express, Laravel, Elasticsearch, Redis, Docker, and AWS services, so useful work often meant understanding how different parts of the system interacted.

This was employer team work rather than an independently owned product.

The backend included both Node.js and Laravel services, which meant consistency and deployment discipline mattered.

What I built

Suggested search APIs

Worked on APIs for suggested search results using external sources such as Google Search, YouTube, Maps, IMDb, IGDB, and iTunes-style APIs.

Elasticsearch optimization and migration

Optimized Elasticsearch queries and supported migration work around Elasticsearch 6.7 so search behavior could keep pace with product needs.

Scrapers and data support

Built and supported scrapers for latest songs and games data, helping external content flow into the product more reliably.

Laravel APIs, caching, and deployment support

Worked on Laravel APIs for user updates, posts, and screen queries, plus Redis caching and Docker/AWS deployment support across staging and production environments.

Suggested search result APIsThird-party API integrationsElasticsearch query optimizationElasticsearch 6.7 migration supportSongs and games data scrapersRedis caching and AWS deployment support

Technical decisions

Suggested search pulled from multiple external APIs, so response shaping, validation, and fallback behavior mattered.

Elasticsearch query work and version migration support required care because search behavior is user-facing and easy to regress.

Redis caching, Docker workflows, and AWS EC2/RDS/S3/API Gateway environments shaped practical deployment support.

The recurring backend lesson was that integrations need operational thinking: validation, caching, response shaping, and graceful behavior when a third-party source changes.

Working across Node.js and Laravel made API consistency important. The product should not feel like separate backend services stitched together from the front end.

Outcome

The work improved backend features, search behavior, integration coverage, and deployment support for a real product.

It is strong proof of professional backend/API experience, third-party API integration, search optimization, AWS deployment, caching, and mixed-service codebase exposure.

What I would improve

I would standardize API response conventions and integration failure logging as early as possible.

Those habits are cheap at the start and much more expensive once many endpoints and external APIs are already live.

Tech stack

Node.jsLaravelElasticsearchRedisDockerAWS

Next step

If you need similar work, let’s talk through the constraints first.

The useful part of a project like this usually starts before code: understanding what the CMS should own, what should live in a backend service, and where integrations or automation can stay maintainable.

Start a conversation

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