Back to work
Employer product teamAPI platform development

Product-team API work

Fayvo Social Platform

API and search engineering for a social product serving around 10,000 users.

Project summary

Worked inside a product team on the API layer for core social features, search, and cross-service consistency across Laravel- and Node-based systems.

Consistent API behavior across Laravel and Node.js on a live social product.

Abstract social graph illustration for Fayvo

Project summary

User base

~10K users

Backend stack

Laravel + Node.js

Worked inside a product team on the API layer for core social features, search, and cross-service consistency across Laravel- and Node-based systems.

Buyer-facing summary

Client problem

The product needed stable backend APIs and feature delivery for a real user-facing application.

What I delivered

I contributed to backend services, API implementation, data flows, and production feature work.

Business result

The product had stronger backend support for user-facing features and ongoing development.

Problem

Fayvo was a social product with user profiles, posts, chat, and guest access for pre-signup browsing. My role sat on the API side of the stack, where each client-facing feature depended on backend behavior staying reliable.

The backend had grown across Laravel and Node.js modules. A large part of the job was making the system feel like one platform from the outside even though the internals were split.

This was an employer team project rather than an independently owned product.
Different services had grown with different conventions, which made consistency an ongoing engineering task.

What I built

APIs for core social features

Built and maintained REST endpoints for posts, chat, and user flows, keeping contracts predictable for the front-end team even when implementations crossed service boundaries.

Guest-user access flows

Implemented guest browsing behavior carefully enough to share parts of the product safely without exposing the wrong data or assumptions between authenticated and unauthenticated states.

Elasticsearch search integration

Moved search-heavy behavior away from increasingly slow MySQL patterns so posts and users could stay discoverable as data volume grew.

Query and service debugging

Tracked down performance and behavior issues across multiple backend modules, which often meant isolating responsibility before writing the actual fix.

REST APIs for posts, chat, and guest-user flowsSearch integration with ElasticsearchMySQL query optimizationCross-service debugging across Laravel and Node.js modules

Technical decisions

API consistency mattered because the front end should not need to care which backend service handled a request.
Search improvements required separating lookup concerns from generic MySQL queries while still keeping data in sync.

Consistency was the recurring theme: response shapes, pagination behavior, and service boundaries needed enough discipline that the API felt coherent.

A lot of useful backend work on this project was not greenfield feature coding. It was profiling, debugging, and standardizing behavior across services that had evolved separately.

Outcome

The platform’s core API-driven features remained reliable for a live user base of around 10,000 people.
Search improved materially once Elasticsearch took on the search workload instead of asking MySQL to do everything.
The front-end team was not constantly paying the tax for backend inconsistency.

What I would improve

I would standardize response conventions and pagination rules earlier so inconsistencies do not accumulate quietly across services.

That kind of discipline is cheap at the beginning of a product and surprisingly expensive once dozens of endpoints already exist.

Tech stack

LaravelNode.jsMySQLElasticsearch

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