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Aixo LabAixo Lab

GraphQL Development

We design and build enterprise GraphQL APIs — schema design, federation, and resolver architecture — for the specific cases where GraphQL's tradeoffs genuinely pay off, not as a default replacement for REST.

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  • Schema-First
  • Federation
  • Data Loaders
  • Type-Safe Schema
  • N+1 Prevention
Overview

Our approach

We design and build enterprise GraphQL APIs — schema design, federation, and resolver architecture — for the specific cases where GraphQL's tradeoffs genuinely pay off, not as a default replacement for REST.

  • Precise Data Fetching

    A client requests exactly the fields it needs in one round trip, which matters most when a UI aggregates data from several sources.

  • One Endpoint, Many Shapes

    A single schema serves web, mobile, and partner clients with different data needs, instead of a REST endpoint per view.

  • Strongly Typed Contracts

    The schema is the contract — client and server agree on shape and types before a single query is written.

  • Real-Time by Design

    Subscriptions are a first-class part of the spec, not a separate protocol bolted alongside the API.

  • Built for Composite Frontends

    A backend-for-frontend layer that aggregates multiple services behind one schema, without each client learning every service's API.

  • Not a Default, a Deliberate Choice

    We recommend GraphQL when the data-fetching problem actually calls for it — not because it's available.

What's Included

Everything under one roof

Everything included in this engagement, from architecture to long-term support — one team, one system.

Enterprise APIs

Internal and partner-facing APIs where a single schema needs to serve many different consumers with different needs.

Mobile APIs

APIs designed around mobile bandwidth and battery constraints, where precise field selection genuinely reduces payload size.

Frontend Optimization

Query shapes designed to match what a screen actually renders, cutting the over-fetching common in generic REST responses.

Real-time Applications

Live dashboards and collaborative features built on GraphQL subscriptions where persistent updates are core to the product.

Microservices

A GraphQL gateway that aggregates multiple backend services behind one coherent schema for client consumption.

API Gateway

A unified entry point in front of existing REST or gRPC services, without forcing a full backend rewrite.

Headless CMS

Content delivery through a GraphQL layer that lets frontends query exactly the fields a given page needs.

Marketplace APIs

Complex, relational marketplace data — listings, sellers, reviews — queried in the shapes each client screen needs.

AI Platforms

AI features exposed through a typed schema, with streaming handled via subscriptions where that fits the product.

BFF (Backend for Frontend)

A dedicated GraphQL layer per frontend that aggregates and reshapes data from underlying services.
Why Aixo Lab

Why companies choose Aixo Lab

  1. We Recommend GraphQL When the Data Shape Justifies It

    If a REST API with well-designed endpoints solves the problem, we say so — GraphQL earns its complexity, it isn't assumed by default.

  2. We Design Schemas as Long-Term Contracts

    Schema design gets the same architectural attention as database design, because a schema is far harder to change once clients depend on it.

  3. We Solve the N+1 Problem Before It Reaches Production

    Data loaders and query batching are part of the initial resolver design, not a performance fix discovered after the first slow dashboard.

  4. We Build Federation for Real Multi-Team Boundaries

    Federated schemas are used when multiple teams genuinely own separate services, not added as unnecessary architecture for a single backend.

  5. We Hand Off Code Your Team Can Own

    Clear architecture and documentation mean your own engineers — or ours, later — can extend this schema without archaeology.

Our Capabilities

Our GraphQL Capabilities

The specific technical capabilities behind every GraphQL engagement — not a generic feature list, the actual engineering surface we work in daily.

GraphQL APIs

A single, typed endpoint serving precisely the data each client requests, designed around real query patterns.

Schema Design

Types and relationships modeled around the domain, built to evolve without breaking clients already depending on them.

Queries

Read operations shaped around how clients actually consume data, not a one-to-one mirror of database tables.

Mutations

Write operations with clear input validation and consistent response shapes for the data that changed.

Subscriptions

Real-time updates delivered over a persistent connection, used where the product genuinely needs live data.

Federation

Multiple subgraphs composed into one schema, so separate teams can own their services independently.

Schema Stitching

Combining schemas from multiple sources into a unified graph, for cases federation's tooling doesn't fit.

Authentication

Identity verification integrated at the gateway or resolver level, consistent with the rest of the system.

Authorization

Field- and type-level access control, so permission checks don't rely on the client behaving honestly.

Caching

Response and field-level caching applied deliberately, since GraphQL's flexible queries make naive HTTP caching harder than REST.

Performance

Query complexity limits and resolver-level profiling to keep expensive queries from degrading the whole API.

API Evolution

Additive schema changes as the default path, with deprecation strategies for the times a field genuinely needs to go.

Monitoring

Visibility into query patterns, resolver latency, and error rates — the difference between debugging and guessing.

Error Handling

Structured, partial-failure-aware error responses, since a single GraphQL request can partially succeed.
Process

How we work

The same disciplined process behind every engagement, from the first architecture decision to launch.

  1. 01
    Discovery

    Understand the business problem and its real constraints.

    Output:
    Scope and goals document
    Your involvement:
    Initial workshop
  2. 02
    Product definition

    Translate the problem into concrete product requirements.

    Output:
    Feature spec and priorities
    Your involvement:
    Requirements review
  3. 03
    UX/UI design

    Design user flows and interface before development starts.

    Output:
    Wireframes and design system
    Your involvement:
    Design feedback
  4. 04
    Technical architecture

    Define system structure, data flow, and technology stack.

    Output:
    Architecture document
    Your involvement:
    Technical review (optional)
  5. 05
    Iterative development

    Build in short cycles with visible, regular progress.

    Output:
    Regularly shipped working versions
    Your involvement:
    Sprint review participation
  6. 06
    Quality assurance

    Test functionality, performance, and security before release.

    Output:
    Test results and fixes
    Your involvement:
    Acceptance sign-off
  7. 07
    Launch

    Deploy to production with a rollback plan in place.

    Output:
    Product deployed to production
    Your involvement:
    Launch approval
  8. 08
    Continuous improvement

    Monitor, maintain, and evolve the product after launch.

    Output:
    Maintenance and improvement roadmap
    Your involvement:
    Regular check-in meetings
Technology Stack

Built on a modern, production-grade stack

Every technology here is a deliberate choice, not a default.

GraphQL

The query language and schema specification underlying every API we build this way.

Apollo Server

A production-grade GraphQL server for building and serving the schema.

Apollo Client

Client-side query management, caching, and state handling for GraphQL-powered frontends.

Next.js

A full-stack framework for GraphQL-powered applications that need both frontend and API layers.

React

The frontend layer most commonly paired with Apollo Client for GraphQL-powered interfaces.

React Native

The mobile client layer that benefits most from GraphQL's precise, bandwidth-conscious data fetching.

Node.js

A backend runtime well suited to resolver logic and subscription handling.

Laravel

An enterprise backend framework used as a GraphQL server layer for existing PHP-based systems.

PostgreSQL

The relational database most GraphQL resolvers ultimately query against.

Redis

Caching and pub/sub infrastructure for GraphQL subscriptions and query result caching.

Docker

Containerized builds for consistent environments across development, staging, and production.

AWS

Cloud infrastructure for GraphQL APIs that need more control than a managed platform alone provides.
GraphQL Engineering

GraphQL Engineering

The engineering decisions that determine whether a GraphQL API stays fast, secure, and maintainable as it grows, not just at launch.

Schema Design

Types and relationships modeled around the domain during planning, so the schema doesn't need a breaking rewrite as the product grows.

Resolvers

Resolver logic kept thin and testable, with business logic living in services the resolvers call rather than in the resolvers themselves.

Data Loaders

Batching and caching within a single request so related data is fetched efficiently instead of one query per field.

N+1 Problem

The most common GraphQL performance failure, solved deliberately with data loaders rather than discovered in a slow production query.

Caching

Field- and query-level caching strategies designed for GraphQL's flexible shape, where a single cache key rarely fits.

Authentication

Identity verification enforced consistently whether a request comes through the gateway or a federated subgraph.

Authorization

Field-level permission checks that prevent a client from requesting data it shouldn't see, not just endpoint-level gating.

Pagination

Cursor-based pagination patterns that stay consistent and performant as connections between types grow.

Performance

Query complexity analysis and depth limiting so a single client request can't accidentally overload the API.

Monitoring

Per-field and per-resolver observability, since a slow query in GraphQL can hide inside an otherwise fast request.

Federation

Subgraph composition designed around real service ownership boundaries, not split for its own sake.
Representative Solutions

Where this technology fits

Reference architectures from our Representative Solutions collection that could plausibly be built on this stack.

Enterprise CRM
Representative Solution

Enterprise CRM

Enterprise Software
Human Resources Management Platform
Representative Solution

Human Resources Management Platform

HR Technology
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