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Redis Development

We architect Redis into enterprise software as more than a cache — distributed sessions, queues, pub/sub, and rate limiting designed around the actual concurrency and data-loss tolerance a system needs.

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  • In-Memory Speed
  • Distributed Cache
  • Pub/Sub
  • Distributed Locks
  • High Availability
Overview

Our approach

We architect Redis into enterprise software as more than a cache — distributed sessions, queues, pub/sub, and rate limiting designed around the actual concurrency and data-loss tolerance a system needs.

  • Speed That Changes What's Architecturally Possible

    Sub-millisecond reads make patterns like real-time rate limiting and live leaderboards practical in ways a disk-based store can't support.

  • More Than a Cache

    Lists, sorted sets, streams, and pub/sub channels give Redis real architectural roles beyond storing key-value pairs.

  • Atomic Operations for Distributed Systems

    Atomic increments and distributed locks solve coordination problems that are genuinely hard to get right across multiple processes.

  • Built for Ephemeral, Not Just Persistent, Data

    Sessions, rate-limit counters, and real-time state that don't need durable storage get a data store designed for exactly that.

  • Simple Enough to Reason About

    A single-threaded core and predictable command behavior make Redis's failure modes easier to design around than more complex systems.

  • Battle-Tested at Internet Scale

    A data store proven under some of the highest-throughput production workloads in the industry, not a newer option with unknowns.

What's Included

Everything under one roof

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

Caching Layer

Cache-aside, write-through, or write-behind strategies chosen based on the actual read/write pattern and staleness tolerance.

Session Management

Distributed session storage that scales horizontally across application servers without sticky sessions.

Queue Processing

Reliable job queues with retries, backoff, and dead-letter handling for work that shouldn't block a request.

Background Workers

Dedicated worker processes consuming from Redis-backed queues, scaled independently from the application servers.

Real-time Notifications

Pub/sub channels delivering live updates to connected clients without polling.

Distributed Locks

Coordination primitives that prevent race conditions across multiple application instances acting on shared state.

API Rate Limiting

Sliding-window or token-bucket rate limiting implemented with atomic Redis operations that stay correct under concurrent load.

Leaderboard Systems

Sorted sets powering real-time rankings that update and query efficiently even with millions of entries.

Job Scheduling

Delayed and recurring job execution built on Redis-backed queue infrastructure.

Scalable SaaS Platforms

Multi-tenant caching and session infrastructure that scales with tenant count without a redesign.
Why Aixo Lab

Why companies choose Aixo Lab

  1. We Design Cache Invalidation Before We Design Caching

    A cache without a clear invalidation strategy doesn't speed things up, it serves stale data faster — so invalidation is the first architecture decision, not an afterthought.

  2. We Treat Redis as a Data Structure Server, Not Just a Cache

    Sorted sets, lists, and streams solve real problems directly, instead of building that logic in application code around a plain key-value cache.

  3. We Architect for Data Loss, Not Just Speed

    Persistence and replication decisions are made explicitly based on what's acceptable to lose, because Redis's speed comes with real durability tradeoffs.

  4. We Size Redis to Actual Memory Pressure

    Eviction policy and memory sizing are chosen based on real usage patterns, not left at defaults until a production incident forces the question.

  5. We Hand Off an Architecture Your Team Can Own

    Clear documentation of what lives in Redis and why means your own engineers — or ours, later — can extend this system without archaeology.

Our Capabilities

Our Redis Capabilities

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

In-memory Database

Sub-millisecond read and write latency for data that needs to be accessed far faster than a disk-based database can provide.

Distributed Cache

A shared cache layer across multiple application instances, keeping cached data consistent as the system scales horizontally.

Session Storage

Session data stored centrally so any application server can handle any request, without session affinity.

Queues

Reliable job queues with visibility into pending, active, and failed jobs — not a fire-and-forget message with no tracking.

Pub/Sub

Real-time message broadcasting to multiple subscribers, used for live updates and cross-service event notification.

Rate Limiting

Atomic counter operations that enforce limits correctly even under high concurrent request volume.

Background Jobs

Asynchronous processing decoupled from the request cycle, with retry logic for jobs that fail transiently.

Real-time Systems

Live dashboards, notifications, and collaborative features backed by Redis's pub/sub and low-latency reads.

Distributed Locks

Mutual exclusion across multiple processes or servers, preventing race conditions on shared resources.

High Availability

Sentinel or Cluster configurations that provide automatic failover when a Redis node goes down.

Replication

Read replicas and failover configured for the application's actual availability and read-scaling requirements.

Performance Optimisation

Command and data structure choices that avoid the O(N) operations that quietly become a bottleneck at scale.
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.

Redis

The in-memory data store underlying every engagement built this way.

PostgreSQL

The relational database of record, with Redis handling the ephemeral and high-speed data alongside it.

Node.js

A backend runtime commonly paired with Redis for caching, queues, and real-time pub/sub.

Laravel

An enterprise backend framework with built-in Redis support for caching, sessions, and queues.

Next.js

A full-stack framework for applications that need both a frontend and a Redis-backed API layer.

React

The frontend layer for interfaces that consume Redis-backed real-time updates via an API.

React Native

The mobile client layer that benefits from Redis-backed low-latency APIs and real-time features.

Docker

Containerized Redis instances for consistent environments across development, staging, and production.

AWS

Cloud infrastructure — including managed Redis options — for deployments that need more control than a single managed platform.

BullMQ

A Redis-backed job queue library for Node.js, used for reliable background job processing with retries and scheduling.

RabbitMQ

A message broker we compare against Redis's queue capabilities when a workload needs more sophisticated routing guarantees.

OpenAI

AI model access paired with Redis for caching responses and managing rate limits against the provider.
Redis Engineering

Redis Engineering

The engineering decisions that determine whether Redis stays fast, consistent, and recoverable in production, not just in a demo.

Caching Strategies

Cache-aside, write-through, or write-behind chosen based on the actual read/write ratio and how stale data is acceptable to be.

Cache Invalidation

An explicit invalidation strategy designed alongside the cache itself, not assumed to happen automatically.

Distributed Sessions

Session storage architected so any application server can serve any request, without sticky session routing.

Queues

Job queue architecture with retry policy, backoff, and dead-letter handling designed before the first job type is written.

Pub/Sub

Message broadcasting patterns designed for the actual delivery guarantees the feature needs — Redis pub/sub doesn't persist undelivered messages.

Rate Limiting

Atomic rate-limiting operations that stay correct under concurrent requests, not a check-then-increment race condition.

Performance Optimisation

Data structure and command choices that avoid operations whose cost grows with data size in ways that surprise you later.

Persistence Options

RDB snapshots, AOF logging, or no persistence at all, chosen based on what's actually acceptable to lose on a restart.

Replication

Read replicas and Sentinel or Cluster failover configured for the application's real availability requirements.

Monitoring

Memory usage, eviction rate, and command latency tracked so problems are caught before they become an outage.

Scaling

Clustering and sharding strategy sized to the application's actual data volume and throughput, not added preemptively.
Representative Solutions

Where this technology fits

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

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