What is AI integration in software architecture?

AI integration in software architecture involves designing systems that can seamlessly incorporate artificial intelligence components like RAG pipelines, machine learning models, and intelligent automation. This requires modular architecture, clean data flows, and scalable infrastructure.

How long does a software architecture project take?

Software architecture projects typically take 2-12 weeks depending on complexity. Simple systems can be architected in 2-4 weeks, while enterprise-scale AI-integrated systems may require 8-12 weeks for complete design and implementation planning.

Do you offer free consultations?

Yes, we offer free initial consultations to assess your project requirements and determine if our expertise aligns with your needs. Contact us to schedule your consultation with our senior architect André.

What makes Polyxmedia different from other consultants?

Unlike theoretical consultants, we have real production experience with billion-event systems. André has 20+ years of enterprise-scale development experience and has built systems handling billions of transactions. We focus on practical solutions that work.

Can we meet in person?

Yes, we can organize meetings as required in a mutual location in London or Guildford. If you need me to travel I need notice but get in touch and we can accommodate. We tend to find most things can be done over Google Meet saving everyone time and money which is what we're all after. We believe in being lean and whilst no issue with meeting, we encourage remote first approach because it's the most cost effective for serious operators.

How much does a Fractional CTO cost?

Polyxmedia offers Fractional CTO services at £185/hour or monthly packages: Strategic Advisory at £1,850/month (~10 hours), Growth Partner at £7,400/month (~40 hours), or Platform Leadership at £14,800/month (~80 hours). No long-term contracts required.

What is RAG and how does it work?

RAG (Retrieval-Augmented Generation) combines a retrieval system with a language model. Your documents are converted to embeddings stored in a vector database. When users ask questions, relevant documents are retrieved and provided as context to the LLM, enabling accurate answers based on your specific data.

Why do 80% of AI projects fail?

Most AI projects fail due to: unclear business objectives, poor data quality, treating AI as magic rather than engineering, lack of production expertise, and unrealistic expectations. Success requires clear use cases, quality data, and engineers who have shipped AI to production before.

Custom SoftwareLimited availability

Custom Software Engineering

I architect and build enterprise-grade systems that actually scale. 20+ years building white-label platforms for major brands, and you get direct access to that experience.

Talk About Your Project See the Work

How I Actually Build Software

20+ years of lessons learned, mostly from things that broke and taught me what not to do.

Code That Lasts

I write code that's easy to maintain, not code that's clever. Clean architecture means you're not rebuilding everything in 18 months because someone wanted to show off.

•Domain-driven design patterns
•SOLID principles in practice
•Database design that scales
•Testing strategies that catch bugs

Fast Where It Counts

I optimize for what users actually notice. There's no point having 10ms response times if the UX is terrible, so I focus on perceived performance.

•Caching strategies that work
•Database query optimization
•Frontend performance patterns
•Load testing under real conditions

Secure by Default

Security baked in from the start, not bolted on at the end. When security audits happen, they find what I expected them to find... nothing interesting.

•Authentication & authorization patterns
•Input validation & sanitization
•HTTPS & encrypted data storage
•Regular security audit practices

Built to Evolve

APIs and data flows designed so you can add AI later, scale up, or integrate new services without ripping everything apart. Because requirements always change.

•REST & GraphQL API design
•Microservices architecture
•Event-driven communication
•AI-ready data structures

Tools I Use

I pick what's proven, not what's trendy. These are technologies I've used in production for years.

backend

Laravel
Node.js
Go
Python
PostgreSQL
Redis
GraphQL

frontend

Vue.js
React
TypeScript
Tailwind CSS
PWA

infrastructure

Docker
Kubernetes
AWS
Azure
Serverless
CDN

monitoring

Elasticsearch
Prometheus
Grafana
Sentry
DataDog

How I Work

These are practices I actually follow, not just things that look good on a website. They're habits at this point.

•Test-driven development (TDD) practices
•Continuous integration and deployment
•Code reviews and pair programming
•Performance monitoring and optimization
•Security audits and penetration testing
•Comprehensive documentation
•Scalable database design
•API-first development approach

What I Build

SaaS Platforms

Multi-tenant applications with subscription management, analytics, and scalable architecture.

Customer portalsBusiness dashboardsAPI platforms

E-commerce Solutions

Full-featured online stores with payment processing, inventory management, and CRM integration.

Online marketplacesB2B platformsMobile commerce

Enterprise Applications

Custom business software that integrates with existing systems and supports complex workflows.

CRM systemsERP solutionsWorkflow automation

Data Platforms

Systems for collecting, processing, and analyzing large volumes of business data.

Analytics dashboardsReporting systemsData pipelines

The Process

Realistic timelines, honest communication, no surprises at the end.

Discovery

1 week

•Understanding what you actually need
•Technical assessment of current systems
•Architecture planning
•Identifying what could go wrong

You Get:

Clear roadmap with realistic timelines and potential issues surfaced early

I'll tell you if your timeline is unrealistic. Better to know now than later.

Build

2-16 weeks

•Weekly progress demos
•Continuous testing throughout
•Performance optimization as I go
•Your feedback integrated immediately

You Get:

Production-ready system that works under real conditions

You see working software every week. No big reveal at the end.

Launch

1 week

•Deployment automation
•Monitoring setup
•Performance validation
•User onboarding support

You Get:

Live system with monitoring that catches issues before users notice

Soft launches work. I make sure it's solid before everyone uses it.

Evolve

As needed

•Performance monitoring
•Feature additions
•Security updates
•Scaling when you need it

You Get:

System that grows with your business instead of limiting it

Built to evolve. Not to be replaced in 2 years.

Work I've Done

Real software running in production, with actual metrics from real usage.

Trap Music Museum

E-commerce platform connecting their physical gift shop with online sales. Real-time inventory sync so they stop overselling, and it handles the traffic spikes when celebrities visit.

Results:

Zero downtime during traffic spikes, inventory sync issues completely eliminated

Technology Stack:

Vue.jsNode.jsMongoDBStripe APIReal-time Sync

Engineering Solutions: