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.

Enterprise Cloud InfrastructureLimited availability

DevOps & Cloud Infrastructure

I build fault-tolerant systems handling billions of events

Enterprise-grade DevOps and cloud architecture for companies that can't afford downtime. Real scale, real reliability.

Start Infrastructure Project View Infrastructure Cases

10x

Deployment Frequency

75%

Lead Time

90%

Recovery Time

40%

Infrastructure Costs

What I Build

The infrastructure services that keep modern applications running at scale

Container Orchestration

Docker and Kubernetes deployments with auto-scaling, load balancing, and zero-downtime updates.

Key Capabilities:

Docker containerization

Kubernetes clusters

Auto-scaling

Load balancing

Serverless Architecture

Cost-efficient serverless solutions that scale automatically and reduce operational overhead.

Key Capabilities:

AWS Lambda

Azure Functions

Event-driven architecture

Cost optimization

CI/CD Pipelines

Automated build, test, and deployment pipelines with rollback support and quality gates.

Key Capabilities:

GitHub Actions

GitLab CI

Automated testing

Deployment automation

Monitoring & Observability

Comprehensive monitoring, alerting, and observability stacks for proactive issue resolution.

Key Capabilities:

Real-time monitoring

Custom alerts

Performance metrics

Log aggregation

Cloud Platforms I Work With

I've deployed production systems across all the major clouds

AWS

Services:

• EC2

• EKS

• Lambda

• RDS

• S3

• CloudFront

Specialties:

Enterprise scale

Global infrastructure

Comprehensive services

Azure

Services:

• App Service

• AKS

• Functions

• SQL Database

• Blob Storage

Specialties:

Microsoft integration

Hybrid cloud

Enterprise tools

Google Cloud

Services:

• Compute Engine

• GKE

• Cloud Functions

• Cloud SQL

• Cloud Storage

Specialties:

AI/ML services

Data analytics

Developer tools

Multi-Cloud

Services:

• Terraform

• Kubernetes

• Istio

• Prometheus

• Grafana

Specialties:

Vendor independence

Best-of-breed

Risk mitigation

AI Infrastructure

I set up infrastructure specifically for AI and ML workloads... the stuff that actually needs to handle real training and inference

AI Workload Optimization

Specialized infrastructure for machine learning training and inference with GPU acceleration.

Features:

GPU clusters

Model serving

Auto-scaling ML workloads

Cost optimization

Data Pipeline Infrastructure

Robust data processing pipelines for AI training data and real-time inference.

Features:

Stream processing

Batch processing

Data lakes

Vector databases

MLOps Platforms

Complete MLOps infrastructure for model development, training, and deployment lifecycle.

Features:

Model versioning

Experiment tracking

Automated retraining

A/B testing

Security That's Built In

Security isn't something I bolt on at the end. Every infrastructure component gets defense-in-depth baked in from the start.

Infrastructure as Code (IaC) with security scanning

Automated security patching and updates

Network segmentation and firewall rules

Identity and access management (IAM)

Secrets management and encryption

Compliance monitoring and reporting

Vulnerability scanning and remediation

Backup and disaster recovery automation

How I Work

My approach to cloud migration and infrastructure optimization... no surprises, clear phases

Assessment

1 week

Analyze current infrastructure, identify bottlenecks, and design optimal cloud architecture.

Activities:

Infrastructure audit

Performance analysis

Cost assessment

Security review

Outcome:

Comprehensive migration and optimization plan

Foundation

2-3 weeks

Set up core infrastructure, networking, security, and monitoring foundations.

Activities:

Cloud account setup

Network configuration

Security implementation

Monitoring setup

Outcome:

Secure, monitored cloud foundation

Migration

3-6 weeks

Migrate applications and data with minimal downtime using proven migration strategies.

Activities:

Application containerization

Database migration

DNS cutover

Performance testing

Outcome:

Applications running in cloud environment

Optimization

2-4 weeks

Optimize performance, costs, and implement advanced features like auto-scaling.

Activities:

Performance tuning

Cost optimization

Auto-scaling setup

Advanced monitoring

Outcome:

Fully optimized, self-managing infrastructure

Need infrastructure that actually scales?

I build cloud infrastructure that handles growth without breaking, supports AI workloads, and keeps your team from getting paged at 3am.

Limited availability. I take on 2-3 infrastructure projects per quarter.

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