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.

How long does AI integration take?

8-12 weeks for a production RAG pipeline with existing data. Simple AI features can be added in 2-4 weeks. Enterprise implementations with custom training take 12-16 weeks.

What is RAG and do I need it?

RAG (Retrieval-Augmented Generation) lets AI use your specific business data without retraining models. You need it if you want AI that knows your products, processes, or domain-specific information.

How much does AI implementation cost?

RAG pipeline: £15k-25k. Custom AI features: £8k-15k. Enterprise AI systems: £30k-60k. Pricing depends on data complexity, integration requirements, and scale.

Can you integrate AI into my existing system?

Yes. 20+ years building production systems means integrating AI into legacy systems, modern stacks, and everything between. API-based integration, embedded solutions, or complete rebuilds.

AI Strategy & ImplementationLimited availability

AI That Scales To Billions

I solve complex performance challenges and lead technical initiatives at enterprise scale. 20+ years building AI systems for major brands, and you get direct access to that experience.

Talk AI Strategy See the Work

Honest Talk About AI

Everyone's selling AI magic. Here's what actually works in production.

Myth

AI will revolutionize everything

Reality

AI helps with specific problems. I'll help you find the ones that actually matter to your business.

Myth

You need the latest models

Reality

Often a fine-tuned smaller model outperforms GPT-4 for your specific use case. I've seen it happen.

Myth

AI implementation takes months

Reality

Smart AI starts with proof of concepts. I can show value in weeks, not quarters.

Myth

AI is too expensive for most businesses

Reality

Done right, AI pays for itself quickly. I focus on high-ROI applications first.

What I Actually Build

AI systems that work in production, not just demos that impress stakeholders.

RAG Systems That Actually Work

Your documents talking to GPT-4, but smarter. I build systems that know when they don't know something, which is harder than it sounds.

Custom embeddings

Semantic search

Context optimization

Hallucination detection

Model Fine-Tuning & Training

Making smaller, faster models that outperform the giants on your specific data. It's often the better choice.

Domain adaptation

Parameter efficient tuning

Custom training loops

Performance benchmarking

Intelligent Agents & Workflows

AI that can actually do things, not just generate text. Plan, execute, verify, repeat. With proper error handling.

Multi-step reasoning

Tool integration

Error handling

Human oversight

Production AI Infrastructure

Systems that scale and monitor themselves. Built for reliability, not just demos that look good in meetings.

Auto-scaling

Performance monitoring

Cost optimization

A/B testing

How I Work

Honest timelines, real deliverables, measurable progress at each step.

First

Reality Check

I figure out where AI actually helps vs where it's just hype for your specific situation.

1 week

You get:

Use case validation

ROI projections

Technical feasibility

Then

Proof of Concept

Build a working prototype to prove value before you make a big investment.

2-3 weeks

You get:

Working prototype

Performance metrics

Cost analysis

Build

Production System

Scale the proven concept with proper monitoring and safeguards for real usage.

4-6 weeks

You get:

Production system

Monitoring setup

Documentation

Evolve

Optimize & Iterate

Continuous improvement based on real usage data. AI systems get better with feedback.

Ongoing

You get:

Performance reports

Optimization plan

Training materials

Real Results, Not Demos

AI implementations that actually moved business metrics.

Major UK Telecom - AI Event Processing

I led AI implementation for an enterprise event processing system handling 7 billion events annually. Intelligent routing and anomaly detection that actually works at scale.

Processing Speed +30%

Impact

False Positives -90%

Performance

7B events/year

Scale

AI Tech:

Real-time ML inferenceAnomaly detectionEvent routing AIPerformance optimization

Business Results:

30% faster processing

90% reduction in false positives

99.99% uptime maintained

Fortune 500 Client - Process Intelligence

I architected AI-driven workflow automation for enterprise operations serving millions of customers. The kind of automation that actually saves money.

Processing Time -60%

Impact

Decisions Automated 80%

Performance

£2M/year

Scale

AI Tech:

Process mining AIDecision automationWorkflow intelligenceEnterprise ERP integration

Business Results:

60% reduction in processing time

Automated 80% of routine decisions

£2M annual cost savings

WideZike Social Intelligence

I built a real-time recommendation engine with behavioral analysis and content optimization. Users stayed longer because the content actually matched what they wanted.

Engagement +85%

Impact

Session Time +60%

Performance

92%

Scale

AI Tech:

Recommendation MLBehavioral analysisContent scoringReal-time inference

Business Results:

85% engagement boost

60% longer sessions

Smart content curation

Need AI that actually works?

No buzzwords, no overselling... just practical AI that delivers measurable business value.

Limited availability. AI strategy from someone who builds production systems, not presentations.

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