AI & ML Strategy & Implementation

AI That Solves Real Problems

The AI landscape is moving fast. Large language models, autonomous agents, retrieval-augmented generation, computer vision, and predictive analytics are transforming how businesses operate — but only when applied to the right problems with the right architecture. The gap between what AI can do and what it should do for your specific business is where most companies get stuck.

I help businesses bridge that gap. As a fractional CTO with hands-on experience building and deploying AI-powered systems — from conversational AI chatbots to full production ML pipelines — I bring both the strategic perspective to identify where AI creates value and the technical depth to actually build it.

AI Strategy Services

Not every business problem needs AI, and not every AI solution needs a custom model. Strategy starts with understanding your business objectives, data landscape, and team capabilities — then mapping the AI opportunities that deliver the highest return on investment.

AI Readiness Assessment

Before writing a single line of code, I evaluate your organisation’s readiness for AI adoption. This includes data quality and availability, existing infrastructure, team skills, regulatory constraints, and competitive positioning. The output is a clear-eyed assessment of where you are today and what needs to change to unlock AI’s potential — not a slide deck full of buzzwords.

Use Case Identification & Prioritisation

I work with your leadership team to identify AI opportunities across the business — customer experience, operations, product features, internal productivity — and rank them by impact, feasibility, and time to value. This prioritised roadmap becomes the foundation for disciplined AI investment rather than scattered experimentation.

LLM & Foundation Model Selection

The foundation model landscape is rapidly evolving. Claude, GPT-4, Gemini, Llama, Mistral, and domain-specific models each have different strengths in reasoning, code generation, multimodal processing, cost, and latency. I help you select the right models for each use case — balancing capability, cost, data privacy requirements, and vendor dependency. In many cases, the right answer is a multi-model architecture that uses different models for different tasks.

AI Governance & Risk Management

Deploying AI responsibly requires thoughtful governance. I help establish frameworks for model evaluation, output quality monitoring, bias detection, data privacy compliance (GDPR, DPA 2018), and human-in-the-loop safeguards. With my background in enterprise security and ISO 27001 governance, I bring a structured approach to AI risk that satisfies both regulators and customers.

AI Implementation Services

Strategy without execution is just a presentation. I build and deploy AI systems in production — not prototypes that never leave the lab.

Retrieval-Augmented Generation (RAG) Systems

RAG is the architecture that makes LLMs useful for business-specific knowledge. I design and build RAG pipelines that connect language models to your proprietary data — documents, knowledge bases, product catalogues, support tickets — using vector databases (Pinecone, Weaviate, pgvector), embedding models, and intelligent chunking strategies. The result is AI that understands your business, not just the internet.

AI Agent & Workflow Automation

Autonomous AI agents that can reason, use tools, and complete multi-step tasks are transforming knowledge work. I build agent architectures using frameworks like the Claude Agent SDK, LangChain, and custom orchestration layers that automate complex workflows — from customer support triage to document processing to code review. These aren’t simple chatbots; they’re systems that take action, with appropriate guardrails and human oversight.

Conversational AI & Chatbot Development

I’ve built and deployed production conversational AI systems that handle real customer interactions — not demo-day prototypes. This includes the AI-powered chat on this very website, as well as the EmpireVault platform’s AI-assisted support chat, ticket triage, and smart reply systems. I design these systems with retrieval-augmented generation, context management, fallback strategies, and seamless human handoff.

ML Feature Engineering & Model Integration

Not every AI use case is a language model problem. I implement traditional ML solutions where they’re the right tool — lead scoring, churn prediction, anomaly detection, recommendation engines, and predictive maintenance. This includes feature engineering, model selection, training pipelines, A/B testing frameworks, and production serving infrastructure on Kubernetes.

LLM Application Architecture

Building reliable LLM-powered applications requires more than an API call. I architect production systems with prompt engineering and management, structured output parsing, token cost optimisation, caching strategies, fallback chains across model providers, evaluation and regression testing, and observability (logging, tracing, cost tracking). These engineering practices are the difference between a demo and a product your customers can depend on.

Technical Approach

My approach to AI implementation is grounded in production engineering principles — the same rigour I apply to any technology leadership engagement:

• Start with the business problem, not the technology — AI is a tool, not a strategy. Every implementation begins with a clearly defined business outcome and measurable success criteria
• Build incrementally — Ship a working MVP, measure results, iterate. Avoid the trap of spending months on a perfect system before validating the approach
• Design for production from day one — Containerised services on Kubernetes, CI/CD pipelines, monitoring, and alerting. AI systems need the same operational discipline as any other production service
• Keep humans in the loop — Appropriate oversight, review workflows, and escalation paths. AI augments human decision-making; it doesn’t replace accountability
• Optimise for cost and latency — Model selection, caching, batching, and architectural patterns that keep AI-powered features fast and affordable at scale

Proven Track Record

This isn’t theoretical knowledge. I build with these technologies daily:

• EmpireVault SaaS Platform — Built AI-powered ticket triage, smart reply generation, lead scoring, and conversational AI as part of a full SaaS platform delivered in 90 days
• Selbytech.ai Conversational AI — The AI chatbot on this website is a production RAG system that answers visitor questions using site content, qualifying leads and booking consultations
• AI-Powered Development Workflows — I use Claude Code, Claude Agent SDK, and multi-model architectures in my own engineering practice daily, giving me first-hand understanding of what works in production and what’s still hype
• Enterprise AI Governance — Experience from Honeywell’s global IT operations applying governance frameworks, risk assessment, and compliance controls to technology deployments at scale

Technology Stack

I work across the full AI/ML technology landscape, selecting the right tools for each specific use case:

• Foundation Models — Claude (Anthropic), GPT-4 (OpenAI), Gemini (Google), Llama, Mistral, and domain-specific models
• Agent Frameworks — Claude Agent SDK, LangChain, LangGraph, custom orchestration
• Vector Databases — Pinecone, Weaviate, pgvector, Chroma
• ML Platforms — Python (scikit-learn, PyTorch, TensorFlow), Hugging Face Transformers
• Infrastructure — Kubernetes, Docker, CI/CD pipelines, GPU-optimised compute
• Observability — LangSmith, custom logging, cost tracking, evaluation frameworks

Frequently Asked Questions

Do we need AI, or is this just hype?

Maybe, and that honesty is exactly what I bring to the conversation. Not every business problem benefits from AI. During the readiness assessment, I’ll tell you clearly which use cases will deliver real ROI and which ones aren’t worth pursuing. I’d rather save you money than sell you a project.

Should we build or buy AI capabilities?

It depends on how core AI is to your competitive advantage. If AI is a product differentiator, you should own the implementation. If it’s operational tooling, a commercial solution may be faster and cheaper. I help you make this decision for each use case and avoid building what you should buy.

What about data privacy and GDPR?

Data governance is a first-class concern in every engagement. I design AI systems with data residency requirements, access controls, and privacy-by-design principles built in — not retrofitted. For LLM-based systems, this includes careful consideration of what data is sent to external APIs versus processed locally.

How does this fit with your fractional CTO services?

AI strategy and implementation is one dimension of the fractional CTO service. For many clients, AI work happens alongside broader technology leadership — architecture, team building, security, and delivery. For organisations that specifically need AI expertise, standalone AI engagements are available. See pricing for engagement options.

Can you work with our existing engineering team?

Absolutely. I typically work alongside your developers — establishing patterns, reviewing implementations, and mentoring your team on AI/ML best practices. The goal is to build internal capability, not create dependency on external consultants.