Expertise across the whole stack, from the ontology to the infrastructure
Strategy, applied AI, semantic architecture, data platforms, cloud and product engineering, with the reference architecture and industries behind them.
Technical leadership without the gap between slides and code.
Most organisations have strategists who can't build and builders who aren't in the room when strategy is set. I work across both, so the decisions that get made are ones the system can actually honour.
Technology strategy & fractional CTO
Own the technical agenda at board and executive level, and make it real in the engineering org.
- Roadmaps tied to business outcomes
- Build vs. buy vs. partner decisions
- Hiring, team topology, engineering culture
- Technical due diligence
Applied AI & agentic systems
Move from demos to dependable AI products with evaluation, guardrails and cost control built in.
- RAG and GraphRAG pipelines
- LangChain / LangGraph multi-agent workflows
- MCP tool integration & agent ops
- LLM evaluation, observability, governance
Knowledge & semantic architecture
Give your data a shared meaning so humans, analytics and AI agents all reason over the same model.
- Ontology & taxonomy design
- Knowledge graphs, entity resolution
- Standard vocabularies & common data models
- Semantic layers for analytics and LLMs
Data platforms at scale
Design the pipelines and stores that carry the business. Real-time and batch, reliable under load.
- Event-driven & streaming architectures
- MongoDB, Postgres, BigQuery, warehouses
- Data modelling & aggregation design
- Quality, lineage and governance
Cloud & platform engineering
Infrastructure that is reproducible, secure and pleasantly boring, on any cloud.
- Kubernetes & internal developer platforms
- Terraform IaC across AWS, Azure, GCP
- CI/CD, GitOps, observability, SRE
- Cost and reliability engineering
Product engineering
Hands-on delivery of the product surface: APIs, web and mobile, built to the architecture's standard.
- Python, Go, TypeScript, Java, C#
- React, Angular, React Native
- API design & system design
- Developer tooling & CLIs
An intelligent platform, built in layers.
The blueprint I adapt for AI-ready organisations. Raw data flows up through a semantic core, the ontology, so that every product, report and AI agent works from the same meaning. Platform and governance run the full height of the stack.
Applications change every year; what a "patient", "order" or "account" means should not. Put meaning at the core and you can swap models, vendors and frameworks without rewriting the business.
Agents grounded in a knowledge graph cite their sources, respect permissions and fail visibly. That's the difference between a chatbot and a system you can audit.
Security, compliance and operability bolted on later cost the most. Built in from day one, they become a delivery accelerator.
Scale is six problems, not one.
Throughput is only the start. I design for every dimension a growing organisation hits, and set explicit targets for each before a line of code is written.
Throughput
1M+ tx / dayEvent-driven, horizontally scalable services with idempotency and back-pressure built in.
Availability
multi-AZRedundancy, graceful degradation and tested recovery, with SLOs agreed with the business.
Latency
p95 budgetsLatency budgets per critical path, with caching and data placement designed to meet them.
Cost
unit economicsCost per transaction, per tenant and per AI token, measured and driven down as volume grows.
Security
zero-trustLeast privilege, encryption everywhere and auditable access, ready for regulated data from day one.
Organisation
team topologyPlatforms and boundaries that let more engineers ship independently without stepping on each other.
Prove it
- Architecture that won't need a rewrite
- MVP on managed services
- First AI use case in production
Make it reliable
- CI/CD, observability, on-call
- Domain model & data contracts
- First senior hires
Make it scale
- Event-driven re-architecture
- Internal platform on Kubernetes
- Knowledge graph & AI platform
Make it governable
- Multi-region, multi-tenant
- Compliance & audit at scale
- Architecture governance
One approach, many domains.
The method stays the same: model the domain, design for scale, build in governance. What changes is the vocabulary, the regulations and the failure modes, and I bring those with me so your team doesn't spend months learning them.
Healthcare & life sciences
Fragmented clinical feeds, competing code systems and strict privacy rules make every integration slow and every AI idea risky.
- Integrations that take months
- Research cohorts nobody can reproduce
- Clinical AI that legal won't sign off
- FHIR-first interoperability with terminology mapping
- OMOP CDM and OHDSI for research-grade analytics
- HIPAA-aligned platforms with de-identification and audit
Financial services & fintech
High volumes, zero tolerance for lost transactions, and auditors who want to see every decision.
- Batch jobs that can't keep up with growth
- Latency spikes at market open or month end
- AI pilots blocked by model-risk and audit
- Event-driven cores proven at 1M+ transactions a day
- Market and trading data pipelines
- Explainable, logged AI with model governance
Enterprise & B2B SaaS
Every team defines a customer differently, the platform buckles as tenants grow, and the AI assistant hallucinates.
- Data that never reconciles across teams
- Noisy-neighbour tenants and rising cloud bills
- Enterprise buyers asking for SSO, audit and residency
- Ontology and semantic layer as the shared model
- Kubernetes platforms with tenant isolation and FinOps
- Enterprise features: SSO/SCIM, RBAC, audit, regions
AI-native startups
Speed matters, but so does an architecture that survives the first big customer and the first due diligence.
- A demo that won't survive real users
- No senior technical leader yet
- Investors asking hard questions about the stack
- Founding architecture for agents, RAG and evals
- Fractional CTO cover until the right hire
- Investor-ready technical story and data room
E-commerce, retail & marketplaces
Search that can't find the exact SKU, order flows that break at peak, and personalisation built on messy product data.
- Search that misses exact product codes
- Peak-season order and inventory failures
- Product data inconsistent across channels
- Hybrid search combining vector and keyword retrieval
- Event-driven order and inventory pipelines
- Product ontologies and clean catalogue models
Media, content & knowledge platforms
Huge volumes of unstructured content that people, search engines and AI all need to find and trust.
- Content locked in silos and formats
- Taxonomies that drift over time
- AI answers with no sources
- Content knowledge graphs and taxonomies
- Aggregation and enrichment pipelines
- Grounded AI search with citations
Investors & private equity
You need to know whether the technology and the team can carry the plan, before you commit.
- Pitch decks that oversell the AI
- Hidden technical debt and key-person risk
- Unclear cost to scale
- Architecture, code, data and team assessment
- AI due diligence: what's real, what's wrapper
- Post-deal value-creation plans
Regulated & sovereign data (US & UAE)
Data residency, overlapping regimes and new AI rules shape what you can build and where it can run.
- Unclear rules across federal and free-zone regimes
- Data that must stay in-country
- AI use cases stuck in compliance review
- Region-pinned platforms with policy as code
- AI gateways with redaction, routing and audit
- Private LLMs for data that can't leave
The full stack, from meaning to machines.
Every layer below is one I've designed, built or operated in production. Breadth matters at this level: the costly mistakes happen at the seams between layers.
Strategy & leadershipWhere the technology meets the business
Ontology & semanticsA shared model of meaning across the enterprise
AI & agentsLLM systems that are measurable and governed
Machine learningModels, features and the infrastructure behind them
Data engineeringPipelines that carry the business, real-time and batch
DatabasesChoosing and tuning the right store for the job
Backend & systemsServices designed for throughput and change
Product & frontendThe surface people actually touch
Cloud & platformReproducible, secure, observable infrastructure
The right tool, chosen for the problem.
Polyglot by necessity. I choose languages for the job, the team that will maintain the system, and the ecosystem around it, not out of habit.
AI and LLM systems, data pipelines, ML, APIs, event processors, infrastructure automation.
Platform services, high-concurrency systems, Kubernetes tooling and distributable CLIs.
React and Next.js front ends, Node.js services, React Native mobile apps.
Analytical modelling, warehouse design, performance tuning across Postgres and BigQuery.
Enterprise services and healthcare research platforms (OHDSI WebAPI).
.NET services, algorithms and data structures.
Web applications, e-commerce APIs and content aggregation.
Angular and React applications, browser tooling, code generators.
Querying knowledge graphs and ontologies: property graphs and RDF.
Aggregation pipelines, schema design and query optimisation at scale.
Infrastructure as code across AWS, Azure and GCP.
Automation, CI/CD pipelines, Kubernetes manifests and Helm.
Let's talk about what you're building.
The fastest route is a free 30-minute call. Prefer to write? Send a message and I'll reply personally within two business days.