Services
Specific pieces of work, grouped by where you are today. Durations are typical, and every engagement is scoped to the problem.
Explore & Define
I have an idea or a problem, but no clear technical direction.
Outcome: A clear understanding of the opportunity, the recommended approach, the risks, the likely investment, and the next practical steps.
AI Readiness Assessment
1–2 weeks
You suspect AI could help, but you're not sure your data, systems, or people are ready for it.
- Readiness review across data, systems, skills, and risk
- Gaps ranked by impact
- A short, prioritised action list
AI Opportunity Discovery
1–3 weeks
Everyone has AI ideas. Nobody knows which ones are worth the money.
- Structured opportunity workshop
- Opportunities scored on value, feasibility, and risk
- Two or three recommended starting points
Business Process Analysis
1–3 weeks
Work is slow, manual, or fragile, and it's hard to see where technology would actually help.
- Current-state process maps
- Bottlenecks and automation candidates
- Target-state recommendations
AI Strategy and Planning
3–6 weeks
Leadership wants an AI position that holds up to scrutiny, not a slide of buzzwords.
- AI strategy tied to business goals
- Build, buy, or partner guidance
- Phased plan with investment ranges
Technical Feasibility Assessment
1–4 weeks
You have an idea and need to know whether it can be built, roughly how, and at what risk.
- Feasibility findings and key unknowns
- Candidate architecture
- Spike or proof-of-concept where needed
Product Discovery Workshop
2–5 days
The product idea is exciting, but the scope, users, and first release are still fuzzy.
- Facilitated discovery sessions
- User and problem definition
- Scoped first release
Technical Roadmap
2–4 weeks
You know where you want to get to, but not the sequence of technical work that gets you there.
- Sequenced roadmap with dependencies
- Team and capability needs
- Decision points and checkpoints
AI Adoption Planning
2–4 weeks
Tools are being bought, but nobody is planning how people will actually adopt them.
- Adoption plan by team and workflow
- Training and change approach
- Usage and value measures
Build & Implement
I know what needs building. I need someone who can make it real.
Outcome: A working prototype, system, product, integration, or automation that is usable, testable, and ready for the next stage.
AI Product Prototyping
3–8 weeks
You need something real in front of users or investors, quickly, without throwing it all away later.
- Working prototype
- Evaluation of model behaviour and quality
- Notes on what it takes to reach production
AI Agent and Workflow Development
4–10 weeks
Repetitive, judgement-light work is eating your team's time.
- Agent or workflow built around your tools
- Human review points where they matter
- Monitoring and evaluation
Software Engineering
Scoped per project
You need dependable software built by someone who also understands the wider system.
- Production-quality code
- Tests, documentation, and handover
- Delivery alongside your team or independently
Technical Architecture
2–6 weeks
Early decisions will shape cost, speed, and reliability for years. You want them made well.
- Architecture design and decision records
- Infrastructure and data approach
- Security and scaling considerations
LLM and Data Integration
3–8 weeks
Your knowledge lives in documents, databases, and systems that an AI model can't yet use reliably.
- Retrieval and data pipelines
- Model selection and evaluation
- Guardrails, cost, and latency tuning
Voice AI Systems
4–10 weeks
Calls, intake, or field conversations could be handled better with voice, but latency and accuracy matter.
- Voice agent or pipeline
- Telephony and system integration
- Testing for accuracy, latency, and edge cases
Internal Tools and Automation
2–8 weeks
Spreadsheets and copy-paste are holding the business together.
- Internal tools built for real workflows
- Integrations between existing systems
- Automations with clear ownership
Hardware, IoT, and Software Integration
Scoped per project
Devices, sensors, firmware, and cloud software all need to work as one system.
- Device-to-cloud architecture
- Data capture and telemetry pipelines
- Integration, testing, and field readiness
Production Implementation
4–12 weeks
The prototype works. Now it has to be secure, observable, and dependable.
- Hardening and deployment
- Monitoring, alerting, and runbooks
- Handover to the team who will run it
Optimise & Lead
We have systems and a team. We need them to perform better.
Outcome: Stronger technical direction, better-performing teams, clearer priorities, improved systems, and a practical plan for sustainable growth.
Fractional CTO Support
Ongoing, monthly
You need senior technical leadership, but not a full-time executive hire yet.
- Regular leadership time each week or month
- Technical strategy and board-level communication
- Hiring, vendor, and architecture decisions
Technology Leadership
3–12 months
A key initiative needs an experienced technical lead to own it end to end.
- Leadership of a defined programme
- Planning, delivery, and stakeholder alignment
- Clear reporting and decisions
Technical Team Mentoring
Ongoing
Capable engineers or new leads need someone experienced to grow with.
- One-to-one and group mentoring
- Code, design, and decision reviews
- Growth plans for individuals
Engineering Team Cohesion
1–3 months
The team is talented but not pulling in the same direction.
- Team and process assessment
- Working agreements and ownership
- Delivery rhythm that people trust
Architecture Review
1–3 weeks
You need an honest outside view of a system before you invest more in it.
- Review of architecture, code, and infrastructure
- Risks ranked by severity
- Practical improvement plan
Existing-System Optimisation
2–8 weeks
The system works, but it's slow, costly, brittle, or hard to change.
- Performance and cost analysis
- Targeted improvements
- Before-and-after measures
AI Transformation Programmes
3–12 months
AI needs to move from scattered experiments to coordinated, measurable change.
- Programme design and governance
- Prioritised portfolio of AI initiatives
- Delivery leadership and adoption tracking
Scaling Architecture and Delivery
1–6 months
Growth is exposing limits in your platform, processes, or team structure.
- Scaling plan for systems and teams
- Platform and delivery improvements
- Capacity and reliability planning
AI Governance and Policy
2–6 weeks
People are already using AI. You need sensible rules that don't stop useful work.
- AI use policy and guidelines
- Approval and review processes
- Roles, responsibilities, and training
AI Safety and Security Assessment
1–4 weeks
You need to know how your AI systems could fail, leak, or be misused.
- Threat and failure-mode review
- Prompt injection and data exposure testing
- Mitigation plan
Technical Due Diligence
1–3 weeks
You're investing in, acquiring, or partnering with a technology business and need an expert view.
- Technology, team, and risk assessment
- Clear findings for non-technical decision-makers
- Post-deal recommendations
Have a technology or AI problem worth solving?
A 30-minute conversation. No pitch, just an honest view of whether and how I can help. Or talk to Maya, my AI guide, right now.