I help founders turn an AI idea into a focused, testable product: validate the customer problem, choose one journey worth building, create the app with AI-assisted tooling, evaluate the model against real inputs and launch with analytics. You do not need a technical co-founder before you can test whether the product deserves to exist.
START HERE
Start with the job the user needs to complete, not with the model or the feature list. A strong first AI app normally has one clear customer, one valuable journey and one measurable reason for that customer to come back or pay.
If you want to build an AI app without an internal engineering team, the first version can be created with AI-assisted tools such as Lovable, Cursor or Bolt. The important work is still deciding the scope, protecting customer data, testing whether the AI output is good enough and making the app reliable outside the demo path.
SCOPE
These are common product shapes, but the right MVP usually starts with one narrow workflow rather than trying to combine several at launch.
A task assistant grounded in your domain, with guardrails, evaluation and a clear escalation path.
Answers over your own documents with citations, retrieval testing and clear behaviour when the source material is insufficient.
A manual workflow turned into a product with triggers, approvals, structured data and an audit trail.
Two-sided supply and demand with listings, search, matching and the smallest journey needed to test liquidity.
People, jobs, journeys or inventory matched by rules, embeddings or AI where it genuinely improves the decision.
Inbound or outbound voice with speech, model logic and telephony, designed around latency, escalation and real call failure modes.
Automation focused on repeatable service tasks, measured on resolution and trust rather than message volume.
Ops dashboards, review queues and back-office workflows where the value can be measured against time or error reduction.
An AI feature added to a product that already has users, with evaluation, cost and failure handling designed before rollout.
THE REAL WORK
Lovable, Cursor and Bolt can accelerate implementation. They cannot decide which customer problem is worth solving, whether the model is reliable enough for the job, how permissions should work, or whether the economics survive real usage.
| Factor | AI builder alone | Product-led build |
|---|---|---|
| Scope | Everything that can be generated quickly. | One journey that solves a real customer problem. |
| Data | Seeded demo data and the happy path. | Real inputs, permissions and edge cases. |
| AI quality | Looks impressive in a controlled demo. | An eval set, acceptance thresholds and a plan for failure. |
| Auth and data access | Often added late. | Accounts, roles and access rules tested before launch. |
| Cost | Unknown until usage arrives. | Model and infrastructure cost considered per user or successful action. |
| Launch | A live URL. | Positioning, onboarding, analytics and a commercial test. |
Need ongoing product leadership after launch? See fractional CPO engagements.
DELIVERY SHAPE
This is the delivery shape I use once the problem is validated and the core journey is locked. It is not a universal promise for every AI product; more roles, integrations, specialist requirements or unresolved validation change the scope.
Customer evidence, problem definition and the decision on what not to build.
Wireframes, core journey, AI stack decision and evaluation plan.
Frequent deploys using the right AI-assisted build tools for the product and scope.
Landing page, pricing, onboarding, analytics and the conversion path.
Release, first usage, handover and the next validation roadmap.
PRICING
My current focused web AI MVP engagement is normally scoped between £12,000 and £25,000 once the problem and core journey are clear. That is my service range, not a UK market benchmark. Integrations, data access, user roles, AI evaluation requirements and specialist risk or compliance needs can change the final scope.
PROOF
Core professional proof is kept separate from experimental product-lab concepts. Each card links to the evidence-led case study.
Mobility
A workplace and community carpooling platform designed to match passengers with drivers already making substantially the same journey, reducing commuting costs, parking pressure and unnecessary vehicle journeys.
Read case study →AI
An AI receptionist designed for trades, clinics, salons and appointment-led businesses. It answers calls, identifies customer intent, qualifies enquiries and moves suitable customers toward an appointment or callback.
Read case study →Music Technology
A direct-to-fan music ecosystem exploring digital ownership, independent artist commerce, music learning, AI-assisted creativity and physical-digital collectibles.
Read case study →SaaS
Wocal was a SaaS platform connecting remote workers with participating venues and flexible places to work. I founded the business and led product from 2020 to 2025, scaling the platform to 300+ venues with a reported £2.7M pre-money valuation.
Read case study →FREQUENTLY ASKED
REQUEST A QUOTE
The useful starting point is the customer, the job they need to complete and any evidence you already have.
Prefer to build it yourself? Explore the AI MVP course.
Need ongoing product leadership? See fractional CPO.
IDEA TO MVP GUIDES
These guides cover validation, scope and the practical path from a raw founder idea to a focused product in customers' hands.
A practical six-week route from validation and scope through build, launch and first customer evidence.
Read the guide GUIDEA compressed founder-led plan for validating, scoping, building and launching one core journey with AI tools.
Read the guide GUIDEA ten-step validation checklist for testing the problem and willingness to pay before writing production code.
Read the guideEXPLORE NEXT
Every stage from first idea to first revenue, mapped end to end.
Audit a Lovable or AI-built app, fix the real launch blockers and take it to a production-ready release.
Read moreFree 20-question check for customer evidence, access and data, reliability, AI quality and commercial launch readiness.
Read moreHow a shared-travel marketplace was scoped down to one journey worth building.
Read moreDesigning an AI call product where being wrong costs the customer money.
Read more