Projects
How I’d help, by industry.
I won’t hand you a wall of logos or invent a portfolio. Here’s the honest version: I may not have built your exact product, but I’ve solved its shape before. Below is how I’d approach the technical strategy and build in your world — the risks I’d watch for, and the first moves I’d make.
HealthTech
Clinical-grade software under real regulation.
You’re a clinician or health founder who understands the care problem better than any engineer ever will. What you need is someone who turns that into a compliant, auditable platform without slowing you to a crawl.
Where it usually goes wrong
- Privacy and data-residency handled as an afterthought, then re-architected under duress before a hospital or insurer will sign.
- Integration with legacy clinical systems (HL7/FHIR, practice management, pathology) underestimated until it blocks launch.
- “Move fast” AI features shipped without the audit trail or clinical-safety case a regulator will ask for.
How I’d help
- Design the platform HIPAA / SOC 2-aligned from day one — data boundaries, encryption, access model and audit logging baked into the architecture, not bolted on.
- Map the integration surface early (FHIR/HL7, EHR, identity) and de-risk the hardest connection with a spike before it’s on the critical path.
- Stand up agentic coding workflows with human review gates on anything clinical-safety-relevant, so you get speed and a defensible paper trail.
- Give you a board- and buyer-ready technical narrative that a clinical-governance or procurement committee will actually trust.
First 90 days · A compliance-aware architecture, a proven integration spike, and a delivery cadence your team and auditors can both follow.
LegalTech
Trust, confidentiality and defensible automation.
Legal buyers are conservative for good reason — confidentiality and accuracy are the product. You need to move quickly on AI without ever putting privilege or client data at risk.
Where it usually goes wrong
- Client-confidential data flowing through third-party LLMs with no data-processing controls or retention guarantees.
- AI drafting features that hallucinate, with no citation or human-in-the-loop, quietly creating malpractice exposure.
- Matter and document data locked in silos that never become the structured foundation real automation needs.
How I’d help
- Architect AI features on a governed data pipeline — private model endpoints, no-train guarantees, retention and access controls you can put in front of clients.
- Put retrieval, citation and mandatory human review around every generative feature so speed never trades against defensibility.
- Automate the genuinely repetitive — intake, conflict checks, document assembly, discovery triage — where ROI is highest and risk is lowest.
- Own the vendor and compliance conversation: the build-vs-buy calls, the DPA review, the security posture a firm’s risk committee will sign.
First 90 days · A confidential-by-design AI architecture, one high-value workflow automated end-to-end, and a governance model your partners trust.
Fintech
Speed without breaking the things that hold customer money.
Trust is the whole business. Security, compliance and reliability aren’t features — they’re your licence to operate. But you still have to ship faster than the incumbents.
Where it usually goes wrong
- Compliance (KYC/AML, PCI, data protection) treated as a late-stage checklist instead of an architectural constraint.
- A monolith that couples payments, ledger and customer data so tightly that every change is high-risk.
- Security debt that surfaces at the worst moment — mid-way through a partner-bank or enterprise due-diligence review.
How I’d help
- Set an architecture that isolates money movement and sensitive data, with the audit trails, idempotency and reconciliation fintech actually requires.
- Bake compliance into the build — controls mapped to obligations, evidence generated as a by-product of delivery, not a scramble before an audit.
- Use agentic coding for velocity where it’s safe to move fast, and human-gated rigour on the core where it isn’t.
- Prepare you for partner-bank and investor diligence with a security posture, threat model and roadmap that stand up to scrutiny.
First 90 days · A de-risked core architecture, a compliance-to-controls map, and a credible security story for your next partner or raise.
Professional Services
Turn expertise-for-hire into leveraged software.
You sell expertise, and lose margin every time that expertise gets trapped in manual, repeatable work. The opportunity isn’t a moonshot product — it’s systematising what your best people already do.
Where it usually goes wrong
- Knowledge and process living in individuals and spreadsheets, so growth means linearly hiring more people.
- A patchwork of SaaS tools that don’t talk to each other, with humans as the integration layer.
- AI experiments that impress in a demo but never get wired into how the work actually happens.
How I’d help
- Find the three to five workflows where automation and AI reclaim the most billable hours, and sequence them by ROI.
- Build the internal tools and integrations that connect your stack so data flows instead of being re-keyed.
- Deploy agentic automation on the high-volume, judgement-light work — reporting, intake, document generation — with your experts reviewing, not typing.
- Give you operating leverage: grow revenue without growing headcount at the same rate.
First 90 days · A ranked automation roadmap and one workflow live in production, reclaiming measurable hours every week.
E-commerce & Retail
Systems that see and react in real time.
You win on conversion, fulfilment and the systems that connect them. Growth exposes every seam between storefront, inventory, fulfilment and support.
Where it usually goes wrong
- A stack of disconnected tools held together by manual operations that break at peak season.
- Infrastructure that buckles under traffic spikes exactly when it’s most expensive to fail.
- Data spread across platforms, so you can’t answer basic questions about margin, stock or customer behaviour.
How I’d help
- Unify storefront, inventory, fulfilment and support into a coherent, event-driven stack with a single source of truth.
- Harden infrastructure for peak load and instrument it so you see problems before your customers do.
- Automate the manual operations — reconciliation, routing, customer comms — that quietly cap how far your team can scale.
- Bring AI where it moves numbers: merchandising, support deflection, demand forecasting — measured against revenue, not novelty.
First 90 days · A connected core stack, peak-ready infrastructure, and the first manual operation automated away.
Early-stage & VC-backed Startups
Build the right thing, fast, and survive diligence.
Every dollar and week counts. You need to prove the model, not gold-plate it — and end up with a technical story an investor believes at Seed or Series A.
Where it usually goes wrong
- A small team spread across too many technical decisions, making expensive, hard-to-reverse ones by default.
- Building the wrong thing quickly — velocity pointed at unvalidated features.
- A codebase built for the demo that becomes a liability the moment you try to hire or raise.
How I’d help
- Scope the smallest product that actually tests the model, and sequence the build so the riskiest unknowns are proven first.
- Set an architecture that’s right-sized for now but won’t have to be thrown away at Series A.
- Use agentic coding to get a lean team to production fast, with the tests, CI and repo standards that make it hireable and auditable.
- Carry the technical half of your investor story — the diligence, the roadmap, the “why this scales” — so you can focus on the raise.
First 90 days · A validated, user-testable MVP on an architecture you won’t regret, and a technical narrative that stands up to diligence.
Don’t see yours?
The pattern travels.
These are illustrations, not limits. The through-line is the same in every industry: understand the business fast, find where technology creates or destroys value, and build with governed, AI-native speed. Tell me your world and I’ll give you an honest read on how I’d approach it.
Let’s talk about your world.
Bring the business problem. I’ll bring an honest read on whether — and exactly how — I can help.