Industry-deep across insurance, healthcare, regulatory ops, and lending, and the consultancies that serve them.
I'm Lindsay. I make the build/buy/stay call, design the migration, manage the cutover, and run the platform your numbers are tied to. No translation tax. No team to hire. CTO at Context Is Everything; creator of PlanB Backups and 60 Bubble plugins.
Or if your platform is how you deliver, visit Context Is Everything →
The wedge
Vibe coding has become a commodity. Knowing what to type hasn't. Every translation hop, business reality, agency, spec, LLM, loses nuance. I already speak the four industries I work in, so the brief that goes to the agent is direction, not translation. Sometimes the right answer is to buy off-the-shelf instead of building. I'll tell you that on day one.
Architecture, agent direction, cutover plans, service-level commitments (SLOs), ongoing operation. Vibe coding produces working software. It doesn't land a cutover at 2am or hold a platform to the business KPIs it drives. Engineering is judgement and accountability. I sign up for both.
Adopting AI · the strategic question
Every business gets that AI will reshape their cost base. Most are stuck in pilot mode, a Claude prompt here, a ChatGPT workflow there, no system. The companies that get this right won't be the ones with the best models. They'll be the ones who put their own context, methodology, customer knowledge, institutional memory, inside the AI.
That's what I co-founded Context Is Everything for. We systematise the move from "AI is a tool we use" to "AI is in the product we sell." Inside delivery, inside the commercial offer, inside the platform itself.
Where I work
What I bring
Your industry isn't a learning project. I've been in the room since long before "Fractional CTO" was a phrase.
Context Is Everything systematises how serious businesses move from "we've tried ChatGPT" to "AI is in our delivery, our product, and our commercial pitch." The Contour methodology, the SASHA platform, and a team that's done it before, available when the engagement calls for it.
A private on-premise LLM trained on your proprietary methodology, when "AI inside the product" needs to be defensible against generic competitors.
What you're actually buying
Predictable infrastructure. SLOs owned and reported, mapped to the business KPIs that matter in your industry. No 2am outages. No compliance time bomb sitting under the business.
A platform that takes 10× customers, a new product line, a new geography, or an acquirer's due diligence, without rewriting again.
Build 5× faster with directed AI agents. Lower headcount, better margin, faster cycle time. The efficiency story.
New capabilities your customers will pay for. A repositioned commercial pitch, AI-native broker platform vs we have software. Higher acquisition multiple. The revenue story.
Platform SLOs mapped to the business KPIs they drive, time-to-bind, FP17 completion, AIP cycle time, audit-cycle time. If platform misses, fixing it is in scope. If business misses with platform green, that's a different conversation. Either way, you stop debating which.
Same conversations keep coming up. AI that works in a demo but not in production. Apps that outgrew their platform six months ago. Projects that failed before anyone opened an editor. I write about the stuff I actually see.
A renamed enum crashed a live interview. The fix: generate the consumer’s TypeScript directly from the schema column in the database. The entire drift class becomes a compile error.
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The LLM’s default is Prisma. Most teams should take it. Here’s the criteria for when you shouldn’t, and the prompt that does the SQL-first setup in an afternoon.
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Operational work used to need a team because the cost wasn’t the work. It was the context switch. An agent in the terminal collapses it.
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GA4 funnels, secrets inventory, security scanning, OWASP review and more, each reproducible from a single copy-pasteable prompt to Claude Code or Codex.
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