Industry

Startups

Move fast with solid foundations

I partner with startups as a technical co-founder figure-helping make foundational decisions, build MVPs, and establish practices that scale. From pre-seed to Series B, I understand each stage's unique challenges.

At a glance
Regulations
5 frameworks
KPIs tracked
6 core metrics
Reference stacks
5 patterns
Services
4 engagements
Case studies
0 published
Perspective

How I think about startups

The architecture, the trade-offs, and where I push back on conventional wisdom.

Startups don't fail because the code wasn't elegant. They fail because they ran out of money, built the wrong thing, or couldn't hire fast enough when traction hit. My role as a technical partner is to keep you out of all three failure modes for as long as possible - to help you ship the right MVP fast, to keep your runway honest, and to set up an architecture that doesn't punish you when you 100x.

The pre-seed and seed stage is about ruthless prioritization. Every architectural decision should be reversible at low cost or deferred entirely. I push teams toward the boring stack - Next.js on Vercel, Postgres via Supabase or Neon, Resend or Postmark for email, Stripe for billing - because the boring stack ships in days, not months, and works fine to ten million dollars in revenue. The interesting engineering can come later, when you've earned the right to it.

Hiring your first engineers is the most consequential thing a technical founder does in their first year. The first three hires set the engineering culture for the next fifty. I help founders interview and onboard - designing technical screens that test for real signal, building a 30-60-90 ramp plan, and mentoring the senior IC who'll eventually become a tech lead. Bad first hires cost you 18 months. Good ones unlock the rest of the team.

Series A is where the architecture you got away with starts hurting. The monolith that was fine at five engineers gets gridlocked at fifteen. The hand-rolled deploy script that worked at 100 deploys/month breaks at 1,000. The Heroku bill stops being a rounding error. I help teams sequence the modernization - extract the noisy services, introduce CI/CD discipline, build the platform team - without losing the velocity that got you to A in the first place.

The thing I tell every founder is that technical debt is a real liability, but premature scalability is a worse one. Build for the next 6-12 months, not the next 5 years. Buy not build for everything that isn't your differentiator. Hire ahead of pain, not behind it. And find a senior advisor who's seen the movie before - the cost is dwarfed by the mistakes you'll avoid. See how I helped launch an MVP or start a project.

Challenges

What teams struggle with

The recurring problems I see on startups engagements.

  • 1Building fast without accumulating crippling debt
  • 2Making foundational decisions with limited data
  • 3Hiring and onboarding first engineers
  • 4Scaling from 0 to 1 to many
  • 5Balancing feature velocity with technical quality
How I help

Capabilities I bring

Concrete engineering work that resolves the challenges on the left.

  • MVP development in weeks
  • Technical co-founder advisory
  • First engineering hire coaching
  • Investor pitch technical preparation
  • Scaling architecture planning
Metrics

What teams measure

The KPIs leadership obsesses over in this sector. Most tie back to performance and architecture decisions made years before the dashboard was built.

01

Runway (months)

The single number every founder watches; engineering decisions should always be priced in runway.

02

Burn multiple

Net burn divided by net new ARR; <1.5 is healthy, >2 is a warning sign.

03

Time-to-first-deploy

Days from a new engineer joining to merging code to production; a culture metric in disguise.

04

Activation rate

Signups that reach the aha moment; the leading indicator of product-market fit.

05

Weekly active engineers shipping

How many people moved a story to done; reveals onboarding and platform health.

06

p95 page load on the marketing site

Conversion lever for inbound, often the cheapest fix in the early stack.

Reference stacks

Stacks I see most often

Patterns I reach for first when scoping a startupsengagement. I don't pick technologies for novelty - read more about how I choose.

1

Next.js + Vercel + Supabase or Neon Postgres + Stripe + Resend

2

Rails or Django on Render/Fly.io for teams that prefer batteries-included frameworks

3

TypeScript monorepo with Turborepo, Tailwind, shadcn/ui, Drizzle or Prisma

4

OpenAI or Anthropic with LangChain or LlamaIndex for AI-native MVPs

5

PostHog for product analytics, Sentry for errors, Linear for engineering ops

Technologies

Tools of the trade

The platforms and frameworks I lean on for startups work.

Building for Startups?

Let's discuss your specific challenges and how technology can help you ship safely, sleep well, and keep regulators happy.

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