Build AI-powered SaaS products customers actually pay for
I design and build production-grade AI SaaS products end-to-end — from the data model and agent architecture to the billing flow and admin dashboards. Backed by 10+ years of full-stack engineering, built on the modern AI stack: Next.js, Supabase, OpenAI, Anthropic, TypeScript.
- MVP in 4–8 weeks, production-ready
- Full-stack: backend, frontend, AI layer, infra
- Clean architecture that scales past Product-Market Fit
Who I build with
I work with founders and CTOs who need a senior partner to own the technical build of an AI-native SaaS — not a generalist agency churning out templates.
Seed & Series-A founders
You've validated demand, raised capital, and now need to ship a real product before runway bites.
Solo founders with domain expertise
You know your industry inside-out and have a wedge for AI — but need a technical partner who can ship without hand-holding.
CTOs expanding capacity
Your team is solid but doesn't have the LLM integration experience to ship the AI product line by the deadline.
Agencies bringing in specialists
You deliver client work but need a deep AI engineer to join the team on an agentic or LLM-heavy project.
Why most AI SaaS builds stall
Most AI SaaS projects I see fail for the same reasons: teams treat the AI as a bolt-on prompt instead of the core product, architecture choices from a no-code prototype don't survive a real user load, token economics are ignored until the first month's OpenAI bill arrives, and the infrastructure gets duct-taped together from a dozen services that can't be swapped out later.
The result is a product that demos well, ships late, costs 3x more to run than expected, and breaks every time you try to iterate. Founders end up rewriting from scratch six months in — losing the runway they needed to find product-market fit.
A real AI SaaS build isn't about picking the trendiest model. It's about designing the right architecture for your use case: when to cache, when to stream, when to run agents, when to use embeddings, when a cheap model is fine, when you need a reasoning model, and how to make all of that swappable as the landscape shifts every few months.
How I build AI SaaS that scales
I approach AI SaaS builds as production systems, not demos. Every decision is made with cost, latency, maintainability, and evolvability in mind.
Product-first architecture
I start with your user's job-to-be-done, not the tech stack. The architecture follows the product — not the other way around. This means the AI layer is shaped around your workflow, not forced into a generic chat interface.
Swappable AI layer
Models change monthly. I build an abstraction layer so you can swap between OpenAI, Anthropic, open models, or fine-tuned variants without touching business logic. Lower costs and hedging against vendor lock-in, built in from day one.
Token-aware by default
Every LLM call is budgeted, cached, and observable. Cost per user, cost per feature, cost per customer cohort — all tracked. You know your unit economics before you launch, not after a surprise bill.
Type-safe end to end
TypeScript everywhere with Zod runtime validation and structured outputs from LLMs. No runtime surprises when the model returns unexpected JSON — the system handles it gracefully and logs for improvement.
Observability from day one
Every prompt, response, token count, latency, and error is logged and queryable. You can debug a bad response from three weeks ago. You can A/B test prompt changes. You can see which features actually get used.
Production infrastructure
Supabase for data + auth, Vercel for compute, proper RLS, background jobs, rate limiting, monitoring, CI/CD, and staging environments. No DIY servers, no flaky deploy scripts — boring, reliable, fast.
How we work together
Every engagement follows the same phased process. You always know what's happening, what's next, and what decisions need your input.
Discovery (Week 0)
30-min call to understand the product, then a deeper technical scoping session. I map the user flows, agent behaviors, data model, integrations, and cost model. You get a written proposal with architecture diagram, milestone breakdown, fixed price, and timeline.
Foundation (Week 1)
Repo setup, Supabase schema, auth flow, core UI shell, AI abstraction layer, observability, deployment pipeline. By end of week 1 you can log in, see the shell, and every change deploys automatically to a preview URL.
Core build (Weeks 2–6)
I ship features in weekly milestones. Each week ends with a demo URL you can test. You see real progress every Friday, not a month later. Client portal shows milestone status in real-time.
Polish & launch (Final week)
Load testing, edge cases, onboarding flow, billing integration, production secrets, domain + SSL. Launch-readiness checklist signed off before go-live.
Post-launch (30 days)
Included: bug fixes, small adjustments, monitoring. Optional retainer if you want ongoing iteration and new features.
What you get
Investment
Fixed-price, milestone-based. No hourly billing, no scope creep surprises. You get the exact scope we agree on in the proposal.
AI MVP
$12,000 – $22,000
4–6 weeks. Core product with 2–3 key AI features, authentication, billing, admin dashboard, deployed to production. Right for validating an idea with real users.
- 2–3 core AI features
- User auth + profiles
- Billing integration
- Admin dashboard
- Production deployment
- 30-day post-launch support
AI SaaS Build
$25,000 – $55,000
6–12 weeks. Full production product with multiple agent flows, complex data models, integrations, and polish. Right for funded teams launching a real product.
- Multi-agent or complex AI flows
- Custom integrations
- Advanced data model with RLS
- Multi-tenant support
- Observability + analytics
- Onboarding flow + documentation
- 30-day post-launch support
Retainer
From $6,000/month
Monthly engagement for ongoing feature work, iteration, or embedded CTO-level support post-launch.
- Dedicated weekly capacity
- Priority response
- Roadmap + architecture advisory
- Ongoing ops + monitoring
Every engagement starts with a 30-min discovery call and a detailed written proposal — no surprises.
Tech Stack
Working With Clients Across
Recent Work
Related case studies
Frequently asked
Can you work with our existing codebase?
Yes — I take on engagements that start from scratch as well as additions to existing Next.js/Node.js codebases. For legacy stacks (Ruby, PHP, older Python), I can design the new AI service to run alongside and integrate cleanly.
Do I own the code and infrastructure?
Yes, completely. All code is built in your GitHub org, all infrastructure (Supabase, Vercel, OpenAI, Stripe) is in accounts under your name. You have full ownership from day one. My IP rights end at final payment — the code is yours.
How do you handle LLM costs?
Every project starts with a cost model: expected tokens per user action, caching strategy, model selection per feature. You see projected monthly AI spend before build starts. In production, costs are observable in real-time per user and per feature.
What if OpenAI releases a better model mid-build?
The AI layer is abstracted so switching models is a config change, not a rewrite. This is baked in by design — model churn is a fact of life in AI and the architecture anticipates it.
How many projects do you run at once?
Maximum two active builds at any time. This keeps velocity high and context deep. When a slot opens, it's typically booked 2–4 weeks out.
Can you sign an NDA?
Yes. I'm happy to sign mutual NDAs before discovery calls. Standard engagements also include confidentiality clauses.
Do you do equity-only deals?
No — I focus on paid engagements so I can dedicate senior-level attention without distribution conflicts. For the right opportunity, equity can be part of compensation alongside cash.
Ready to start?
Every engagement starts with a 30-minute discovery call. I'll listen, ask sharp questions, and send a written proposal within 48 hours.
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