01: The problem
Most AI startup MVPs die between the demo and the launch.
You raised the round on a working demo. Six months later, the product still isn't in users' hands. The auth flow isn't done. The billing is "almost ready." The model behaves differently in production than it did in the demo. The team is shipping in circles.
The gap between "it works on my laptop" and "real users are paying for it" is where 95% of funded AI startups stall. It's not because the AI is hard. It's because shipping a SaaS product around the AI is hard, and most teams underestimate everything that lives outside the model.
We close that gap. We've shipped production software for years and AI products for long enough to know where the edges hide, the model, the app it lives inside, and the infrastructure underneath. And we charge you a fixed quote to ship yours.
02: Right fit?
Is this the right fit?
Best for
- ✓Pre-seed, seed, or Series A startups with funding and an AI product thesis
- ✓Solo founders or 2–3 person teams who need engineering capacity, not advisors
- ✓Teams who've validated the problem and need the product shipped, not re-discovered
- ✓Founders who'd rather have a fixed quote than an "it depends" timeline
Probably not
- —Pure idea stage with no validation, no users, no funding
- —Projects that need 6+ months of research before any code is written
- —Teams shopping the lowest hourly rate
- —"Build me Cursor / ChatGPT / Lovable" without a differentiated angle
03: What you get
What you get when we ship
Every AI SaaS MVP we ship is a complete, paying-customer-ready product. Not a prototype. Not a demo. Not a "working spike." A live product on your domain, with users signing up and getting billed.
01The product itself
- Branded web app on your domain
- Sign-up, login, password reset, team invites
- Subscription billing (Stripe), free trials, plan tiers
- User dashboard, settings, usage limits
- Admin panel for you to manage users and accounts
02The AI core
- The intelligence that makes your product worth paying for, agents, predictions, generation, retrieval, or whatever your product thesis requires
- Model selection (we'll recommend the right one for your use case and budget)
- Prompts, evaluations, and guardrails, so the AI behaves the same in production as it did in the demo
- Cost controls so the bill doesn't surprise you on month one
03The infrastructure underneath
- Hosting on your accounts (AWS, Azure, GCP, Vercel, your call)
- Database, storage, monitoring, error tracking
- Backups, security baseline, uptime monitoring
04The launch kit
- Marketing site (the page that sells the product)
- Onboarding flow that converts
- Analytics + funnel tracking from day one
- Documentation and support email setup
- A go-live checklist we run together before launch
04: How we work
The Production Path
Every engagement runs on the same 4-phase delivery system. No surprises. No "discovery phase that never ends." No hourly invoices.
Phase 1
Discover
1 week
A paid Scoping Sprint. We pressure-test the AI use case, sketch the architecture, define the MVP scope, and deliver a fixed quote for the full build. Ends with a go/no-go, you can walk after the sprint.
Phase 2
Build
4–10 weeks
We ship in weekly milestones. You see working software every Friday. The AI layer, the app, and the infrastructure are built in parallel, not sequentially.
Phase 3
Harden
1–2 weeks
Evals, load testing, security review, billing live, monitoring wired up. We don't call it "done" until your product is genuinely ready for paying users.
Phase 4
Operate
30 days included, retainer optional
Post-launch support included for 30 days. After that, optional retainer for monitoring, iteration, evals, and feature work, most clients move into one at month two.
05: Stack
The stack we ship on
We pick the boring, battle-tested stack, because the AI is novel enough; the rest of the product shouldn't be.
- Frontend
- Next.js, React, TypeScript, Tailwind, shadcn/ui
- Backend
- Node.js, Python (FastAPI), TypeScript-first APIs, REST + GraphQL
- Database & storage
- PostgreSQL, Supabase, pgvector (for embeddings), Redis, S3-compatible storage
- Auth & billing
- Clerk, Auth0 or NextAuth (auth), Stripe or Paddle (billing), your call
- Models
- Claude, GPT-4o, open-source (Llama, Mistral), selected per use case and budget
- AI tooling & evals
- LangGraph, LangChain, LlamaIndex, custom orchestration; Langfuse, LangSmith, RAGAS for evals
- Infrastructure
- AWS, Azure, GCP, Vercel, Cloudflare, deployed to your accounts, not ours GitHub Actions / GitLab CI for deploys Monitoring: Sentry, Datadog, OpenTelemetry
06: Why us
Why we'll still be here when you launch
The Production Guarantee
If your MVP doesn't ship, we don't invoice the final milestone. We're accountable for production, not hours.
You own everything
Code, prompts, fine-tunes (if any), data, infrastructure. Hosted on your accounts. No license fees. No vendor lock-in. You can fire us and take the whole product tomorrow.
Methodology in the open
The full Production Path is published. Phase by phase, what you get, what we need from you, exit criteria. The only thing we don't publish is pricing, that's quoted after scoping.
NDA standard
Every engagement is covered by an NDA by default. Mutual NDA on request before the scoping call.
07: FAQ
Frequently asked
How long does a typical AI SaaS MVP take?+
8–14 weeks for the production build, plus a 1-week Scoping Sprint up front. Total: 9–15 weeks from kickoff to live users. The exact timeline lands in the fixed quote after scoping.
What does it cost?+
We don't publish pricing because scope varies heavily, the AI core for a vertical SaaS is very different from the AI core for a consumer app. The Scoping Sprint is fixed-fee; you'll have the full quote at the end of week 1, before you commit to the build.
Can you work with our existing technical co-founder or in-house engineer?+
Yes, we frequently work alongside in-house engineers and technical co-founders. We ship the core product, your engineer owns specific modules, and we document properly and hand off cleanly. No "you can't read our code" lock-in.
What about post-launch?+
30 days of post-launch support is included in the build. After that, most clients move into a retainer for ongoing iteration, monitoring, and feature work. Some take the codebase in-house. Both are fine.
Who owns the IP?+
You do. Full source, full prompts, full infra on your accounts. We don't keep a license fee, a revenue share, or any other long-term hook.
Will the AI behave differently in production than in our demo?+
Yes, that's the trap. We solve it with evals, prompt versioning, and observability from day one. Every AI feature has a measurable success criterion before we call it shipped.
What if our scope changes mid-build?+
Small changes inside the scope are absorbed. Material scope changes trigger a written change order with a delta to the fixed quote. No surprise invoices, no scope creep arguments.