You've got a SaaS idea. You've validated the problem. Now you need software that works — yesterday.
The old playbook: hire a dev shop, burn 3–6 months and $50K–$150K, pray the scope doesn't creep. The new playbook: use AI to compress the build cycle, but don't let the hype fool you into thinking the AI builds it for you. It doesn't. It accelerates you (or the dev you hire).
Here's how we actually do it at TF Globe — no vaporware, no "AI agent" fantasies, just the workflow that gets a custom MVP live in 4–8 weeks for SMB/startup budgets.
The Real Bottleneck Isn't Code — It's Decisions
AI writes boilerplate in seconds. It scaffolds auth, databases, APIs, UI components. What it can't do is decide:
- Which 3 features actually make the MVP viable (not "nice to have")
- Where the data model will bite you in month 3
- Whether you need multi-tenancy now or can fake it with row-level security
- How to handle billing edge cases without rewriting Stripe integration twice
We spend week 1 on those decisions. Not coding. A 90-minute discovery call + a written spec doc (we call it a "build brief") that locks scope, data model, and acceptance criteria. If you skip this, AI just helps you build the wrong thing faster.
The Stack We Actually Use (And Why)
No points for creativity here. Boring stacks ship fast.
| Layer | Choice | Reason |
|---|---|---|
| Frontend | Next.js 14+ (App Router) + TypeScript + Tailwind | Vercel deploys in minutes, AI knows this stack cold, component libraries (shadcn/ui) are mature |
| Backend | Next.js API routes or Hono on Cloudflare Workers | Zero infra ops, scales to zero cost, edge latency for SEA users |
| Database | Postgres (Neon or Supabase) | Real SQL, branching for preview envs, row-level security for multi-tenancy later |
| Auth | Clerk or NextAuth v5 | Battle-tested, handles orgs/teams/SSO when you need it |
| Payments | Stripe Billing + Webhooks | Don't roll your own. Ever. |
| AI SDK | Vercel AI SDK | Streaming, tool calling, evals — built for this stack |
| Observability | Sentry + PostHog | Errors + product analytics in one dashboard |
Estimate: This stack cuts 2–3 weeks off a traditional Rails/Laravel + React + AWS setup. Not because it's "modern" — because the AI training data is dense with patterns for it, so generated code compiles on first try 80%+ of the time.
The AI-Assisted Workflow (Week by Week)
Week 1: Spec & Scaffold
- Human: Write the build brief (features, user flows, data model, acceptance criteria)
- AI: Generate Prisma schema, API route stubs, auth config, project structure
- Output: Repo that runs
pnpm devwith working auth + empty pages
Week 2–3: Core Loops
- Human: Prioritize the one workflow that delivers value (e.g., "user uploads CSV → gets scored report → downloads PDF")
- AI: Write the service layer, background jobs (Inngest or Trigger.dev), UI components
- Human: Review every PR. AI hallucinates business logic. It invents fields that don't exist. It forgets idempotency keys.
Week 4: Billing & Onboarding
- Stripe Checkout portal, subscription webhooks, usage metering, trial logic
- This is where most DIY builds die. We have a tested Stripe module we drop in — saves ~1 week.
Week 5: Hardening
- E2E tests (Playwright) for the happy path
- Load test the background job queue (k6, 100 concurrent)
- Fix the 3 things that always break: file upload timeouts, webhook retries, timezone bugs
Week 6: Launch Prep
- Custom domain, SSL, CSP headers
- SEO basics: sitemap, robots.txt, OG tags, JSON-LD for SaaS schema
- If you care about AEO/GEO later, this is when we structure content for answer engines — run a quick readiness check to see what's missing.
Where AI Actually Saves Time (And Where It Doesn't)
| Task | AI Speedup | Human Still Required |
|---|---|---|
| Boilerplate (routes, types, configs) | 10x | Review & integrate |
| UI components (forms, tables, modals) | 5x | Design decisions, accessibility |
| Database migrations | 3x | Schema design, index strategy |
| Business logic (scoring, workflows) | 1.5x | All of it — AI guesses wrong |
| Testing (unit + e2e) | 3x | Test strategy, flaky test debugging |
| DevOps / CI/CD | 2x | Secrets management, env parity |
| Debugging production issues | 1x | You own the pager |
Bottom line: AI turns a 12-week build into 6–8 weeks if the human steering it has senior-level judgment. Junior dev + AI = confident bugs. Senior dev + AI = velocity.
What "Fast" Actually Costs
We price fixed-scope MVPs at $12K–$25K depending on complexity — see the full breakdown. That includes:
- Build brief + spec (week 1)
- Custom code on the stack above (not a template)
- Stripe billing + onboarding flow
- Deploy to your domain, docs, 30-day warranty
What's not included: Mobile apps, native integrations (Salesforce, HubSpot), AI model fine-tuning, compliance (SOC2, HIPAA). If you need those, we'll tell you upfront — they're phase 2.
The Trap: "I'll Just Use v0 / Bolt / Cursor Myself"
You can. Plenty of founders do. Three questions to answer honestly:
- Can you read a Prisma schema and spot a missing index? If no, you'll ship a time bomb.
- Do you know what a Stripe webhook retry storm looks like? If no, you'll lose revenue silently.
- Will you actually write the Playwright tests? If no, you'll break checkout on the next deploy.
If you answered "no" to any — hire the judgment, not just the keystrokes. That's what TF Globe sells. The AI is just our power tool.
Next Step
You've got the outline. Now you need a build brief.
See how we did it → request a quote and we'll send you the brief template + a 30-min call to scope your MVP. No retainer, no discovery fee — just a fixed price and timeline before you commit.
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