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AI-First MVP · Startup

AI-First MVP for a B2B Marketplace

AI-assisted discovery, prototype flows, and a functional MVP with onboarding, listings, messaging, and AI-assisted matching.

AI-FIRST MVP
8 weeks
concept to live MVP
12
design-partner companies onboarded
1
production codebase — no throwaway
The challenge

What the client was facing

Two industry founders had a clear thesis for a B2B marketplace but no engineering team. They needed a real product to test with design partners — not a clickable prototype, an actual working system — and they needed it in weeks, not quarters.

What we built

The solution

  • AI-assisted discovery and architecture in week one
  • Working onboarding, listings, messaging, and admin flows by week six
  • AI-assisted supplier-buyer matching as the core differentiator
  • Built for evolution: clean modules, real database, ready for the next 12 months
In production

What it looks like

Illustrative screens — actual client UI, branding, and data redacted under NDA.

AI-First MVP for a B2B MarketplaceOverview82%Accuracy3.2kItems12Today4.7ScoreTrend
AI-First MVP for a B2B Marketplace — analytics 1 2 3 4 5 6 7 8class ClaimsExtractor: def __init__(self, llm, schema): self.llm = llm self.schema = schema def extract(self, document): prompt = self.build_prompt(document) raw = self.llm.complete(prompt) return self.schema.validate(raw)AI suggestion ▸
AI-first delivery angle

Why AI-first mattered here

AI-assisted coding compressed the build phase. Senior engineers owned the data model, auth, and matching logic so the MVP could grow into the production product without a rewrite.

Technologies

Stack

Next.js TypeScript Postgres Prisma OpenAI Vercel

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