AI SaaS MVP Build (LLM-Powered Product)

I build AI-native SaaS MVPs where the language model is the core feature, not a bolt-on. The stack is Next.js with Supabase or Postgres, authentication, Stripe billing, and an OpenAI or Anthropic Claude API engine at the centre of the product. I add vector search where your product needs retrieval, a streaming chat or generation UX so responses feel instant, and a multi-tenant architecture so every customer's data stays isolated from day one. Founders in Dubai, Abu Dhabi and Sharjah go from idea to a launched, billable product instead of a demo that never ships.

What separates an AI product from a normal app is cost and reliability, and that is engineered in. I implement usage metering and rate limits, per-tenant token and prompt cost controls, and evaluation harnesses so model quality is measured rather than guessed. That keeps your AED gross margin healthy when usage scales and stops a single power user from burning your API budget. For UAE free-zone and mainland startups, this is the difference between an AI MVP that survives its first 100 paying users and one that goes broke on inference costs.

This is specifically the AI/LLM-native track. My separate SaaS / MVP Build gig covers general subscription software where the value is the workflow. Choose this gig when the model is the product — when you need metering, token cost controls, evals and AI-specific streaming UX that a standard CRUD SaaS build does not include.

How it works

1. 1
   Scope the AI product

   We define the core LLM feature, model choice, tenancy model and the cost/quality targets the MVP must hit.

2. 2
   Build the foundation

   Next.js app, Supabase/Postgres, auth, Stripe billing and the multi-tenant data layer go in first.

3. 3
   Wire the LLM core

   I integrate the OpenAI/Claude engine with streaming UX, vector search, metering, rate limits and cost controls.

4. 4
   Eval, launch & handover

   We run evals on model quality, ship to production, and hand over full code and IP with a runbook.