AI SaaS MVP for a property-management startup

A two-person proptech startup had a validated problem (property managers drowning in tenant messages and maintenance requests) and a deck, but no product. Through our ai-saas-product-development practice we designed, built and launched an MVP in twelve weeks: multi-tenant SaaS with an AI assistant that reads tenant messages, triages maintenance issues, drafts replies and schedules contractors. Six agencies were paying pilots within the first quarter.

Challenge

The founders needed something real enough for agencies to pay for and stable enough to demo to investors, on a seed budget and a hard deadline. They also needed the AI to be genuinely useful rather than a chat window bolted on; the value was in reading a photo of a leaking tap at 11 p.m., classifying urgency, and drafting the right message to tenant and plumber. Multi-tenancy, billing, email and SMS ingestion and a clean data model all had to exist from day one.

What we built

Product scoping in week one: a ranked feature list, three user journeys and an explicit "not in the MVP" list the founders could show investors.
A multi-tenant Nuxt and Node application on Supabase and Vercel with role-based access, audit logs and Stripe billing (ai-integration).
Message ingestion from email, SMS and a tenant web portal, with an LLM layer (Claude for drafting, GPT-4o for image understanding) that classifies category and urgency, extracts property and unit, and drafts replies for the manager to approve, or sends automatically for low-risk categories.
A contractor-scheduling flow that proposes slots and confirms by SMS via Twilio, and an assistant view (ai-chatbots) where managers ask questions about any property's history.
Analytics on triage accuracy and manager overrides from launch, feeding a fortnightly prompt-and-rules review.

Results

The product launched to its first paying pilot in week twelve and had six agencies onboarded by the end of the first quarter. Around 70% of tenant messages are triaged without manager input , and pilot managers reported the after-hours drafting as the feature they would not give back. The startup used the pilot metrics in its next funding conversation and hired its first engineer, who took over the codebase after a two-week paired handover.

What made the difference

We acted as the founders' technical co-founder for a quarter: saying no to features, choosing boring infrastructure, and instrumenting AI accuracy from day one so the pilots produced evidence, not anecdotes.