Case study
Property Ops Intelligence Platform
Hospitality and property operators running mixed portfolios (vacation rentals + long-term units) who need one calm system that thinks like the operator — not another dashboard of unused AI features.
The challenge
Guest issues, owner messages, expenses, and PMS data live in separate tools. Operators carry the mental load: what needs attention today, what was promised last stay, which receipt is unmatched. Generic AI chat forgets context and drafts that sound like robots — or worse, acts without approval.
What we built
We design and ship operations intelligence platforms on Next.js, Supabase, and managed AI: persistent context across guests and owners, proactive “needs attention” queues, explainable automation, and draft-then-approve messaging that carries the brand voice. PMS and webhook work runs in reliable background jobs — never fragile request handlers — with PII kept behind RLS.
Outcomes
- Mental load offloaded: surface what needs attention before anyone asks
- Context that persists across stays, expenses, and owner threads
- AI drafts in the company voice; humans keep guest-facing authority
- Explainable automation operators can trust and audit
Inside the system
Capabilities we ship and train teams to own — not slideware.
Persistent memory
Past guest issues, expenses, and owner context inform every response — nothing is answered from scratch twice.
Proactive attention
Flags unhappy guests, overdue tasks, and unmatched receipts without being prompted.
Draft, don’t overstep
Messages sound like the company. AI drafts; humans approve before anything goes out.
Plain-language ops
Calm UI with one obvious workflow — built for operators, not engineers.
Visual frame
Ops intelligence, not chatbot theater · PMS + webhook jobs with retries · Human-in-the-loop guest messaging
Product screenshots can drop into this case study later. Until then, the story and capabilities above are the proof.
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