Recently Magic AI closed a $10 million seed round to bring real-world AI personalization to restaurants and hospitality. Mario Carbone’s Major Food Group is lead investor.

This is a big deal that should give businesses pause. Some context:

Magic AI’s flagship product Loyalist unifies fragmented data sources like reservations, POS (point-of-sale), private events, guest history, and social mentions into a live CRM that staff can actually act on.

It surfaces actionable guest-preferences in real time (for example: “this guest always sits in the booth by the window,” “this guest loves chocolate cake,” “this guest is vegetarian but will try seafood if recommended”).

That allows staff to tailor seating, menu suggestions, and upsell offers.

Magic AI allows restaurants to scale what used to be one-to-one high-touch service to many guests. This means regulars get that personal recognition without manual tracking.

It’s not just “nice to have” – it’s tied to measurable revenue. Magic cites they manage tens of millions of guest interactions annually, over $2 billion in guest spend (in the restaurant vertical alone) and revenue growing more than 10x in the past year.

Whether you’re a foodie or not, here’s why this matters to your business:

Unified guest/customer data is table stakes

Whether you sell SaaS, services or hospitality, you need a reliable single source of truth combining contacts, accounts, transactions and engagements for true, up-to-the-minute personalization.

The same logic applies in B2B: deals, product usage, support tickets, marketing engagement.

Personalization requires actionable insight, not just data collection

It’s not enough to know “this guest ordered veal marsala.” You need workflows and triggers like “because this guest ordered veal marsala and visited 3 times in 6 months, send them A offer or seat them in zone B.”

You can use HubSpot workflows + dynamic content + AI Assistants to execute those kinds of personalized sequences.

Make the customer feel known, not marketed to

One of Magic’s differentiators is the “feels personal at scale” concept — meaning guests feel recognized in the moment.

That means your messaging, your website content, and your outreach must reflect individual preference, not generic segments.

In HubSpot, consider dynamic website modules, segmentation beyond just “industry” or “persona”, and email content that references past behaviour.

Operationalize the frontline experience

For restaurants, this means hosts, servers, and kitchen staff have the context to act.

For your business, it might mean your sales rep, CSM, or support agent has the context (usage signals, past conversations, propensity model) at the moment they talk to the customer.

Use HubSpot’s contact timeline, custom objects, and connect to other systems so have full visibility in the moment.

Tie personalization to revenue metrics, then test and iterate

Magic AI is growing fast because their personalization system is tied to repeat visits, higher ticket value, and increased loyalty.

For you, pick concrete metrics (conversion rate, upsell rate, deal size, retention). Use HubSpot to build A/B or cohort experiments: “Visitors who saw personalized hero vs generic”, “Emails referencing past product usage vs standard,” etc.

Plan for privacy, fallback logic, and inclusive UX

The more you personalize, the more you need to manage consent, data quality, and fallback logic. If you don’t have rich data, your fallback should still feel respectful.

In a digital business, ensure default “guest” flows aren’t broken when cookie signals or external integrations are missing.

Start small, scale fast

Restaurants piloted personalization at select locations or for top guests before rolling out across the chain.

In your case, pick a key segment (e.g., existing customers with 6+ months usage), build a personalization workflow, measure impact, then scale using HubSpot ecosystems and integrations.

For teams using HubSpot and focused on revenue operations, here’s a clear blueprint:

1. Clean, unified customer data
2. Real-time context and actionable triggers
3. Personalized outreach & experience across channels
4. Metrics connected to revenue outcomes
5. Scalable model with frontline adoption and governance