restaurants · guest data & ai agents

turn anonymous diners into regulars you can name.

your guests leave a trail every visit: the reservation, the check, the wifi login, the instagram tag. most restaurants save none of it. hi.os stitches it into one record per guest, then puts agents to work on it: win-back, birthdays, reviews and audiences built from people who already paid. every agent stops for your yes.

built by a working dc nightlife operator. installed and running in a live venue today, with a measured before/after on the client's own data. read the case study

01 · capture every diner

one record per guest, not five half-lists.

who your guest is lives in the reservation system, the pos, the wifi portal and your instagram, and gets saved in none of them together. hi.os pulls what each one can export into one guest record you own, on your logins.

  • reservations, pos, wifi & instagramturned into real profiles instead of four disconnected lists.
  • nobody types a number twicemost of it flows in on its own; what doesn't gets fed in once a week.
02 · know your regulars

know who matters before they sit down.

ask the guest record the questions you can't answer today: who are my top spenders this year? who are my most frequent diners? who has a birthday this month? what is one guest actually worth to me?

  • top spenders & most frequent dinerssurfaced and ranked, so you know who your regulars are.
  • real lifetime valuewhat a regular is worth to you, worked out from your own sales rather than a benchmark.
03 · win back regulars

notice who stopped coming, and ask them back.

a regular who drifts away rarely announces it. the win-back agent finds guests who haven't been back in a while, writes the text, builds the segment, and waits for your yes. the birthday offer and the review request work the same way.

  • win-back & viplapsed regulars found, message drafted, awaiting your approval.
  • birthdays & reviewsthe right offer at the right moment, and reviews asked for after a good night.
04 · get found by more of them

find more people like the ones who already pay.

an email, phone or instagram handle is the key to meta. with your approval, paying guests become an audience of people who already paid, then widened to people like them. locally, the same record feeds google, local search and reviews.

  • paid-guest audiencesbuilt from real diners, not interests, and launched only when you approve.
  • google, local seo & reviewsso the next regular can find you.
proof

the same engine, already running.

our live install is in nightlife, where the guest record, deposits and promoter tracking run today. restaurants get the same engine, tuned to covers and visit frequency instead of tables.

38 → 682unique buyers captured at a live venue
$9,239in table deposits through Posh in the first four weeks, 100% captured with name, email & phone
72Kguest profiles in the database
6 yrsoperator-built, in-market

client figures come from the venue's own ticketing data: buyers compare day 0 with the first measured window; the four-week deposit total covers jul 31 – aug 23, 2026, across the client's two rooms. operator figures come from the founder's own events. the full case study

questions owners ask

restaurant faq

Will it work with our reservation system and POS?

We map what your reservation and POS systems can export during the audit week and tell you exactly what we can capture before you commit to anything.

Does this replace our reservation platform?

No. It works on top of the systems you already pay for and on your logins. The guest record sits across them; your team keeps using the tools they know.

Will our guests get spammed?

No. Every win-back text, birthday offer and campaign is drafted and waits for your approval. Nothing sends, spends or posts without a person saying yes.

Who owns the guest data?

You do. The guest record lives on your logins, and we operate the agents on it. Matching guests to Meta for audiences is a step you approve; nothing is shared or sent without your yes.

Is this built for nightclubs rather than restaurants?

The engine is the same. Our live install is in nightlife, where tables and deposits drive the night. For a restaurant it is tuned to covers, visit frequency and win-back instead.

What does it cost?

Every install is shaped around your restaurant, so it is quoted after the audit. For scale, boutique agencies run $3,000–8,000/mo per venue.

30 minutes

see what your diners are already telling you.

we'll look at where guest data leaks between your reservations, pos and socials today, and what it's worth. if one person on your team already runs this well, we'll tell you that on the call.

rather write it yourself? email samer@gethios.com ·

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