The menu
becomes a
conversation.
A new way to choose.
An AI sales layer for restaurants.
A restaurant intelligence business to build.
THE NEXT CHAPTER IS COMMERCIAL.
What if the menu
understood
the guest?
“We’re four people.
We want to share.
Our budget is 400 lei.”
That is how a person thinks about dinner. Our ambition is a menu that can respond in the same language, with relevant choices from the restaurant’s offer.
We start with a QR code and conversational recommendations. The long-term goal is to make this an expected part of dining.
VISION · ADOPTION STILL TO PROVEOne question.
Real dishes.
“A vegetarian lunch
under 100 RON.”
The public MVP returned Tabbouleh at 39 RON and Golden Cauliflower at 44 RON, with selection controls.
Observed 18 October 2026. Demo venue, not a customer. Zero customers today.
NOW MVP with conversational dish recommendations.
NEXT Reliable restaurant deployments and confirmed outcome measurement.
Useful to the guest.
Worth paying for.
Recurring software revenue, justified by measurable additional contribution for the restaurant.
additional sales per assisted order to cover the monthly subscription.
Assumes 500 assisted orders/month and a 65% incremental contribution margin. Setup and operator effort add to the hurdle. No uplift measured yet.
Understand
what guests
wanted.
A sale tells you what someone bought.
A conversation can reveal what they could not find.
Our ambition is to connect guest intent with recommendations and confirmed purchase outcomes. That could help restaurants improve menus and help MYAIMENU make better recommendations.
Demand the current menu
doesn’t satisfy.
Repeated requests for an unavailable type of dish could identify an opportunity for operator review. A question is a signal of interest, not proof of willingness to pay.
- 01Capture intentConsistent menu context and agreed interaction events.
- 02Confirm outcomesLink suggestions to purchases where reliable matching exists.
- 03Test improvementCompare recommendations against a meaningful baseline.
- 04Earn expansionUseful results should support retention and more locations.
Planned capability. No established data moat or proven network effect. Appropriate data rights, minimal personal data and reliable attribution are prerequisites. No data-sale revenue in the financial plan.
A narrow start.
A much larger horizon.
A subscription business grows one retained location at a time. Explore the arithmetic if adoption reaches a different scale.
€450 / month × 12 × paying locations.
One modelled subscription revenue stream. Future analytics and other monetization excluded.
This is scale arithmetic, not a forecast, valuation or verified market size. It assumes the same price at every scale and excludes churn, discounts and expansion costs.
At €450/month, €1 billion in ARR would require roughly 185,186 paying locations.
THE WORK IS EARNING THAT DISTRIBUTION.First, earn
the right to scale.
Product demonstration available.
Zero customers today.
Approved menus, reliable deployment and usable measurement.
Paying locations. Retention cohorts and measured restaurant value.
Paying locations. Evidence of repeatable sales and delivery.
Operating targets, not commitments. Interviews and founder sales begin during fundraising. Hiring expands with commercial evidence.
Armando
Goldman Laszlo
Entrepreneur, educator and AI product builder. Experience across product strategy, sales, communities and monetization. Active in financial markets since 2007.
Founder and sole owner of MYAIMENU INC, Delaware. Principal current team member. Leads product and early sales, with senior engineering and onboarding capacity planned first.
Build the
next menu.
From a working product to paid adoption, reliable deployment and measurable restaurant value. We welcome conversations with potential investors, restaurant operators and distribution partners.
Public company overview. Commercial adoption and the long-term data advantage remain to be demonstrated. Detailed financing information is shared directly following an introductory conversation.