01Planned
Intent Memory
Remember what a diner is looking for across interactions, combining expressed preferences with relevant context to improve future recommendations.
Resactly, a product of OKAO Labs
Resactly
Resactly helps diners choose where and what to eat. Our long-term vision connects those decisions with restaurant intelligence—helping operators understand emerging demand, improve menus, and reach diners through genuinely relevant experiences.
The consumer product is live and Resactly is pre-revenue. We’re recruiting restaurant design partners to test the proposed intelligence tools, pricing and packaging.
The problem
A purchase is the end of a decision, not the decision itself.
Transaction data reveals what someone bought. It gives an incomplete picture of what they considered beforehand — the occasion, the company, the constraints, the dishes they weighed and passed over. Resactly is built at the point where those considerations are still being expressed.
What diners can use today.
- Consumer restaurant discovery and recommendations.
- Group dining sessions with shared discovery, voting, chat, and saved decisions.
- Restaurant search and recommendation tools through ChatGPT/MCP integrations.
- First-party product interaction capture.
- Menu extraction and structured dish data.
- A foundation for restaurant onboarding and profile management.
How the foundation compounds
Occasion & company
What the evening is for, and who is at the table.
Constraint
Distance, timing, budget, dietary limits.
Appetite
Familiar comfort, or something unfamiliar.
Dish-level interest
Which specific dishes are explored, saved, or clicked.
Diners contribute preference and consideration signals by using discovery experiences that help them choose. Searches, comparisons, saves, and recommendation clicks show interest; they do not by themselves prove a visit or purchase.
Structured menus and existing restaurant administration tools contribute supply information. Over time, future Studio usage could add more context. Our intended compounding advantage is to use both sides to improve diner recommendations and restaurant decision support; that advantage has not yet been proven.
Early evidence
Reporting period: August 16–September 14, 2026 UTC.
- 788
- Devices took a deliberate dining action
- 50.5%
- Of engaged devices took two or more deliberate actions
Up 37.5% from 573 in the previous 30 days, using the same 12 consistently instrumented events.
Measured using strict gesture deduplication across a broader cohort of 873 engaged devices.
Devices are not unique people. The growth and action-depth metrics use different cohorts. Growth occurred alongside increased paid acquisition; repeat usage remains an early-stage challenge.
We record displayed recommendations and subsequent digital interactions to support future evaluation. Predictive lift has not yet been demonstrated.
Clicks, saves, directions requests, and reservation referrals are observable product actions. They are not equivalent to verified arrivals, bookings, purchases, or satisfying meals. Connecting expressed intent, subsequent choices, and reliably measured dining outcomes remains long-term vision.
Resactly — the intended loop
01–03 Existing foundations04 Product actions observable05–06 Planned connections
- 01
Intent
Available today
- 02
Context & menu data
Available today
- 03
Recommendation
Available today; lift not yet demonstrated
- 04
Choice
Product actions observable today
- 05
Outcome
Reliable measurement planned
- 06↺ 01
Learning
Planned; improvement not yet demonstrated
- THE LOOP
The complete loop is the intended architecture. Its outcome and learning connections are planned, not operating today.
Product horizon
Next milestones and long-term vision.
Next milestones
- Evaluate recommendation performance against a defined baseline.
- Recruit restaurant design partners to test the proposed intelligence tools, pricing and packaging.
- Measure repeat usage and recommendation engagement.
Long-term vision
- Intent memory across relevant diner interactions.
- Aggregate demand intelligence and dish-gap analysis for restaurants.
- Restaurant Studio for menu analysis and development.
- Opt-in contextual proximity recommendations.
- Learning from choices and reliably measured dining outcomes.
Long-term vision
Where we’re going
From a useful diner product to a hospitality intelligence platform.
These are documented concepts in our long-term vision. They are planned—not launched, commercially validated, or necessarily in active implementation.
02Planned
Demand Intelligence
Help restaurants understand what participating diners search for, compare, and consider—including interest that does not lead to an observed action on Resactly.
Planned views include dish-gap analysis, aggregate local demand patterns, and comparison insights. A missing observed action is not evidence of a lost customer.
03Planned
Restaurant Studio
An AI advisory experience for analyzing an existing menu or developing a new one, with recommendations grounded in menu structure, local context, and Resactly demand signals where sufficient evidence exists.
Restaurant Studio is planned. It is distinct from the restaurant administration and menu-management foundations available today.
04Planned experience
Contextual Proximity Recommendations
You searched for crab sushi earlier in the week. Later, with your permission, Resactly could alert you when you’re near an open restaurant whose menu includes a matching dish.
This planned experience depends on opt-in location access, relevant menu coverage, and notification controls. It would apply to participating users, not track non-users.
05Planned
Learning from Outcomes
Our goal is to connect expressed intent, recommendations, subsequent choices, and—where reliably measured—dining outcomes, using that evidence to improve the system.
A click, save, directions request, or reservation referral is a product action—not proof of an arrival, booking, purchase, or satisfying meal.
Long-term visionProposed restaurant business model
Restaurant intelligence subscriptions
Recurring access to demand, audience and menu views.
Premium restaurant analysis
Deeper one-off analysis for a specific venue or menu.
Hospitality intelligence APIs
Programmatic access for groups and platform partners.
Resactly is pre-revenue. These proposed lines are not commercially validated. We’re recruiting restaurant design partners to test the proposed intelligence tools, pricing and packaging.
Founder
Fisayo Oluwadiya — Founder & CEO
Fisayo is a principal software engineer building Frankenstein and leading OKAO Labs' energy infrastructure proofs of concept. He built Resactly across product development, data infrastructure, recommendation systems, AI integrations, and restaurant intelligence, and brings that experience to both.
The consumer product and its underlying technical foundation have already been built. Predictive lift has not yet been demonstrated. We’re recruiting restaurant design partners to test the proposed intelligence tools, pricing and packaging.
LinkedIn →Investors
We're speaking with pre-seed investors aligned with Resactly's diner-intent and hospitality-intelligence thesis.
Investor inquiries →Resactly hospitality intent deck →Restaurants
We’re recruiting restaurant design partners to test the proposed intelligence tools, pricing and packaging, and to help shape the product.
Become a restaurant design partner →resactly@okaolabs.co