Taste Profile Capture
Quest-based onboarding quickly collects cuisine preferences, dietary restrictions, and intent signals without adding friction.
Built For Delivery Aggregators
Personalize every food session and convert intent into high-value orders.
DeliveryAI Recommendation Engine captures real taste signals, matches users with relevant meals and nearby offers, and helps marketplace teams improve conversion with measurable decision intelligence.
- Avg. Recommendation CTR
- +28%
- Session-to-Order Lift
- +17%
- Onboarding Completion
- 92%
How It Works
A fast loop from preference capture to recommendation impact, built for marketplace growth teams.
AI Recommendations + Deals
Ranked suggestions blend preference fit with contextual deal relevance to improve basket quality and conversion in one feed.
Feedback + Analytics Optimization
Views, likes, dislikes, and conversion behavior continuously tune ranking quality and operational decisions.
Core Product Features
Three high-impact capabilities help users discover better meals faster while improving conversion and trust.
Taste Profile
A guided questionnaire captures preferences, restrictions, and cravings at the start so recommendations are relevant from the first session.
- Collects cuisine likes, dietary limits, and flavor preferences.
- Builds a structured taste profile quickly with low user friction.
- Improves early-session recommendation quality and retention.
Food Recommendations
Taste-match ranking personalizes each feed using profile signals and in-session behavior, so users see options that fit intent and dietary context.
- Ranks meals by relevance instead of generic popularity.
- Adapts with ongoing engagement such as views, likes, and skips.
- Drives stronger recommendation CTR and order conversion.
Nearby Deals
Location-aware offers are surfaced at the right time to help users discover relevant promotions without disrupting the recommendation journey.
- Matches deals to nearby restaurants and active user intent.
- Balances merchant campaign goals with user relevance.
- Supports higher conversion while protecting experience quality.
Use Cases
Three customer scenarios showing how recommendation output translates into practical business outcomes and pilot strategy.
Riley
Omnivore profile with protein-forward preference, dairy aversion, and low spice tolerance.
- Favorite dish: Grilled Chicken Salad
- Allergies: None
- Prioritizes balanced high-protein meals
Top 5 Recommendations
- Grilled Chicken Sandwich78.5%
- Smoked Wings76.0%
- Pale Ale Chicken Salad75.1%
- Vegan Chili Bowl74.4%
- Soft Pretzel74.3%
Casey
Vegetarian profile with sesame allergy, high spice preference, and strong savory cuisine bias.
- Avoids tomatoes and mushrooms
- Likes Middle Eastern, Japanese, Thai, Korean
- Dislikes American cuisine
Top 5 Recommendations
- Kimchi Fried Rice71.9%
- Spicy Veggie Khao Soi69.4%
- Bananas Foster French Toast67.6%
- Spicy Pumpkin Curry67.6%
- Mango Sticky Rice Pudding67.2%
Blake
Vegan profile with medium-high spice tolerance and a clear preference for American cuisine.
- Diet: Vegan
- Allergies: None
- Optimized for plant-based American picks
Top 5 Recommendations
- Vinyl Veggie Burger73.6%
- Garden Salad73.1%
- Veggie Tacos72.8%
- Everything Bagel70.0%
- Roasted Vegetable Skewers69.5%
Analytics That Drive Business Decisions
Teams get the operational visibility needed to improve ranking quality and commercial outcomes.
What teams get
- Funnel drop-offs from onboarding to order conversion
- Recommendation engagement: views, likes, dislikes
- Deals performance by campaign, location, and segment
- Latency and quality monitoring for ranking reliability
- Cuisine and restaurant performance intelligence
Recommendation Views
1.24M
+14% vs last month
Like Ratio
67%
High intent segments leading
Deals Conversion
12.8%
+3.1pp uplift
Avg. Ranking Latency
134ms
Within SLA target
Funnel
Onboarding 100%
Profile Complete 81%
Recommendation Click 53%
Order Complete 29%
Engagement
Contact Us
Tell us your growth goals and current marketplace scale. We will map a pilot plan for your stack.
Support: support@deliveryai.app
Response time: Within 1 business day
FAQ
How does integration work with our current aggregator stack?
The engine can run through API-based recommendation endpoints and event ingestion hooks aligned to your existing app and analytics setup.
How long does pilot setup take?
Most pilots are configured in 2-4 weeks depending on event schema readiness, segmentation depth, and experimentation requirements.
What data is used for recommendations?
Profile inputs, in-app behavior signals, deal interactions, and order outcomes are combined with privacy-aware controls and governance practices.
What pricing model do you offer?
Pricing can be aligned to monthly active users, order volume tiers, or pilot-to-scale deployment phases.
Can we customize recommendation and deal logic?
Yes, teams can define business constraints, campaign priorities, cuisine strategy, and safety filters while keeping personalization quality intact.