TasteRay's Emotional AI doesn't just match preferences. It understands the deeper motivations driving your users' choices across any vertical.
Traditional recommendation engines see patterns. They don't understand context, mood, or the real reasons behind user choices.
New users get generic suggestions that feel impersonal. They bounce before your algorithm can learn.
Collaborative filtering shows "similar users liked this" but can't explain why it matters to this specific user.
Batch-processed models miss real-time signals. Your user's mood changed, but your recs didn't.
TasteRay combines frontier LLMs' reasoning with real-time web grounding to deliver recommendations that feel genuinely personal and explainable.
Pass unstructured user profiles, conversation excerpts, or simple preference lists. TasteRay understands context in any format and identifies emotional drivers.
Every recommendation is grounded with live web data. New releases, updated ratings, current availability - always fresh, always relevant.
Every recommendation comes with human-readable explanations. Tell users exactly why they'll love it, building trust and boosting engagement.
Movies, restaurants, products, travel, jobs, real estate - one unified API handles 25+ verticals with the same simple interface.
A complete recommendation infrastructure that scales from prototype to production.
Real-time search via Exa provides current information - new releases, reviews, availability. No stale data.
Structured arrays or natural language profiles. Pass conversation excerpts, survey responses, or simple lists.
Every recommendation includes why_match, key_factors, and potential_concerns. Build transparent "For You" experiences.
Enable fast inference for time-sensitive applications. Same quality, lower latency for real-time scenarios.
Generate recommendations and explanations in any language. Just set the locale code in your request.
Track requests by vertical, monitor rate limits, and analyze patterns with the built-in usage endpoint.
From entertainment to e-commerce, TasteRay adapts to your domain without custom model training.
One URL. Infinite personalization.
Point TasteRay at any product feed—XML, JSON, CSV, Google Shopping, Shopify, whatever format you have. We detect it automatically, index every product, and keep it synced in real-time.
No configuration. No schema mapping. No manual setup. Your recommendations now draw from your actual inventory. Updated prices. Real availability. Products your customers can actually buy.
Your recommendations get smarter. Automatically.
Send us what your users actually do - views, clicks, purchases, dismissals. We turn that signal into sharper recommendations without you touching a single model.
No ML expertise required. No training pipelines to maintain. Just a simple feedback endpoint that makes every recommendation better than the last.
TypeScript. Python. Whatever you ship with.
Type-safe clients that make integration a one-liner. Full autocomplete, proper error handling, and zero boilerplate for recommendations and feedback.
Plus framework starters for Next.js and Cloudflare Workers. Drop in a component, get a "For You" feed with SSR and caching built in.
One user. Infinite context.
Persistent taste profiles that grow with every interaction. Explicit preferences merge with implicit signals to build a rich understanding of each user over time.
The longer someone uses your product, the more personal their recommendations become. No cold starts. No amnesia. Just recommendations that feel like they know you.
See exactly what works.
Built-in A/B testing that tells you whether TasteRay is actually moving the needle. CTR, conversion, engagement - tracked automatically with statistical significance.
Tag requests with experiment IDs. Compare variants. Get clear answers without a data science degree. Because "it feels better" isn't a business case.
A simple REST API with comprehensive documentation. No ML expertise required.
// Get personalized recommendations const response = await fetch('https://api.tasteray.com/v1/recommend', { method: 'POST', headers: { 'Content-Type': 'application/json', 'X-API-Key': process.env.TASTERAY_KEY }, body: JSON.stringify({ vertical: 'products', context: { profile: 'Looking for noise-cancelling earbuds for daily commute.', constraints: { budget_max: 300 } }, options: { count: 5 } }) }); const { recommendations } = await response.json(); // recommendations[0].item.name = "Sony WF-1000XM5" // recommendations[0].explanation.why_match = "..."
# Get personalized recommendations import requests import os response = requests.post( 'https://api.tasteray.com/v1/recommend', headers={ 'Content-Type': 'application/json', 'X-API-Key': os.environ['TASTERAY_KEY'] }, json={ 'vertical': 'products', 'context': { 'profile': 'Looking for noise-cancelling earbuds for daily commute.', 'constraints': {'budget_max': 300} }, 'options': {'count': 5} } ) recs = response.json()['recommendations'] # recs[0]['item']['name'] = "Sony WF-1000XM5"
# Get personalized recommendations curl -X POST https://api.tasteray.com/v1/recommend -H "Content-Type: application/json" -H "X-API-Key: $TASTERAY_KEY" -d '{ "vertical": "products", "context": { "profile": "Looking for noise-cancelling earbuds for daily commute.", "constraints": {"budget_max": 300} }, "options": {"count": 5} }' # Response: {"recommendations": [...]}
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