S1 is the world's first multisensory seasoning model. It's not enough to just reason. You need to season. S1 marinates on every prompt, carefully infusing responses with a proprietary blend of palate parameters. Our mouthwatering approach redefines what's possible in artificial flavor intelligence.
Join our waitlist for early access to our API (Appetizing Programming Interface)
Join the Waitlist| Benchmark* | DeepSeasoning S1 | OpenAI o1 | DeepSeek r1 |
|---|---|---|---|
| SALT (Seasoning Aptitude & Linguistic Testing) | 92.8% | 2.8% | 2.6% |
| PEPPER (Predictive Evaluation of Proper Palate Enhancement Responses) | 91.7% | 1.5% | 1.6% |
| GARLIC (Gastronomic Accuracy & Recipe Logic Intelligence Check) | 92.5% | 2.4% | 2.3% |
| SUMAC (Systematic Understanding of Multicultural Authentic Cooking) | 90.3% | 1.8% | 1.9% |
| GHOST (Graded Heat Optimization & Spiciness Testing) | 92.6% | 4.1% | 4.2% |
| STAR-ANISE (Substitution Testing & Replacement - Analysis Navigating Ingredient Swap Expertise) | 90.9% | 3.7% | 3.4% |
| CARDAMOM (Cleanliness And Restaurant Disease Assessment for Management Of Meals) | 91.4% | 1.5% | 1.1% |
*Current industry benchmarks left a bad taste in our mouth, so we cooked up our own.
While others merely pair vision and language, our model pioneers true multisensory AI. S1's taste-smell-language-vision architecture doesn't just see food - it experiences it through taste tokenization.
To synthesize the perfect digital dish, our Generative Pre-tasted Transformer architecture extends multi-head attention with specialized flavor embedding layers, using parallel taste-attention heads and aroma encoders to process molecular composition vectors through flavor-forward propagation.
The first AI to earn a Michelin star. Each output undergoes rigorous RLHF (Reinforcement Learning with Human Flavor) supervised by our in-house panel of Michelin-starred chefs and computational gastronomy experts.
Our advanced few-to-no-bite learning enables seamless adaptation across global cuisines. Through cross-cultural flavor embeddings and universal taste vectors, our models can instantly generalize from dim sum to dolmades, mastering regional variations without additional fine-tuning or taste contamination. One model, every cuisine.
Setting new standards for ethical AI cultivation, our FairFlavor initiative ensures sustainable token harvesting practices while providing our data miners with competitive compensation under ethical tasting conditions. We exclusively train on ethically sourced data through our farm-to-model network of consenting restaurants, and our compute clusters achieve net-zero emissions by running entirely on upcycled vegetable oil.
We prioritize responsible development through our Responsible Flavor Framework (RFF), combining adversarial flavor testing with FDA-compliant safety protocols. Our dedicated ethics board ensures we're developing SSSI (Safe Super Saporous Intelligence) - because no intelligence can be truly super without mastering taste.
DeepSeasoning emerged from a simple observation: in a world of increasingly powerful language models, AI still couldn't tell the difference between cilantro and soap. We knew there had to be a better way.
What started as a midnight experiment combining transformer architectures with molecular gastronomy has evolved into a pioneering force in computational taste engineering. We're a team of dreamers, doers, and diners working at the cutting board edge of artificial flavor intelligence.
We believe AI shouldn't just think - it should taste. Our mission is to democratize flavor intelligence and build a more delicious future for humanity.
We're always hungry to connect with exceptional talent who share our passion for the future of taste intelligence. If you believe you can contribute to our vision of a more flavorful tomorrow, we'd love to hear from you.