Real food behavior. Reliable decisions

AI creates confidence. Evidence earns trust with statistically significant data.

We unite our consumer panel with market trackers and food-trained AI agents to turn what consumers order, cook, and eat, and why, into statistically meaningful, ready-to-act evidence.

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How Tastewise turns observed behavior into curated consumer intelligence across three layers. Statistical validation Noise removal Bias reduction Signal weighting Panel construction Audiences Occasions Ingredients &dishes Purchasedrivers Geographies Categories Trends 3 Consumer intelligence
Curated, statistically validated intelligence you can act on.
 2 Intelligence orchestration
We clean, validate and enrich signals, removing noise and bias to ensure statistical significance.
 1 Observed behavior
1B+ real-world signals every month from how people cook, search, shop, dine and engage.
Click a layer above to explore it
1 Observed behavior 1B+ real-world signals every month from how people cook, search, shop, dine and engage.
2 Intelligence orchestration We clean, validate and enrich signals, removing noise and bias to ensure statistical significance.
3 Consumer intelligence Curated, statistically validated intelligence you can act on.
Layer 1 · Observed behavior ×
Curated, statistically validated intelligence

The decision-ready layer: every output is traceable back to the observed behavior beneath it.

AudiencesOccasionsIngredients & dishesPurchase driversGeographiesCategoriesTrends
Questions it helps answer
→Which audiences are driving demand for this ingredient, and where?
→What occasions should we build this concept around?
→Which trends are ready to scale into a new product line?
Layer 2 · Intelligence orchestration ×
Cleaned, validated and enriched

We remove noise and bias and weight every signal so findings are statistically significant.

Statistical validationNoise removalBias reductionSignal weightingPanel construction
Questions it helps answer
→Is this signal statistically significant, or just noise?
→How confident can we be that this trend is real?
→Which sources agree, and how is each one weighted?
Layer 3 · Consumer intelligence ×
1B+ real-world signals every month

The raw foundation: how people actually cook, search, shop, dine and engage.

CookingSearchShoppingDiningEngagement
Questions it helps answer
→What are people actually cooking and searching for right now?
→How is this ingredient showing up on menus vs. in retail?
→Where is early demand emerging before it hits the mainstream?

The right data

More data is not the point. The right data is

The food and beverage industry generates billions of signals every day. Tastewise organizes all of it into four independent data streams across 50+ markets, and processes it within the unique context of food and beverage, so you get bespoke evidence you can stand behind.

Consumer panel

Tastewise’s proprietary consumer panel: a structured panel of real consumer voices on what people eat, cook at home, and talk about. Organized by demographic, occasion, and behavior so every signal has context.

Not a social media scrape. Every signal has a source.

Foodservice tracker

Hundreds of thousands of menus, operators, and LTOs across 50+ markets. What is actually on the plate right now.

Not what chefs are speculating about.

E-retail tracker

Shelf data, pricing, and best sellers across e-retail. What consumers are actually buying.

Not just searching for or talking about.

Non-commercial channels

C-store, K-12, colleges, hotels. When something appears here, it is no longer a niche signal.

It is mainstream.

See the consumer evidence behind your next big category decision

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Inside our intelligence orchestration

Every finding arrives with a confidence score you can defend

A validation pipeline cuts residual bias from 86% to under 5% before anything reaches your team.

86%
Bias in the raw signal
<5%
After calibration & peer review
Data foundation & modeling

Behavioral models built on clean, structured data

Billions of signals, curated into one taxonomy, then interpreted by food-trained agents to explain why consumers act.

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Confidence scoring

Calibrated, governed, defensible

Calibrated and peer-reviewed, with residual bias reduced to under 5%.

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Behavioral models built on clean, structured data

Billions of food and beverage behavior signals, covering how people actually eat, shop, and decide, curated and structured into one taxonomy, then interpreted by food-trained agents to explain why consumers act and how behaviors evolve.

  • Consumer panel:: real observed social, digital, and in-home behavior
  • Market trackers:: menus, operators, retail, and e-retail across 50+ markets
  • One taxonomy:: “oat milk” = “oatmilk” = “oat-based latte” counts as one thing
  • Food-trained agents:: intent, context, and behavior trajectory, with analyst oversight
From raw signal to behavioral model
“I’ve been putting hot honey on everything lately.”
Interpreted: Intent: positive adoption · Category: condiments · Signal: early-to-emerging lifecycle stage.
Built on billions of continuously updated signals across 50+ markets, cleaned, structured, and continuously refined with expert food and beverage analyst oversight.

Calibrated, governed, defensible

Audiences are continuously monitored and reviewed to maintain accuracy, ensuring reliable definitions you can trust. Residual bias is reduced from up to 86% to under 5% after calibration. Based on peer-reviewed methodology published with researchers affiliated with EPFL and Stanford, 2025.

Example confidence output
Hot honey: top innovation white space in pizza
Adoption is accelerating across consumers and menus, crossing over from condiments into mainstream formats.
Data reliability Confidence: high
Panel 93% Menu 85% Retail 80% Bias 95% Geography 88% Sample 90%
Confirmed across sources
Consumer ✓
Menus ✓
Retail ✓
All three sources agree, so it is trending.

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Start making decisions based on real behavior

See how our consumer panel, market trackers, and food-trained agents turn billions of signals into evidence you can stand behind.

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