Any food and beverage workflow.
One agent away
From whitepapers to recipes, farm sell-in to market wins. Every F&B workflow can run on a Tastewise agent. We build custom agents for the work that’s unique to you.
“How many times have you heard this?”
“Protein has been growing 4% over the last 13 months, so you should list our yogurt protein product.”
- Generic consumer insight
- Category growth number
- Generic recommendation
High-protein yogurt is winning millennial parents at breakfast, but only when it lands the “kid-friendly + clean label” frame.
Real-time signals across recipes, social, foodservice, and eRetail, synthesized into a narrative your buyer can actually use on Monday.
Real agents. Real work.
Already running for our clients
These aren’t demos. These are agents already delivering results across the industry. Pick the one closest to your workflow, or commission a custom one.
Marketing agent
Generates marketing assets, campaign hooks, content angles, claim language, and consumer rationale, grounded in the trends, occasions, and audiences your brand already cares about.
- Pulled Gen Z occasion mentions in snacking 390 ms
- Cross-referenced top-engagement social posts on protein snacking 1.2 s
- Identified leading claim language (“real food”, “fuel”, “no crash”) 1.9 s
- Drafted 3 hook variants · ranked by claim fit 2.6 s
Recommended hook: “Snacks that keep up. No sugar crash. No fake stuff.” Pairs with the real food + fuel claim cluster (+38% engagement vs. category baseline) and the on-the-go occasion (+22% YoY mention growth in Gen Z). Avoid “guilt-free” language — sentiment fatigue index 0.81.
Sales agent
Builds data-backed sell-in narratives and category arguments, surfacing the demand signals, competitor moves, and consumer behavior buyers care about, formatted for the meeting you’re walking into.
- Pulled Walmart-relevant SKU performance + price tiers 440 ms
- Cross-referenced shopper missions + audience claim preferences 1.3 s
- Detected competitor moves in past 90 days 2.0 s
- Drafted sell-in deck outline with named signals 3.1 s
Lead with “functional yogurt growing 3.2× faster than category baseline” in Mass channel. Cite probiotic + protein dual-claim SKUs at $1.79–$2.29 as the highest-velocity tier. Counter likely Walmart objection on margin: present 18% retailer-margin uplift on dual-claim SKUs vs. single-claim. Reference: Chobani Pro launch (Q3) gained 14% incremental shelf in this tier.
Insights agent
Answers business questions in natural language, backed by structured data from social, menus, recipes, retail, and chains, with citations on every claim.
- Pulled menu mentions across 4M+ foodservice locations 487 ms
- Cross-referenced consumer claim signals (functional, diet, ethics) 1.1 s
- Ranked top 6 channels by signal volume 1.8 s
- Cited 14 sources · drafted answer 2.9 s
Plant-based protein mentions are up +18% YoY in Western US QSRs, driven by high-protein and functional claim families. Social signal leads the channel mix (32% share of voice), followed by foodservice (22%) and recipes (17%). The “high-protein” claim alone is up 41% in K–12 menus.
Data agent
Pulls structured data on demand from across the Tastewise food intelligence graph — menus, recipes, social, retail, eRetail, foodservice, and consumer panels — joins it, normalizes it, and returns the slice you actually need with the methodology spelled out.
- Pulled eRetail SKU list with high-protein claim · price ≤ $2.50 410 ms
- Joined 13 weeks of velocity + distribution from US Mass + Grocery 1.2 s
- Normalized units · removed promo distortions 1.9 s
- Returned 247 rows · 8 fields · methodology note 2.6 s
Returned dataset of 247 SKUs across 9 brands. Avg velocity 5.4 units/store/week; top decile at 18.2. Distribution-weighted growth +12.4% vs. prior 13 weeks. Methodology note attached: promo weeks excluded, ACV-weighted, $1.79–$2.49 band shows highest velocity-to-distribution ratio.
Consumer intelligence agent
Turns real consumer behavior and live market signals into a clear, validated answer with decision-ready takeaways — instead of dashboards to explore, you get the story behind the numbers and what to do next.
- Pulled consumer panel + menu + social signals for pizza 520 ms
- Cross-referenced demand vs. operator coverage by occasion 1.4 s
- Validated against statistically weighted consumer behavior 2.1 s
- Drafted answer + decision-ready takeaway 2.9 s
Top whitespace: “hand-held breakfast pizza” — consumer demand growing +47% YoY across morning daypart, but operator coverage under 6%. Backed by panel data (statistically validated, n=3,200) and 18 months of menu tracking. Takeaway: defensible R&D direction with a clear sell-in narrative for QSR breakfast expansion.
Whitespace explorer
Maps unmet consumer needs and category gaps across markets, demographics, and dayparts, surfacing where demand exists today but supply has not caught up.
- Pulled 2 years of dish + ingredient mentions for snacking category 612 ms
- Filtered to Gen Z audience signals in Texas 1.4 s
- Cross-referenced demand vs. operator coverage (whitespace gap) 2.2 s
- Ranked 9 segments by demand-to-supply ratio 3.1 s
Identified 3 uncontested whitespace segments in better-for-you snacking for Gen Z in Texas: spicy Korean-inspired protein bars, frozen yogurt bites with adaptogens, and savory cottage cheese cups. Demand-to-supply gap of 4.2x in segment 1 — operator coverage under 8%.
Flavor forecast
Forecasts which flavors, ingredients, and pairings will move from emerging to mainstream, with trajectory curves, time horizons, and confidence signals per market.
- Pulled 24 months of ingredient mentions across menus, social, recipes 540 ms
- Computed velocity + lifecycle stage per ingredient 1.3 s
- Filtered to beverage category, USA market 1.9 s
- Ranked top 8 by trajectory + confidence 2.6 s
Three flavors are projected to cross into mainstream within 18 months: ube (currently emerging, +127% social growth, lifecycle confidence 84%), yuzu (growing, +63%, confidence 78%), and black sesame (emerging, +89%, confidence 71%). Tropical hibiscus is decelerating — re-evaluate.
Competitor insights
Tracks competitor product launches, claim shifts, menu moves, and positioning changes across CPG, foodservice, and private label, with continuous monitoring.
- Pulled SKU launch feed for top 5 yogurt brands 380ms
- Cross-referenced eRetail prices and platform availability 1.0 s
- Detected claim shifts (functional, sugar, protein) 1.7 s
- Drafted change summary with named launches 2.4 s
Chobani launched a high-protein zero-sugar line at $1.99/cup (Mass + Grocery). Oikos added 15g protein claim across 40% of Pro line — old line at 12g. Two Good dropped Greek positioning in favor of probiotic claim. Watch: Yoplait quiet on claim shifts but expanded freezer SKUs.
Concept agent
Turns category whitespace and trend signals into ranked product concepts, each scored against real consumer demand and competitive landscape.
- Pulled trending ingredients with lifecycle + relevance scores 510 ms
- Cross-referenced Gen Z claim preferences (functional, taste, texture) 1.2 s
- Scored candidate concepts by demand × whitespace × on-trend 2.0 s
- Ranked top 8 concepts · drafted brief 3.2 s
Top concept: “Crispy chickpea crunch with chili-lime dust” — demand score 87, whitespace 4.1x operator coverage, relevance 0.91 to Gen Z. Top 3 concepts share high-protein + bold flavor claim cluster. Avoid “smoothie pouch” angle — saturation index 0.93.
Formulation agent
Recommends reformulation paths for diet, claim, or cost targets, with alternative ingredients ranked by performance, trend trajectory, and consumer acceptance.
- Parsed existing formula · matched ingredient profiles 420 ms
- Pulled candidate alternative ingredients with claim impact 1.1 s
- Filtered by GLP-1 audience signal + sugar-free claim 1.8 s
- Ranked 6 swap paths by claim coverage + cost + acceptance 2.7 s
Top reformulation path: replace brown rice syrup with allulose (sugar-free claim eligible, GLP-1 audience growth +44%, cost impact +$0.12/unit). Add whey protein isolate at 22g/bar — top concept score on consumer acceptance among GLP-1 users.
Recipe agent
Generates trend-grounded recipes for foodservice menus and marketing content, with full ingredient lists, prep steps, dietary claims, and the consumer rationale behind each choice.
- Pulled trending pumpkin pairings across menus + recipes 460 ms
- Cross-referenced LTO performance signals 1.3 s
- Filtered to hand-held formats + dairy-free claim 2.0 s
- Drafted recipe with ingredient list, prep, claim rationale 3.0 s
Pumpkin-Maple Oat Hand Pie with Coconut Glaze — pairs the +32% YoY pumpkin-maple combo with the +51% growth in hand-held formats. Dairy-free via coconut glaze. Average LTO sell-through for similar formats: 78% in Q4. Cost-per-unit fits within $2.40 ceiling.
Strategy agent
Builds multi-step strategic recommendations: category entry, regional expansion, portfolio moves, by reasoning across the full Tastewise data graph and other agents’ outputs.
- Pulled MX market signals across menus, social, retail 580 ms
- Cross-referenced category demand vs. competitor coverage 1.5 s
- Composed inputs from Whitespace + Concept + Competitor agents 2.4 s
- Drafted recommendation with 3 scenarios + risks 3.6 s
Conditional yes — phased entry. MX better-for-you snacking demand growing 24% YoY, but operator coverage already strong in urban centers (Mexico City, Monterrey). Recommended path: enter with 2 SKUs targeting high-protein claim (uncontested), pilot in Monterrey via 3 chain partnerships before scaling to Guadalajara. Top risk: regulatory delay on functional claim labeling (avg 4–6 months).
Custom agents
For workflows unique to your business — Tastewise builds, trains, and deploys custom agents alongside your team. From intake brief to working agent in 2–4 weeks. You own the agent and its outputs; we own the build and the data plumbing underneath it.
We don’t just give you agents. We build, manage, and monitor them on Tastewise data and yours
Transformation excellence partner for the world’s leading F&B brands. Bespoke agents built on the Tastewise corpus combined with your proprietary stack — Salesforce, Circana, Sysco, Telus and others.
Bespoke agents on Tastewise data + yours
Our team connects the Tastewise food corpus with your proprietary signals — built for your stack, your team’s questions, your workflows.
Continuous optimization
Solutions that compound in value, not static software. Every sprint your AI gets smarter. Every quarter it scales further into the org.
Observable, auditable, governed
Every agent run is traceable, every output citable, every decision auditable. Same enterprise posture every customer AI committee has cleared.
Four functions.
One operating system
Build the story your category needs to hear.
- Market assessment
- Audience targeting
- Occasion mapping
From whitespace to launch, without the guesswork.
- Whitespace identification
- Line extension & seasonals
- Concept validation
- Flavor / dish / ingredient
A narrative your buyer can actually use on Monday.
- Retail growth narratives
- Distribution & assortment
- Merchandising optimization
Always-on signal, on-brand output.
- Messaging strategy
- Asset creation
- Always-on trend feed
AI that doesn’t just answer. It acts
Based on real consumer behavior other AI can’t structure or verify
A live food intelligence built from real consumer, menu, retail, and social signals. Collected, validated, and refreshed daily.
- Menus & recipes, millions of them
- Retail & ecommerce, SKUs, claims, pricing
- Consumer & social, panels and conversations
- New markets added continuously
The deepest
food-data layer in
the industry.
Five things only Tastewise has — all five at once
F&B data depth
Category, consumer, foodservice, retail, trend layers, pre-integrated.
Food data models
Years encoding food taxonomies, signals, and behaviors.
AI talent
GenAI engineers in a vertical where F&B brands cannot hire them.
Already inside the brands that feed the world.
Mars · Nestlé · PepsiCo · Kraft Heinz · Kroger, and dozens more.
Enterprise-grade, already cleared.
100% of customer AI committees have approved Tastewise.
Tastewise agents vs.
ChatGPT, Claude, and Perplexity on food queries
Tastewise is a purpose-built F&B data engine. Our agents run on real-time, true-source data, not the open web — and are tuned for the questions food and beverage teams actually ask.
How leading brands use Tastewise agents
“The partnership between Violife and Tastewise has been a resounding success, significantly boosting our ability to run large-scale lead generation campaigns for our foodservice business.”
Frequently asked
- How is Tastewise Agents different from generic AI like ChatGPT or Claude?Generic AI runs on the open web — incomplete, outdated, and opinionated. Tastewise agents run on a purpose-built food and beverage data graph, refreshed daily and grounded in real consumer, menu, and retail signals. Every claim links back to a verifiable source.
- How quickly can a custom agent be deployed?Most custom agents go from intake to production within 2–4 weeks. Connect your stack on day one and start co-piloting on the workflow your team cares about most.
- Which data sources are included?Menus from 1.4M+ restaurants, 12K+ retail brands, social signals from 90M+ consumers, a proprietary 500K-person panel, and licensed third-party datasets – refreshed continuously.
- Is Tastewise enterprise-ready?Yes. Tastewise is already used by leading global food and beverage brands across retail, foodservice, insights, sales, marketing, and innovation teams. The platform is designed for enterprise workflows, explainable evidence, and repeatable activation across teams and markets.
- What are examples of agentic AI use cases in CPG and foodservice?Food and beverage brands use agentic AI for whitespace identification, flavor forecasting, retail sell-in preparation, campaign messaging, innovation validation, etc
- How do AI agents work in food and beverage?Food and beverage AI agents work by combining live market signals with workflows trained for specific industry jobs. Tastewise agents analyze real consumer behavior to create the evidence teams can use in meetings, innovation planning, retailer conversations, and campaign execution.