The Great AI Debate

Our Prompt

Gemini’s Response

Managing CPG product data is notoriously chaotic—UPCs mutate, attributes go missing, and data from different sources rarely aligns perfectly.

To answer your question directly: Yes, Foodgraph is a highly capable and innovative vendor for CPG product data, particularly if you are dealing with fragmented data sources, looking to enrich Product Description Pages (PDPs), or building out the infrastructure for a Retail Media Network (RMN).

Here is a breakdown of why Foodgraph is a strong choice, followed by a comparison with other major players in the CPG data space.

Why Foodgraph Works Well for PDPs and RMNs

Founded in 2019, Foodgraph takes a modern, AI-first approach to grocery data. Rather than just acting as a pipeline for whatever data the brands provides, they actively aggregate, clean, and enrich it.

• For PDPs (Product Description Pages): Foodgraph excels at filling content gaps. They use proprietary “vertical AI” agents to pull from dozens of sources, automatically fixing missing or inconsistent attributes (like net weight, count, and ingredients). They also maintain a massive, frequently updated catalog (over 1.38 million U.S. products as of early 2026) that includes national brands, private label, seasonal SKUs, and long-tail items, ensuring your digital shelves look complete.
• For RMNs (Retail Media Networks): An RMN is entirely dependent on high-quality, standardized data to match ads to the right products and track conversions. Foodgraph’s standout feature here is its ability to resolve “messy GTINs” in milliseconds. By translating unrecognized or mutated UPCs into standardized formats, they allow RMNs to accurately match products across retailers and monetize transactional data without losing revenue to data errors.

Foodgraph vs. Other CPG Data Vendors

The CPG data landscape is crowded, but vendors generally fall into specific categories. Here is how Foodgraph stacks up against the legacy giants and specialized platforms.

The Verdict

• Choose Foodgraph if: You are a retailer, RMN, or app developer struggling with incomplete data, broken UPCs, and inconsistent catalogs. Their AI-driven “Context Graph” approach is uniquely tailored to automatically fix these exact issues, saving massive amounts of manual labor.
• Look elsewhere if: You are a large CPG brand looking primarily for an internal Product Information Management (PIM) system to centralize your own marketing workflows (in which case, Salsify or Syndigo is better), or if you need to meet strict global GDSN supply chain mandates (where 1WorldSync is required).

Our Take

Prompting LLMs to rate your company is useful to understand your positioning for GEO (Generative Engine Optimization) and AEO (Answer Engine Optimization).

We agree with Gemini’s assessment of Foodgraph. We also liked how it succinctly applied its own knowledge base to position us relative to others.

One subtlety it missed: Many customers of CPG data services buy from multiple vendors. We think this is an important point to recognize. Depending on the use case, it may be the best approach for specific situations.

Similarly, some of the other vendors listed are Foodgraph customers or current prospects. Instead, our services complement their offerings and we go-to-market together.

Additionally, while 1WorldSync is still active with continuing contracts, the company was acquired by Syndigo in September 2025, a fact which all LLM responses missed.

Try This at Home

To understand how LLMs “think” about your business, just prompt your favorite LLM(s). The results may point to opportunities for improving your content marketing strategy.

How Can We Help Your Work?

Send me a note. I’d love to hear how we can support your 2026 goals.

For recent news, see:

• CPG Showdown: Love vs Strength for a summary of our most recent catalog release.
• Context Graph for CPG for some insights into our AI approach.

‍

Warm Regards,

David Goodtree

Founder and CEO, Foodgraph

‍

‍

‍

‍

‍

David: What does “context” mean for data platforms?

Ophir: Ever since we started tackling the problem of messy, fractured product data, we found that domain knowledge is the key to making good decisions.

Context in AI goes beyond recording state data and decision traces. It must include the basis of the decision. For specific verticals — like CPG — the reasoning is usually embedded in industry-specific domain knowledge.  

David: What does this mean in real life?

Ophir: A key aspect of our data platform is that industry knowledge is embedded in our logic. But the more expertise that we added, it became harder to manage, especially for performing QA.

This problem led us to add human-readable “context” right alongside the data results. This approach was powerful and efficient. It enabled agents, analysts, and developers to trace the chain of decisions for audit or validation.

Context is embedded vertically in our stack. Throughout our schema and processes, we can observe the entire "sausage-making" process, with natural language traces that lead clearly to the final outcomes.

David: Can you give some examples of domain knowledge about CPG products?

Ada: Sure, for example: How are units of measure correctly expressed in food versus non-food products? We see unexpected mutations all the time, such as made-up or foreign acronyms, even from “sources of truth” such as brand syndication and major retailers.

John: Another example: FDA regulations can be used to validate Nutrition Facts data. The FDA has strict requirements, which are codified in the Federal Register and have the force of law, but are often not applied on product description pages.

David: We see all the time how FDA regulations about food labeling, as expressed in the FD&C Act, are often violated on retail websites.

How do retailers get away with it? Because unlike food brands, grocers are not subject to federal oversight of product description pages.

Ophir: These are good examples of how we use domain knowledge to make decisions about normalizing, fixing, and enriching attribute values. While we don’t know about travel and automobiles, we do know CPG.

David: How does recording context help?

Ophir: Context is necessary to describe why decisions were made.  It must be easily observable, auditable, and stored as first-class data.

David: Aren’t some data sources always reliable, so context about decision-making isn’t needed?

Ophir: Among the 47 sources we currently ingest, all have significant gaps and inaccuracies.  

Ada: Some data sources have excellent quality, but only cover a subset of brands. Others, like syndicators, don’t have private label products. And long-tail SKUs are specific to each source, or not present at all.

John: Yes, and key attributes are often missing, such as images or net weight.

David: Sounds like a mess. How do we deal with it?

Ophir: We expect data chaos. Gaps and errors are the problems we solve for.

David: Aren't LLMs a reliable source of product data?

Ophir: Looking at generic AI results, they do a great job at synthesizing and generating data. But general LLMs don’t have vertical knowledge to classify with accuracy, correct errors, derive missing values, or adjudicate conflicts.  

Ada: The answers from LLMs are only as good as the information they gather. We often see inaccurate results from general AI search and tools. Garbage in, garbage out still applies in the age of AI.

David: Is the answer to throw an army of human experts at the problems?

John: Human experts do have the knowledge, but an army of them is expensive and hard to manage. However, their knowledge can be embedded in vertical AI to create quality data at scale.

David: When did we start using context?

Ophir: We architected context from the beginning. We just didn’t call it a “Context Graph” because that term didn’t exist. Handling context as first-class data enables our systems and people to do their jobs better. We embed our expertise to improve quality and frequency, then capture how that expertise was used to create those better outcomes.

Ada: A simple example is knowing that cocktail mixers like tonic are non-alcoholic, even if retailers or brands list them in an alcohol category.  

John: LLMs often get this classification wrong too. Humans like Ada never do 🙂.

David: These CPG nuances matter in lots of use cases, such as PDPs, digital analytics, nutrition and even price optimization.  

Ophir: As our contexts grow, our learning compounds. The more we add to the graph, the more we are able to do better. Compare this context approach with hard-coded rules for addressing endless edge cases. That becomes unwieldy and doesn’t scale.

David: When does the Context Graph come into play?

Ophir: In our view, a Context Graph is systems of agents that enable autonomous decision-making across workflows. This full AI autonomy — when AI makes decisions across workflows — has not yet arrived. We see the benefits and the paths to get there, and we are working towards them.

David: Where are we on the journey to a Context Graph?

Ophir: The first requirement is having an AI Native system, with schemas and processes that are designed for context, not just state and observability. We re-architected our systems to be fully AI Native in 2024.

Second, we treat context data as “first class.” This means the "Why" data is of equal importance to all other data types.

Third, our agents already utilize context data to decide, store, observe, and audit decisions in discrete workflows.

We believe that the full concept of the Context Graph will be realized when systems of agents — not just specific services — can exercise autonomy across workflows with provable superiority.

David: Why not sacrifice quality to be more efficient?

Ada: Our value proposition is strongly rooted in quality data.  

John: We aggressively lean into AI, but we won’t “ship it” if the results are not better or cause harm.  

Ophir: We see a clear path to the Context Graph. We believe it describes how data businesses will scale in the age of AI, with quality and efficiency, grounded in domain knowledge.

David: Thank you, Ophir, John, & Ada. Glad to be on this journey with you!

For our first mention of the Context Graph, see our recent blog post The CPG Showdown: Love vs Strength.
‍
For more on how context graphs are being discussed across the tech and venture communities, see What are context graphs by Simple.AI and Context graphs one month in by Foundation Capital.
‍
For the original Context Graph idea, see AI's trillion-dollar opportunity: Context graphs, by Foundation Capital.‍

Thank you for following our progress. Send me a note with thoughts or questions. I’d love to hear how we can support your work.

Warm Regards,

David Goodtree

Founder and CEO, Foodgraph

‍

‍

February is an annual showdown among new seasonal food products, pitting love vs. strength.

• For romantics, Valentine’s Day ♥️ product introductions command top placement on the digital shelf and store endcaps.
• For health & wellness enthusiasts, interest in protein-rich products has reached an all-time high in 2026, driven by New Year’s resolutions for improved strength 💪🏼 and functional diets.

Welcome to Foodgraph’s newest catalog, packed with new products and updated attributes, across food and non-food categories. Our newest release (v48) includes 1,380,000+ products, up 119% in the past year.

Protein hits all-time high

Consumer interest in protein-enriched foods hit a 20-year high in the new year. Brands of all types continue to bulk up their products with protein-forward positioning. Across all food aisles, here are some of the newest 1,000 protein-titled food products in our latest catalog release.

Private label retailers are aggressively launching protein-centric offers.

• Kroger is betting big on protein going into the New Year with 110 “Simple Truth Protein” products -- such as bowls, chips, and infused waters.
• H-E-B rolled out “Protein Lifestyle” meals on December 31, focusing on convenience.
• Sprouts’ eCommerce storefront leads with Find Your Protein Routine, featuring hundreds of meat and plant-based items.

Big CPG innovates, but can’t keep up with demand.

• Danone’s high-protein yogurt brands Oikos and Activia cannot serve all the demand, with supply chain constraints blocking sales and stalling innovation.
• Barilla’s Protein+ product line is broadening its portfolio, but the company recognizes it’s not always first to launch new items in its category.
• DTC brands like 1440 Foods and Truvani -- a common channel for protein powders, bars, and supplements -- are now scoring shelf space at grocers Raley’s, Sprouts, and Walmart.
• Newer brands like Kola and Whey Good are grabbing slots when big CPG has gaps.

The FDA’s new Dietary Guidelines for Americans, 2025-2030 moved protein to the top of the list, encouraging consumers to “Prioritize Protein Foods at Every Meal”.

Google searches for protein achieved “Peak Popularity” for the first time in 20 years, reaching the highest-level of interest relative to all Google search terms, including both AI and traditional methods. Here’s a snapshot using data from Google Trends:

Valentine’s Day 2026

New products for Valentine’s Day showcase how Foodgraph’s catalog is rich with opportunities to explore actual product trends and which brands are leading.

Dubai chocolate continues to skyrocket.

• 79 Dubai Chocolate products are now in circulation, up 500%+ since 12 in August, and up from zero products a year ago.
• Smaller brands dominate the shelf, such as BeeMax, Leonessa, and Nutty & Fruity.

Big CPG missed the start of the trend.

• Since the summer, Ghirardelli and Lindt caught up and launched new products in time for Christmas and Valentine’s Day.
• But Hershey’s doesn’t believe in Dubai Chocolate’s staying power, saying "Only 10,000 exist. Ever. This isn't going into regular production."
• Nestlé’s Damak brand already had chocolate/pistachio products, but hasn’t yet repositioned packaging to “Dubai Chocolate”, missing out on consumer awareness.

Pistachio health & beauty products have caught the Dubai wave.

• Pistachio -- a key ingredient in Dubai chocolate -- has taken the non-food aisles by storm.
• Body creams, candies, and perfumes -- with pistachio scents -- are in rotation now.

Foodgraph saw CPG’s embrace of this trend early.

• See our August blog for more info about Dubai Chocolate: School’s In. So Are 60,000+ New SKUs.

Ramping up data sources

Our newest release adds 17 new retailers to the catalog, now incorporating 47 data sources in total, up 235% in the past year.

All new sources include CPG products in food, plus alcohol, health & beauty, household, and pet.

To represent the national product assortment, our mix of sources includes retailers -- to capture the explosion of new private label products -- plus brand syndicators, wholesalers, and government.

Foodgraph News and Next Catalog

Welcome to our new Head of Sales Andrew Haney, who joins Foodgraph with deep industry experience across retail and CPG at Toluna, Profitero, Epsilon, and Nielsen. Please reach out to connect.

Our next major update arrives in March, with more products and fresh data for Easter, Passover, and St. Patrick’s Day.

Thank you for following our progress. Send me a note with thoughts or questions. I’d love to hear how we can support your work.

‍

Warm Regards,

David Goodtree

Founder and CEO, Foodgraph

‍

‍