Expert Knowledge Graphs to Enhance Agentic Commerce
SearchShopAI and Fodda are partnering to help brands supercharge agentic commerce with expert-built knowledge graphs.
Imagine
- Food & Beverage / Reformulation You changed a formulation six months ago. One ingredient, now an allergen risk for a segment of your customers. A shopper asked an AI if the product was safe. The AI answered from your old product description. It said yes.
- Brand Audit / Category Context You ran a brand audit. AI platforms are flagging your skincare line as "fragrance-free" when it isn't. The audit scores the discrepancy. Fodda's category intelligence explains why it keeps happening: in your segment, "clean beauty" and "fragrance-free" travel together in AI training data, and your metadata never drew the distinction. The fix isn't a single correction. It's a content gap that will keep costing you on every related query until it's closed.
- Metadata / Trend Signals You're launching a sustainable denim line and updating your llms.txt with the obvious terms: "sustainable," "recycled cotton," "eco-friendly." Fodda's trend graph for fashion shows "traceability" and "mill-to-garment" sourcing building fast in your category and not yet saturated, and SearchShopAI's platform data confirms AI agents are already asking for it. Those terms aren't in your schema. A metadata update takes an afternoon. Being first in that query space could take months to replicate.
- Distribution / Platform Routing You sell professional audio equipment and your instinct is to prioritize ChatGPT's shopping surface. SearchShopAI's discovery data, read with Fodda's category signals, tells a different story: your buyer's research journey runs through Perplexity for technical spec comparisons and Copilot for enterprise procurement decisions. ChatGPT handles consumer gifting. Your buyer is rarely there. One routing decision, grounded in category intelligence, stops a quarter's distribution budget from landing on the wrong platform.
The partnership
Fodda is a marketplace of expert knowledge graphs that plugs into AI systems to turn them into domain experts. Instead of relying on training data or generic web search, an AI queries curated, structured data from domain experts and institutional sources, so outputs have more point of view and more evidence behind them. The focus today is retail, beauty, fashion and sport, with institutional data layered on top. The ingredient-safety data below is one part of that layer, not the whole of it. Fodda's category expertise also feeds SearchShopAI's brand audits (providing industry context for what AI platforms are prioritizing in a given sector) and shapes the metadata and distribution strategy that determines how and where products surface when AI agents go looking.
SearchShopAI makes AI platforms discover, recommend, and sell products correctly, and proves it happened. The partnership runs both ways: we refer brands who need each other's layer, and we work together at the technology level. SearchShopAI puts Fodda's category knowledge to work, using it to audit, route and surface a brand more accurately than it could on its own, and to attach dated evidence to every safety verdict it publishes.
The referral is concrete, not vague goodwill. A brand comes to Fodda when the problem is knowledge: they need curated category expertise, trend signals, or sourced evidence behind a claim, structured and consultable rather than scraped. A brand comes to SearchShopAI when the problem is commerce: they need AI platforms to discover, recommend, and sell their products correctly, with safety enforced and the revenue attributed. Most growing brands eventually need both, and now there is a direct path between them. Explore Fodda's knowledge graph platform →
“The edge will come from the quality, structure and provenance of the information you plug into the model, not just the model itself.”
Piers Fawkes, President, Fodda / PSFK
What it means for brands
- Evidence behind every safety judgmentWhen the engine flags a product pairing or an ingredient caution, Fodda-sourced knowledge lets SearchShopAI attach the evidence: de-confounded FDA topical signal, ClinicalTrials.gov study counts, PubMed citations, MeSH pharmacological-action terms, and structured chemical identity, by ingredient. Brands and AI platforms see why, not just what.
- Dated, not just presentEvery cited source carries an as-of date (openFDA FAERS as of May 13 2026, ClinicalTrials.gov as of May 15 2026, for example). Evidence shows its age, not just its existence. That closes the usual gap where "expert consensus" sits behind thin or undated citations.
- Audits grounded in category intelligenceWhen SearchShopAI audits how your brand appears across AI platforms, it taps Fodda's expert graphs to add a frame of reference: which trends are accelerating in your category, what language AI agents use when discussing it, and how your content gaps compare to what buyers are actually asking about. The score reflects how your category works, not just how AI platforms work in general.
- Metadata aligned to what AI is actually askingFodda's trend signals feed directly into the llms.txt, schema markup, and product feeds SearchShopAI maintains for you. If sustainability credentials, clinical efficacy claims, or a new format are the trending attributes AI agents are querying in your category, your metadata reflects that before competitors catch up.
- Distribution routed to the right platformsNot every AI platform carries equal weight for every industry. SearchShopAI routes feed distribution and visibility efforts using Fodda's category intelligence, so they reach the platforms where your sector's buyers actually are, not just the biggest ones.
- Two layers, one jobFodda is the expert knowledge layer for AI. SearchShopAI is the commerce decision layer: the layer that makes the actual call on what to recommend, whether a sale can happen, and which safety rule to enforce. Brands that need both now have a direct path to both.
A health-changing Agentic Commerce case study with skincare brands
AI is already giving your customers safety advice about your products. Most brands have no idea what it's saying.
Is this serum safe during pregnancy? Do these two ingredients cancel each other out? Can I use this if I have sensitive skin?
Real questions, asked to real AI platforms, every day. And the answers matter. A wrong one doesn't just lose a sale. It can send someone home with a product that causes a reaction.
That's the customer side. There's a brand side too. When AI gets your products wrong, your reputation takes the hit. Not the model. The trust you've spent years building gets quietly eroded every time a shopper walks away with bad information.
This is exactly what the partnership is built for. Accurate safety coverage across 743 ingredients, backed by FDA data, clinical trials, and peer-reviewed research. AI platforms get what they need to answer confidently. When they can, they recommend. When they can't, they guess, or they recommend someone who's done the work instead.
The sequence
01
Engine reaches the verdict
Deterministic rules decide what to recommend and whether a purchase can or should happen. No model, no evidence in the loop.
→↓
02
Evidence is joined, read-only
After the verdict, Fodda-sourced evidence is attached by ingredient. It documents the call. It cannot change it.
→↓
03
Brand and AI see the cited, dated backing
The answer arrives with the evidence behind it: de-confounded signal, study counts, sources, each stamped with an as-of date.
The arrow only ever points one way. Evidence flows toward the answer, never back into the decision.
“Instead of leaving logic for the model to guess, [knowledge graphs] allow systems to follow it.”
Piers Fawkes, President, Fodda / PSFK
It goes both ways. Niacinamide, one of skincare's most-used actives, carries 31,236 FDA adverse-event reports, and not one of them is topical. Fodda's curated classification identifies all of them as oral-supplement noise; on skin, it's fine. Other ingredients have a real signal worth knowing about. Getting that distinction right, consistently, across every ingredient in your lineup, is what separates a brand AI trusts from one it hedges on. Every answer is dated too. So customers aren't relying on what the science said three years ago.
“This combination of knowledge graphs and SearchShopAI technology services not only strengthens safety and brand trust, but also improves AI platform visibility and search ranking. LLMs are naturally biased, as they should be, toward recommending safer, more reliable outcomes.”
Nathan Grotticelli, Founder, SearchShopAI
Worth saying honestly: not every risk lives in a database. Some of the most important safety considerations just aren't captured in public records. Where the data stops, the answer says so. No false green lights. That restraint is rarer than you'd think, and it's exactly the kind of thing customers remember.
This is only the beginning
The possibilities and potential for brands that recognize the importance of Agentic Commerce is massive. When a shopper asks an AI about your product and gets a confident, sourced answer, your brand is no longer at the mercy of whatever the model happened to learn. The verdict is yours. The evidence is dated. The logic is traceable.
That is what the partnership produces at the product level. Beyond it, Fodda's expert category knowledge shapes how SearchShopAI reads a brand's audit results, informs the metadata that makes products visible when AI agents go looking, and guides which platforms get priority in a distribution strategy. As Fodda's graphs deepen and SearchShopAI's commerce intelligence compounds, every layer gets stronger and more specific over time. The brands that move now will be harder to displace when every competitor finally catches up.