Powered by SimpleLab: Canopy

Featuring Kendra Kuhl, Co-founder and CEO

Food labels list ingredients, not contaminants. Heavy metals, PFAS, and pesticide residues enter everyday products through how they're grown, processed, and packaged, and almost none of that information reaches the person standing in the grocery aisle.

Canopy, founded by Kendra Kuhl and Kaitlyn Hova, is a community-funded independent testing platform built to close that gap. Community members propose everyday products they want tested and the contaminants they're concerned about. When enough people pool funding to cover the real cost of a test, the product is purchased at retail, sent to a qualified laboratory, and analyzed. Results are published to Canopy's community with reference points that help readers understand what the numbers mean for their own situation. Canopy has no stake in the outcome and no relationship with the brands being tested.

Canopy works with SimpleLab's laboratory network, which gives the platform access to validated food-matrix methods, capacity, and turnaround without having to source and qualify laboratories one at a time.

"Every contaminant has its own methods, detection limits, and set of laboratories that regularly test food, and a new company with a handful of samples and no track record of demand can struggle to connect with the right laboratory. SimpleLab had already done that work." — Kendra Kuhl, Co-founder and CEO, Canopy

Meet Kendra!

Kendra Kuhl, Co-founder and CEO, Canopy

Tell us a bit about yourself and what your role involves day to day.

I work out which tests we offer and at what detection limits, and handle food sample prep and submission. I also define the framework the site uses to turn a lab concentration into per-serving exposure against several benchmarks, so a result is relevant to the person reading it.

What was the moment you realized this needed to exist?

A friend and I started talking about a Consumer Reports article that had found lead in plant-based protein powders. He drank a protein shake every day, and his brand wasn't among the products Consumer Reports had tested. I'd left my last company and was taking a break to think about what to work on next. I've always paid close attention to contaminant exposure in my own life, partly because of my chemistry background and partly out of the ordinary wish to stay healthy. So we split the cost of a heavy metals test on his protein powder, which we sent through SimpleLab. The lab found lead at a very low concentration, so his daily intake came nowhere near any health guidance. Having that number meant he could stop worrying about his protein powder every morning.

The idea grew from there. Lots of people eat the same products and have the same questions about contaminants. A lab test is expensive for a single person to pay for alone. The numbers are possible to get: the instruments exist, the methods are established, labs run these analyses every day. Split the cost of a test across the people who want the answer and the data becomes affordable for everyone. That's what Canopy does. Once a test is funded, we buy the product at retail and publish what the lab finds.

How does a product actually make it from a community suggestion to a sample in a lab?

Every test starts with something a person in our community is actually eating. Today that mostly shows up as a request: someone names a specific product, identified by its UPC barcode, and the contaminant or panel they want it checked for. The request goes up on the site with a funding target set by what the lab work costs. The person who proposed it is usually the best one to reach the others who eat the same thing and want the same answer. They share the request, people chip in, and when the target is reached the test happens. We're building toward tests that come directly from what's in a person's kitchen, on an ongoing basis. Either way, once a test is funded, the path is the same.

We buy one unit at retail, in a store or from an online retailer, the same way any shopper would. We record the lot number, where and when the product was purchased, the expiration date, the claims on the package, and the serving size, and we photograph the packaging. Then the product is prepared and shipped to a laboratory qualified for that specific analysis, anonymized where practical so the product's identity can't influence the result.

The lab returns a concentration, for example micrograms per kilogram, along with a detection limit. We convert that concentration into what a person would consume in a serving, compare the serving-level exposure to health guidance from regulatory organizations and occasionally non-profits, and publish the result with the raw laboratory numbers underneath.

How do you decide what to test for, and how do you turn a community concern into an actual analytical panel?

Two things decide which tests we offer. The first is whether a contaminant is plausible in the food at all. The ingredient list is the starting point, and there's a lot of existing evidence to draw on: FDA's Total Diet Study, which has measured contaminants across hundreds of everyday foods for decades, the academic literature on how specific contaminants move from soil, water, and feed into specific crops and animals, and independent testing programs like Consumer Reports. From that body of work we know rice is worth testing for arsenic, oats and wheat for glyphosate because of pre-harvest desiccation, leafy greens and shellfish for cadmium, and large predatory fish like tuna for mercury. We also pay attention to data that circulates online and raises concern, sometimes because it has been misread. Those are often good candidates for a test, since a clean measurement is the fastest way to settle the question either way. Every contaminant test on the site comes with a description of which foods that contaminant tends to show up in and why, so someone wondering whether a product is worth testing has a place to start.

The second is whether we can put a result in context once we have it. We started with the contaminants where the science is most settled: heavy metals, pesticide residues, and glyphosate all have decades of research behind them, established analytical methods, and health guidance from regulators to compare against. A community concern usually arrives as a category, "heavy metals" or "pesticides," and we map it to a standard panel for that category. More recently identified contaminants, PFAS and microplastics among them, are on our roadmap, and we'll add them as the health science and the reference points mature.

Food is a harder matrix than most people assume. What surprised you about testing it?

The first thing we think about is not adding anything ourselves. At the concentrations these tests measure, a contaminant can come from a container, a utensil, or a glove as easily as from the food. So wherever possible we ship the product to the lab sealed in its original packaging. When we do have to repack something, we choose materials that can't add the contaminant we're testing for. For heavy metals that's a plastic bag. When we move into microplastics and plastic-associated chemicals, we'll need different materials.

The other thing is variation between boxes. Two units of the same product on the same shelf can come from different lots, made on different days from different shipments of raw ingredients, and for produce from different farms or regions. The lot number on the package is a real boundary: a different lot can carry a different contaminant level under an identical label. That's why every result we publish is tied to a lot number and framed as what that unit measured. A single test is a real data point. Repeat testing across lots and over time is what grows confidence in a product, and that's where Canopy is headed.

What does the lab side of this require that would have been hard to build yourself?

Running our own laboratory was never the plan. The right move for a new company is to use existing commercial laboratories where possible. The hard part is finding the right lab for each test and knowing it's reliable. Every contaminant has its own methods, detection limits, and set of laboratories that regularly test food, and a new company with a handful of samples and no track record of demand can struggle to connect with the right laboratory. SimpleLab had already done that work: they've found laboratories that offer the specific tests we need and vetted them for reliability, they have a single interface to order many kinds of tests and get results, and they handle the shipping logistics.

A single sample of a single lot isn't a verdict on a brand. How do you handle that honestly?

Each result on the site is presented as one test of one unit, tied to its lot number and the date and place of purchase. We don't make claims about a brand, a product line, or even the rest of that lot from a single test. We also don't label anything safe or unsafe. Almost every food carries trace levels of something, so the useful question is how much, compared to relevant health guidance.

A single test can tell you what that unit measured, and for most products on the site that's the first independent measurement anyone has published. A high result doesn't mean every box carries that level. It does mean the unit we bought did, and that box made it through the brand's supply chain and quality control and onto a shelf.

We test one unit one time, and that's a deliberate difference from how an academic study would work. To generalize about a lot or a food category, a researcher would run triplicate measurements on multiple units from multiple lots, which multiplies the cost and takes months. That's the right bar for a broad claim about a food category. Our community is asking a more practical question: which foods can I keep eating with confidence, and where might a different brand be worth trying? A single measurement of a single unit gives them a real number to weigh that against, at a cost people can share. Funding a second unit from a different lot adds more information than a duplicate of the first would, and every product page has a "Test Again" button for exactly that. Over time, the dataset accumulates repeats, and those repeats are what start to say something about a brand: consistent low results build confidence, and results that swing from lot to lot point to a problem somewhere in the supply chain.

How do you present results so they inform people without frightening them?

A lab concentration on its own doesn't tell most people anything. Micrograms per kilogram of hummus doesn't connect to the amount on a plate, and a single pass/fail line hides the fact that the organizations setting thresholds don't agree with each other. So we convert every result to exposure: how much of the contaminant a person takes in from one serving of that product. Then we draw that serving as a bar against acceptable daily intake, with a dashed line for each benchmark, ordered strictest to most permissive, and a pass or fail for each. A product can clear three lines and cross the fourth, and that's the honest picture. The reader picks the line they want to follow. Most of those lines come from regulatory bodies, in the US and Europe. Occasionally we add a non-profit benchmark when the regulatory number is controversial.

Some intake guidance is set per kilogram of body weight, so the same serving takes up a larger share of a small child's daily allowance than an adult's. Other benchmarks are fixed daily amounts for a given population. So the page asks for a body weight, defaulting to 150 lb, and every benchmark that scales recalculates for the number the reader enters. A parent can type in their child's weight and see the same result through that lens. Where a benchmark has its own value for a specific group, it gets its own line: a lead result shows one line for children and another for women of childbearing age, each with its own pass or fail. The reader who just wants the number gets the per-serving amount next to the product name and the raw laboratory result, with its detection limit, underneath. When a contaminant isn't found, the chart shows no bar and the label "ND."

We don't tell anyone what to buy. What we can do is show where a product lands, against every reference point we can find, and leave the decision with the person eating it.

What have you learned from what the community asks for?

The requests that get funded are for popular products and for contaminants that are in the headlines. Both conditions matter. A product needs enough people who buy it to share the cost, and a headline is usually what makes those people want an answer at the same time.

Test requests mostly match the known contaminant risks for the type of food. Headlines tend to follow real findings, and the products people ask about are the ones where independent data is thinnest. What a headline can't tell you is the level in the brand you buy, and that's the question the test answers.

It has changed my own shopping. For example, seaweed used to be a daily snack at our house. After seeing the heavy metals results across the brands we tested, it's now a “sometimes” food.

Canopy is early. What has the launch actually been like?

When people learn what Canopy does, the response is almost always some version of "I've wanted this." Nearly everyone wants to know that the food they eat is healthy, and most people have felt the frustration of reading a headline about a contaminant and having no way to find out whether it applies to the product in their own kitchen. So we haven't had to convince anyone that the question matters.

We soft-launched in July with three datasets we funded ourselves, so people could see what a Canopy result looks like before anyone was asked to put money into a test: the method, the lot number, the benchmarks, and the raw laboratory numbers, all visible. Since then, some requests have come from the community and some we've put up ourselves. Each result that comes back is a new data point nobody had before.

Our time is split between growing Canopy's community and improving the platform. The community grows when we share results. Someone sees a number for a product they buy, wants to know about the next one, and signs up. On the platform, we're building an app that connects what's in a person's kitchen to our dataset. It shows which of their own products have been tested and how contaminant exposure adds up across everything they eat, instead of one product at a time.

Where does this go from here, and what would make it easier to get there?

The goal is to give people the tools to be confident that their own dietary contaminant intake sits inside health guidance. That's hard for an individual to do today. FDA's Total Diet Study estimates exposure to heavy metals and pesticides across the U.S. population, by combining measured concentrations in foods with national data on what people eat. It's a serious program, and it answers the question at the level regulators need. Canopy is the next step, applying the same idea to an individual instead of the population. Each of us eats specific products, and what we take in depends on which ones. We want people to be able to see their own exposure, product by product, and then control it by knowing which of the things they eat carry more of a contaminant and which carry less.

Getting there takes a dataset with depth and breadth. Right now most products on the site have one result, and most products people eat haven't been tested at all. Over time we want many more products in the dataset, and the same products tested again, across lots, so a reader can see whether a product is consistently low or swings from batch to batch. We'll keep adding contaminants as the science and the reference points mature. The app we're building is the other half: it connects what's in someone's kitchen to the dataset and shows how exposure adds up across everything they eat.

The dataset grows one funded test at a time. The tools get better with each release. And public awareness that this kind of data exists, and that a person can get it for the food in their own kitchen, builds one shared result at a time. We're early, and it will take time. The fastest way to speed it up is to bring your own question: if there's a product in your kitchen you've wondered about, propose a test at forcanopy.com and send it to the people who eat the same thing.

At SimpleLab, we're proud to support the teams working to put reliable laboratory data in the hands of the people who need it. We're grateful to Kendra and Kaitlyn for showing what independent, community-funded testing can look like, and for holding a high bar on what a single measurement can and cannot say.

Stay tuned for more stories from our growing network of labs, businesses, and changemakers.

Follow Canopy

Want to see what's being tested? Visit forcanopy.com to browse published results or propose a test of your own. You can also follow Canopy on LinkedIn, Instagram, and Facebook, or connect with Kendra Kuhl and Kaitlyn Hova on LinkedIn.

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