The Mashgiach’s Algorithm: Is the Future of Kosher Supervision Smarter with AI?
Q: What are some of the ways OU mashgichim in the field are using AI?
A: Mashgichim often leverage AI to analyze, digest and distill complex information that would otherwise be difficult—or even impossible—to gather.
Consider this example: Rabbi Mordechai Tarkieltaub, an OU field representative in Chicago, was assigned to supervise a special production run at a hard candy manufacturer. The equipment had already been kashered, and the ingredients—including sugar and flavors—had been reviewed by the OU office. Before his visit, Rabbi Tarkieltaub asked AI: “What is the most expensive ingredient in hard candy production?” The answer: sugar. While sugar seems straightforward, in food production even sugar can be tricky.
He followed up with another question: “How does a hard candy company save money on sugar?” Answers included using flavors for sweetness or adding fiber. The third answer caught his attention: recovering candy from a previous batch that didn’t meet quality specifications, purifying it, and reincorporating the recovered material into production. This process, known as rework, allows a company to reuse partially processed—or fully cooked—products instead of discarding them, reducing waste and salvaging imperfect batches.
With this in mind, Rabbi Tarkieltaub asked the plant manager, “How are you recovering your sugar?” The answer was revealing: The facility had equipment to purify imperfect candy and to isolate the sugar for reuse. In this case, the reworked candy had been dairy, so the recovered sugar was dairy as well. The OU ensured the final product was labeled correctly as dairy.
Another example: Mashgichim frequently work with highly sophisticated industrial machinery, such as beverage pasteurization systems used to eliminate bacteria in juice, soda or milk. They must determine how—and whether—these systems can be kashered. AI helps by providing precise, structured explanations of both the conceptual logic and technical mechanics of the equipment, making it easier to identify which parts require kashering.
Q: How is OU Kosher headquarters planning to capitalize on AI resources?
A: OU Kosher maintains a vast repository of decades’ worth of reports, memos and correspondences, created by everyone from rabbinic field representatives (RFRs) to headquarters staff and poskim, covering virtually every issue in the food industry. After every plant visit, a mashgiach or RFR files electronic reports documenting problems encountered. These reports are reviewed by the Rabbinic Coordinator (RC) back at OU Kosher headquarters, and together, they solve whatever problems arose. Over time, these reports create a comprehensive storehouse of institutional knowledge.
Questions about technology replacing a mashgiach long predate AI.
The challenge is that much of this information, while stored, is not easily accessible to other mashgichim and RCs when similar situations arise elsewhere. A large language model can organize and summarize existing records, making it easier for mashgichim to find relevant information when needed.
For example, whenever we certify a new plant, we want to equip the field representative with an understanding of what we call the critical control points—stages in the process that require special attention because they present potential vulnerabilities to the kashrus of the product.
Let us say, for example, that we are setting up a certification at a hard candy manufacturer that produces both pareve and dairy candies. The possibility of recovering already used sugar—the issue of rework we discussed earlier—should be flagged as a potential critical control point for the administrator of this account to consider.
Years ago, I was the RC responsible for managing the kashrus of a company that produced OU-certified grain vinegar. The company also operated a separate production line for non-kosher red wine vinegar. The two lines were entirely separate—or so it seemed.
At one point, we discovered that environmental regulations required the manufacturer to capture vapors released during the vinegar production process. Those vapors had to be collected, diluted and then disposed of in some way. The company devised what it considered an elegant solution: adding the diluted vapor-water back into the grain vinegar. We, of course, had to address this immediately and require a reconfiguration of the system to ensure that wine vapors could not be introduced into the kosher product.
The entire episode was documented in reports and correspondence. For years afterward, I reminded anyone working with kosher and non-kosher vinegar facilities that vapor recovery is a critical control point. By digging into OU Kosher’s vast repository of such cases, AI can help identify these critical control points—the stages of a process most vulnerable to cross-contamination.
As another example, the OU has an Ingredients Department, one of whose responsibilities is determining which ingredients are considered innocuous—meaning an OU-certified company may purchase them even if they are not formally certified. I work in this department. We maintain a listing for an ingredient called guar gum, a plant derivative that must undergo processing before it is usable. What kinds of kashrus risks arise in the processing of guar gum? Our database includes reports from mashgichim who have described these processes in different contexts. Being able to access and synthesize those reports to assess potential risks is invaluable. AI can pull together years of scattered documentation and summarize the findings, saving both time and effort.
Beyond kashrus analysis, AI also offers practical administrative benefits. With nearly 1,000 mashgichim worldwide and thousands of weekly visits, OU Kosher can use AI to optimize RFR routing by mapping mashgichim’s locations against accounts, quickly identifying whether current routes make sense or if another mashgiach could cover a visit more efficiently.
Consider a full-time mashgiach in Belgium, a part-timer in Spain, and a plant in Portugal that needs a visit—who should go? Routing decisions cannot be based on geography or cost alone. Some mashgichim bring specialized technical expertise that may justify a less obvious choice. AI can take all the variables into account and provide clear, constructive guidance that facilitates smarter decisions overall.
Q: Is AI forcing the OU to consider when technology can replace the need for a mashgiach?
A: Questions about technology replacing a mashgiach long predate AI. Certain tasks can be performed by automated systems as well—or even more effectively—than a mashgiach. For example, facilities that process whole eggs often use robotic detection machines to identify blood spots and remove them immediately. Given operations that handle over a million eggs per day, a mashgiach simply cannot match that volume or consistency.
Historically, the OU has embraced new technologies while resisting their use merely for convenience. Take kosher cheese production: Halachah requires mashgiach oversight, and video monitoring has been proposed as a substitute for on-site supervision. Rabbi Yisroel Belsky, zt”l, former OU Kosher senior halachic consultant, maintained that an OU representative must be physically present. Similarly, the OU still requires mashgichim at restaurants despite monitoring technologies that could theoretically replace on-site supervision.
We should not allow our own analytical skills to atrophy.
Rabbi Moshe Feinstein addresses a related case in a teshuvah (Iggeros Moshe, YD 1:35) concerning insect infestation in sauerkraut. In Europe at the time, cabbage was heavily infested. In the United States, however, pesticides were demonstrably effective in reducing infestation to a level at which the cabbage could be considered acceptable even without further checking. Rav Moshe was asked whether the use of pesticides could be relied upon—meaning, assumed to have been used and to have been effective—without additional inspection. He concludes that, technically, for the reasons outlined in that teshuvah, the sauerkraut is acceptable. At the same time, he adds that one who is a ba’al nefesh—someone exceptionally conscientious—should verify that proper oversight systems are in place and not rely solely on presumptions.
This teshuvah illustrates a key principle: There may be sound technical grounds for relying on technology, but the OU, in certifying a product, must take responsibility to ensure that all systems are functioning properly.
Q: Still, would you not agree that AI, in its capacity to learn and develop, is a new paradigm?
A: Yes. But the more pressing question is not whether AI could replace a mashgiach—it’s what is irreplaceable about a properly trained mashgiach. The answer may seem obvious but is worth articulating.
There is an intangible—but nevertheless very real—advantage to a trained and experienced mashgiach. Among other things, he is able to assess an entire situation as a whole. Over time, he develops a sense of whether something is amiss or whether a new development warrants closer attention. Time and again, mashgichim have followed an instinct that led them to probe more deeply into a situation, only to discover that their concern was well founded. Sometimes the trigger is something seemingly minor, such as the body language of a production operator—for example, avoiding expected eye contact when speaking with the mashgiach. I would call it a form of binah (insight) that some particularly gifted mashgichim possess. We all want someone with that sensitivity visiting our companies.
Q: What are some pointers on how not to use AI?
A: OU Kosher staff regularly work with proprietary information. The OU AI Committee has issued a best-practices guide stressing that large language models may incorporate uploaded data into their learning models, potentially compromising confidentiality. Staff must be mindful of this risk.
Another issue—common across professions—is the risk of laziness. At times, using AI will result in people relying on superficial searches or quoting responses without verifying the source. We should not allow our own analytical skills to atrophy or dull.
Q: How is the OU using AI to help consumers?
A: All departments at the OU, including OU Kosher, are working with a consultant who is providing introductory and advanced sessions on meaningful AI use.
We are currently focused on using AI to help the kosher consumer, starting with making our product search—oukosher.org/product-search—smarter and more intuitive. The site already offers the most up-to-date list of thousands of OU-certified products, but our goal is to make searching faster, more precise and genuinely helpful.
With AI, a search for “dairy-free” could surface oatmeal or soy milk—even if those words don’t appear in the product name. In other words, the system doesn’t just match keywords; it understands what people are actually looking for. We are also exploring several other ways AI can assist kosher consumers.
At the same time, we are mindful of AI’s limits. Baruch Hashem, we have a staff of professionals with real intelligence and expertise. Ultimately, any suggestions from AI must be reviewed and validated by senior OU Kosher staff before implementation. AI is a powerful tool—but at the end of the day, it’s just a tool, and nothing more.
Rabbi Gavriel Price is a rabbinic coordinator at the OU and, as a member of the Ingredients Department, conducts research in ingredients manufacturing.
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