The modern swine barn contains a peculiar piece of inherited logic: when the exact breeding window is uncertain, do more. Check heat again. Expose the sow to a boar again. Inseminate twice, sometimes three times. It works, but it works the way carrying two umbrellas works. The extra one is not useful unless your forecast is bad.
Verility, a small animal-health company in Maxwell, Indiana, is trying to improve that forecast. Its product, Fertile-Eyez, combines a phone, compact optical hardware and machine-learning image analysis to read fertility signals beside the animal. The pitch is pleasantly concrete. Tell a producer which sow to breed and when, then help the same operation use fewer semen doses, fewer repeated checks and fewer hours of labor.
This is artificial intelligence without the usual theater. There is no robot wandering between pens and no chatbot advising a pig. A user takes a biological sample, the system captures an image, and an algorithm classifies what it sees. The output is meant to change a decision that already has a cost attached.
01 / The first betA microscope learns to travel
The science did not begin on a farm. Hadi Shafiee’s research group at Brigham and Women’s Hospital developed smartphone-enabled reproductive diagnostics for human health. The underlying idea was to combine inexpensive optics with software that could evaluate samples outside a traditional laboratory. Verility, co-founded by Shafiee and animal-health veteran Liane Hart, licensed intellectual property for veterinary use and began the awkward work of crossing species.
Hart brought the market map. She studied animal science, breeding and genetics at Purdue, worked across animal-health product development and commercialization, and knew that fertility is both a biological problem and a production schedule. Shafiee brought the imaging and diagnostic foundation. Around them, a compact team added finance, product development and links to Purdue’s animal-science and startup networks.
Their first public emphasis was semen quality. Fertile-Eyez was designed to measure concentration, motility and morphology - essentially how many sperm cells are present, how they move and whether their shape appears normal. In a peer-reviewed boar-semen study, the portable system produced measurements broadly similar to established computer-assisted tools and expert assessment. The result mattered because it moved the device from clever demo to plausible instrument.
It also exposed the limit of the first pitch. Semen analysis is valuable, but it is only one side of conception. Farms still had a basic timing problem on the female side. Ovulation is variable, influenced by biology and environment, and hard to track cheaply without repeated observation or invasive tools. Verility changed its emphasis. The product would not merely inspect the input. It would help time the event.
02 / The field testThe numbers that changed the story
In 2024, Verility ran a validation study involving 400 sows with AcuFast, which supplied animals and study oversight. The Fertile-Eyez protocol delivered pregnancy and embryo rates comparable to the traditional protocol, according to results released through Purdue. But it got there with an average of 1.15 inseminations per sow, versus two under the standard approach.
The study also reported 45 percent fewer boar heat tests, 20 percent fewer boar-exposure days and a 38 percent pregnancy rate among nonstanding-heat sows, animals that would typically not be inseminated. That last figure is quietly provocative. A sow interpreted as nonproductive may be culled. A better fertility signal could turn some apparent failures into productive animals.
What precision removed
Reported change versus the traditional breeding protocol
The product’s price has not been made public, so the ROI claim cannot be reconstructed from a price list. The useful cost story is operational. A dose of premium boar semen can be used once instead of spread across defensive repetition. Staff can spend less time checking heat. Fewer nonproductive days mean more efficient use of each sow space. On a large operation, several modest savings can stack into a purchasing argument.
03 / The productFrom sample to “breed now”
Collect
A user takes a non-invasive fertility sample beside the animal.
Image
Phone-mounted optics capture a standardized picture for analysis.
Decide
Software classifies the fertility stage and informs the breeding protocol.
Fertile-Eyez is better understood as a decision system than a gadget. The hardware makes the sample visible. Computer vision makes the reading less dependent on a skilled technician. The mobile interface carries the answer to the point of care. Over time, the analytics layer could aggregate fertility observations across animals and facilities, giving the company a dataset as valuable as the instrument.
That combination separates Verility from any single alternative. A CASA machine can analyze semen, but it is built for a different setting and workflow. Manual microscopy depends on training and interpretation. Conventional heat detection is familiar but laborious. Hormonal interventions can control ovulation, but Fertile-Eyez is pitched as a diagnostic that reads the animal’s biology instead of forcing it. The company’s claimed edge is not that each component is unprecedented. It is that semen analysis and ovulation timing can travel together in an affordable, mobile platform.
The initial customers are swine producers, genetics companies, breeders, veterinarians and reproduction specialists. Verility says it is launching in the United States and Canada, while the broader platform could extend to cattle and other species. The business is B2B, but its exact commercial packaging remains private. Hardware sale, per-test consumables, software fees or enterprise contracts are all possible structures; Verility has not publicly committed to a price card.
04 / The companyPatient capital meets impatient biology
Verility won $100,000 from Purdue’s Ag-Celerator in early 2022, then announced a $3.5 million Series A led by Mountain Group Partners that July. A later securities filing showed the cumulative round reaching almost $4 million. In March 2026, another filing disclosed $150,000 sold through convertible notes in a planned $2 million offering. Valuation and revenue are not public.
The capital has funded a long translation cycle: license hospital intellectual property, adapt it for animal samples, validate against accepted lab methods, prove a farm protocol, protect patents across markets, then begin customer trials. This is slower than shipping a software dashboard. Pigs do not accept a patch on Tuesday because the sprint ended Monday.
Partnerships are part of the operating model. Brigham and Women’s supplied the scientific origin and licensed portfolio. Purdue provided research infrastructure, talent, credibility and venture support. Acuity Genetics helped sponsor semen validation. AcuFast helped run the sow study. Mountain Group Partners brought capital and animal-health experience. A six-person company profile can look tiny beside those names; the network is how a tiny company borrows scale.
Where the thesis breaks
- If sample collection varies by worker or barn, the image model may receive noise instead of biology.
- If a farm cannot change staffing or semen purchasing, measured efficiency may not become cash savings.
- If results do not replicate across genetics, climates, housing systems and herd-health conditions, one strong study stays one strong study.
- If testing adds more friction than the repeated checks it replaces, familiar routines will win.
05 / The copyable moveFind the expensive hedge
There is a founder lesson hiding in the breeding shed. Do not begin with “Where can we add AI?” Begin with “Where does a customer repeatedly pay to protect against uncertainty?” Farms double-inseminate because the biological window is imprecise. Warehouses hold safety stock because delivery is uncertain. Clinics repeat tests because measurements vary. Find the hedge, improve the measurement and let the removed repetition pay for the product.
Verility’s second lesson is less fashionable: let evidence demote the original pitch. Semen analysis gave the company technical credibility and a publishable comparison with established systems. Ovulation detection gave it a sharper economic story. The company did not abandon its first capability; it repositioned that capability inside a larger platform and moved the commercial spotlight.
The next test is not another flattering percentage. It is repeatability under ordinary conditions. In 2025, Hart said the company planned customer trials through 2026 while collecting letters of intent and purchase orders. Awards, including a Farm Bureau challenge semifinal and an Indiana agbioscience award nomination, add visibility. They do not replace orders.
Still, the proposition has the virtue of being falsifiable. If Fertile-Eyez reliably tells farms when to inseminate, the semen refrigerator empties more slowly, staff calendars change and reproductive output holds. If it cannot, the old two-umbrella protocol remains rational. Verility has found a problem where the customer does not need an AI lecture. The barn will keep the score.