Halo AI raises £750k to build a 90-minute antibiotic susceptibility test for newborns with sepsis
The Oxford company’s technology agreed with reference testing on 96.8% of results in a study at 6 NHS hospital laboratories. The round, led by Kestrel Point Capital, will pay for the first benchtop instrument.
Oxford, 13 November 2026: Halo AI, which is developing a rapid antibiotic susceptibility test for newborns with sepsis, has raised £750k in seed funding at a £5m post-money valuation. Kestrel Point Capital, a London investor in early-stage life sciences companies, led the round.
The test shows which of the 8 antibiotics used in neonatal units will kill the bacteria growing in a baby’s blood, and it does so in about 90 minutes. Standard testing takes around two days. In a prospective study of 1,214 blood cultures at 6 NHS laboratories, a laboratory prototype agreed with reference testing on 96.8% of results. The benchtop instrument that hospital labs would use, the Halo Reader, has been designed but not yet built. The seed round will pay to build it, to hire the company’s first four employees and to start taking the test into hospital labs.
Take a baby born six weeks early who develops a fever on day five. Her blood culture flags positive overnight. As things stand, the neonatal team keeps her on standard antibiotics and waits up to two more days to learn whether they are working, because standard testing needs a further day to grow a pure culture and then an overnight test (Journal of Clinical Microbiology study via PMC). With a 90-minute test, the lab could have the answer by mid-morning.
Neonatal sepsis affects an estimated 3 million newborns a year and causes up to 570,000 deaths (Bulletin of the WHO). An estimated 214,000 of those deaths each year are attributed to resistant bacteria (PLOS Medicine meta-analysis). In one global study, 18% of babies whose blood culture grew a pathogen died (GARDP).
Standard testing waits for bacteria to grow into a colony. Halo AI watches individual bacteria instead. A lab scientist pipettes a drop of the positive culture into a single-use cartridge, where the bacteria flow into hundreds of microscopic channels, some of them loaded with an antibiotic. A camera films the channels and the software tracks every cell. Cells that keep dividing show that the drug has failed, and cells that stall and swell show that it is working. Most rapid systems are built for adult Gram-negative infections. Halo AI is trained on the bacteria that infect babies, including Group B strep and staphylococci.
In the Okoro Laboratory at Oxford I filmed 768 combinations of organism and antibiotic under the microscope, and within the hour you could see whether each antibiotic was working. The laboratory result still takes two days. For a baby on an antibiotic that isn’t working, each of those hours gives the infection more time, so we built Halo AI to read the answer as soon as it is there.
When a baby’s culture comes back positive we have to choose an antibiotic there and then, without knowing what the bug is sensitive to. It’s the hardest call we make, and we don’t usually get the answer until day three. If the lab could tell us on day one, that baby would be on the right antibiotic two days earlier.
Availability. Halo AI is not available to laboratories yet. The first Halo Readers built with this round will go to NHS labs serving neonatal units as pilots, and microbiology labs that process more than 20 positive blood cultures a month can register their interest. After the first pilots the company plans a 12-month evaluation with Lakeside Children’s Hospital in Kenya, where resistance to the WHO-recommended first-line drugs is common. Halo AI is recruiting its first four employees, and two of the roles are open to recent graduates. The roles are listed on the careers page.
About Halo AI. Halo AI Ltd is a diagnostics company based in Oxford, UK. It is developing a single-cell imaging test that reads antibiotic susceptibility directly from positive blood cultures, starting with newborn sepsis. The study behind the figures above is available as a preprint (opens in a new tab).