MEQ Solutions Secures A$23M for AI-Driven Meat Grading Tech
Image Credit: Jacky Lee
Melbourne based MEQ Solutions has reported an A$23 million (US$15 million) Series A funding round led by Insight Partners, announced on 15 December 2025. The company says it will use the capital to accelerate rollout of its measurement hardware and software, and to expand its team and partner work across multiple markets.
Why This Matters in An AI and Hardware Sense
Meat grading is still heavily shaped by fast, high volume visual decisions on processing lines. That creates a classic IT problem: inconsistent inputs, limited time to decide, and difficulty turning those decisions into traceable, comparable data. MEQ’s pitch is to shift key grading steps toward sensor capture plus machine learning models, then push results into plant systems and reporting workflows so decisions are more repeatable and auditable.
What MEQ Is Building
Public reporting and MEQ’s own product material describe a suite that combines purpose built devices with a data platform. Startup Daily reports the company started in Adelaide in 2016, with its first product MEQ Probe, and has since built out probes, cameras, and an insights platform.
MEQ Probe: spectral sensing plus deep learning on the hot side
Industry funded work published by the Australian Meat Processor Corporation describes MEQ Probe as collecting spectral data and using proprietary deep learning architecture designed for spectral processing to convert that data into intramuscular fat percentage outputs (for lamb) and marbling outputs (for beef). The same report outlines how the probe is used on the slaughter floor around the 12th and 13th rib, with operator guidance to keep scanning speed consistent.
Meat and Livestock Australia also published a final report (P.PSH.1132) describing trials in lamb and beef plants to stress test the system, and stating the data aim is to be objective and available early in processing to support earlier decisions.
MEQ Camera: video capture plus depth sensing, designed for plant conditions
A JBS Australia fact sheet describing its deployment says the MEQ cold carcase grading camera is implemented as a smartphone application that uses the phone camera and AI chip, using high resolution video and a 3D depth camera rather than a still image to measure carcase traits such as Aus Meat marble score, MSA marbling, fat colour, and eye muscle area. The same document notes the camera is used by accredited AUS MEAT graders. JBS Aussie Beef
On the connectivity side, MEQ’s camera page says scans can be done in chillers or on the line at chain speed, and that results flow into plant systems and MEQ Insights for reporting and integration into existing workflows. It also describes matching each scan to identifiers like hook ID or carcase ticket to create a traceable record. MEQ Solutions
MEQ Live: ultrasound plus machine learning for earlier signals
MEQ Live has been described in Australian industry coverage as using proprietary ultrasound scanning and machine learning to estimate traits like marbling and yield before slaughter, aimed at use in typical feedlot settings.
Standards and Approvals: The Credibility Checkpoints
Two external signals matter here: whether the tools are accepted inside Australian grading programs, and whether they meet recognised grading requirements overseas.
In Australia, Meat and Livestock Australia reported that JBS Australia became the first beef processor to receive Facility Objective Carcase Measurement device approval from AUS MEAT for MEQ’s cold carcase grading camera, and that three JBS sites were approved for camera grading under this collaboration.
In the United States, the USDA Agricultural Marketing Service announced approved instruments for beef grading in August 2025, listing MEQ Camera V2 technology among newly approved systems and stating the technologies are approved to predict marbling score, ribeye area, and yield grade related measures for USDA grading applications.
How It Compares with Similar Hardware and Connectivity Approaches
MEQ is entering a space where computer vision and sensor based grading is already an established direction, but implementations vary a lot.
Smartphone based grading: The USDA list includes a Global Meat Imaging system built around a Google Pixel 7a approved to predict official beef marbling score. That is a notably lightweight hardware approach compared with fixed industrial vision installations.
Fixed industrial vision systems in grading coolers: USDA also lists JBT, Marel, and E plus V VBG2000 variants. Marel describes AuraVBG as a rib eye evaluation system used after carcasses are cooled, designed for yield and quality grading. Separately, E plus V describes VBG 2000 as outputting marbling, rib eye area, fat thickness, yield grade, and meat and fat colour.
The practical IT difference is not just accuracy. It is deployment shape and data flow. Smartphone based capture can reduce installation friction, while fixed systems can be engineered tightly into chillers and line infrastructure. MEQ’s materials and the JBS fact sheet position its camera workflow as mobile capture plus integration into plant systems and reporting, which is closer to an edge device plus workflow software model than a single purpose machine.
What to Watch Next
From a neutral IT review lens, the open questions are less about whether AI can see marbling, and more about operational governance:
Model consistency across sites: multi site operations need calibration control, drift monitoring, and clear audit trails when graders override outputs. MEQ’s own product page points to tagging and overrides as part of keeping consistency across shifts and sites, which is a real world acknowledgement of this issue.
Standards alignment: approvals like AUS MEAT OCM device approval and USDA instrument approval create a pathway for wider adoption, but they also raise expectations around documentation, validation, and ongoing performance checks.
Interoperability: value increases when measurements plug into existing plant systems, supplier reporting, and downstream brand programs. That is a connectivity problem as much as an AI one.
Source: PR Newswire, USDA, Startup Daily, AMPC, MLA, Beef Central, Marel, JBS, MEQ
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