Startups

How CYBERTONGUE® Is Taking the Guesswork Out of Dairy Testing

The Canberra biotech startup is helping processors detect spoilage risks before milk reaches the shelf.

Updated

August 13, 2026 1:01 AM

CYBERTONGUE® Food Testing System, developed by PPB Technology. IMAGE: PPB Technology

Anyone who has opened a carton of milk before its expiry date only to find that it smells off or tastes bitter has experienced the problem firsthand. For dairy processors, the same issue can play out across thousands of litres. Milk may pass routine quality checks, make it through production and still spoil before reaching the end of its expected shelf life.  

CYBERTONGUE®, a biosensing platform, is designed to give dairy processors an earlier warning.  

Developed by Canberra-based biotech startup PPB Technology, the compact food testing system allows processors to analyse milk on-site and receive results within minutes. Its first commercial application detects protease, an enzyme linked to spoilage, shorter shelf life and production losses across dairy products.  

Dairy companies already test milk for fat, protein, bacteria, antibiotics and other indicators. Samples from individual farms may be sent to central laboratories, while tanker loads are tested again when they reach processing plants. These checks provide useful information, but they do not always show whether milk will remain stable in a UHT carton, meet a supermarket’s shelf-life requirements or produce the expected amount of cheese.  

Even after heat treatment, protease can continue affecting milk. For processors making UHT milk, fresh milk or mature cheese, that creates a costly blind spot.  

“Right now, dairy processors are working blind on this,” PPB Technology founder Stephen Trowell told Ventureport. “It’s becoming more of a problem as customers expect longer shelf lives and higher quality.”  

CYBERTONGUE® uses engineered biosensing proteins that emit blue and green light. The balance between the two colours changes when the protein reacts with a target substance. The reader measures that shift and calculates how much of the target is present.  

CYBERTONGUE® software displaying the results of a completed raw milk assay. IMAGE: PPB TECHNOLOGY

Trowell describes himself as a protein engineer at heart. He spent around three decades at CSIRO, Australia's national science and technology agency, and close to four decades in research before becoming a startup founder. The technology behind CYBERTONGUE® grew out of years of biological research, including earlier experiments involving wine aromas and explosives detection.

The PPB Technology team with founder Stephen Trowell. IMAGE: PPB TECHNOLOGY

PPB initially developed its technology to measure plasmin, an enzyme derived from cows. After feedback from the dairy industry, the company shifted its focus to bacterial proteases, which were identified as a more pressing spoilage problem. The platform can make some existing tests faster, but PPB chose to focus first on a measurement previously unavailable to processors.  

“Where we’ve chosen to lead is with a measurement nobody else can do at all,” he said. “The blue ocean, if you like.”  

That focused entry point shapes PPB’s wider strategy. CYBERTONGUE® could eventually test for lactose, allergens, microbial toxins and other targets. Yet launching everything at once would stretch a six-person company too far. Protease gives it a clear route into dairy, where the financial value is easier to demonstrate.  

A processor that detects high protease activity may redirect a batch of milk away from long-life products and use it for yoghurt, curd or another product where the enzyme causes less damage. PPB also says its tests can help trace problems in the milk supply and detect biofilms on processing equipment, allowing factories to take corrective cleaning measures.  

Fresh milk presents another sizeable opportunity, with the global market estimated at around US$81 billion in 2026 and projected to grow at 6.83% annually through 2035. At that scale, even small gains in shelf-life consistency could prevent substantial losses. Supermarkets often require milk to have a minimum amount of shelf life remaining on delivery. Products that fall short may be rejected, discounted or discarded at the processor’s expense. While Australian processors typically aim for a shelf life of 19 to 21 days, they do not always achieve it. CYBERTONGUE® could help them reach that target more consistently.  

Cheese producers face a related issue. Protease can create bitter flavours in hard cheeses and reduce production yield. Cheesemakers add another enzyme, chymosin, during production, often without knowing how much protease activity is already present in the milk. Better information could help them adjust the process and get more value from the same raw material.  

PPB sells the reader as laboratory equipment, but its long-term business model centres on repeat testing. Customers buy the machine, then purchase a PPB reagent cartridge each time they run an assay. Future tests are intended to work on the same reader, making the system more useful as the catalogue expands.  

CYBERTONGUE®’s compact design reflects PPB’s focus on practical onsite testing. The device is small enough to fit into two cupped hands and comes with a tablet running PPB’s software.  

An operator inserts a sample into a CYBERTONGUE® testing device during an on-site milk analysis. PHOTO: PPB TECHNOLOGY

At the time of the interview, 13 to 15 systems had been installed with paying customers. PPB had made sales in 11 countries across five continents, with Europe as its largest market.  

Trowell describes PPB as “born global”. While Australia and New Zealand have sophisticated dairy industries, Trowell thinks CYBERTONGUE® may offer even more value in regions where milk quality is less consistent, transport routes are longer or refrigeration is harder to maintain.  

Trowell also believes PPB has a strong competitive moat, with the combination of manufacturing both the hardware and consumable reagents. Patents form one layer, but he does not consider legal protection sufficient on its own. A competitor would need expertise in protein engineering, reagent production, optical hardware, software and dairy processing.  

Calibration adds another barrier. For each new box of reagents, PPB securely uploads the required calibration data to the customer’s machine. A third party could not simply manufacture a cartridge with the same dimensions and expect it to work correctly.  

“We own the whole stack,” Trowell said, referring to everything from the sensing molecule to the hardware, software and cloud platform.  

That said, he accepts that no technology is impossible to copy. His view is that PPB’s mix of scientific knowledge, integrated system and experience in dairy gives it enough of a head start to build a meaningful position before competitors catch up.  

The technology could eventually reach beyond food. Trowell sees possible uses in environmental testing, veterinary care and medical diagnostics, including the analysis of blood, serum or saliva. PPB has already discussed potential applications with organisations outside the food industry.  

For now, however, moving directly into clinical diagnostics would create more risk than opportunity. Medical products can require years of trials, extensive regulatory work and large development budgets. PPB would also need to build new sales channels and choose between many possible clinical uses without knowing which one would attract the strongest demand.  

For these reasons, food testing offers PPB a clearer path. The regulatory barriers are generally lower, customers have immediate operational problems, and PPB has already built relationships in dairy. Once the platform has proved itself commercially, the company could enter other sectors through partnerships or licensing.  

Trowell has held that view for years. The immediate goal is to build a successful food testing company rather than chase the prestige often attached to medical biotechnology.  

The opportunity may also expand as food production becomes more complex. Dairy factories increasingly process oat, soy and other alternatives on equipment that also handles cow’s milk. This creates cross-contamination and allergen risks, offering another potential use for CYBERTONGUE®.  

PPB still has plenty to prove. It must secure repeat reagent orders, convert early trials into wider deployments and expand its test catalogue without compromising accuracy. Scaling an integrated biotech hardware business across international markets will not be simple.  

Still, its approach offers a useful lesson for other deep-tech startups. Instead of beginning with every possible application, PPB picked a specific problem that costs customers money today. It built a product around that pain point, then used the same technology as the foundation for a broader platform.  

As Trowell put it, customers “need it fast, and they need it right.” CYBERTONGUE® is betting that food testing can deliver both.  

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Artificial Intelligence

Are Workplace Chats Becoming the Next Layer of AI Memory?

As workplace knowledge spreads across chats, AI firms are building systems that can structure, retrieve and preserve it over time.

Updated

August 10, 2026 5:56 PM

A messaging app on a phone. PHOTO: ADOBE STOCK

Votee AI, an enterprise AI company headquartered in Hong Kong, has partnered with its Toronto-based research lab Beever AI to launch Beever Atlas. The new platform is designed to turn workplace chats into searchable knowledge that AI systems can retrieve and understand.

The release focuses on a growing issue inside organisations. Much of today’s workplace knowledge now exists inside chat platforms such as Slack, Microsoft Teams, Discord and Telegram. Important discussions, project decisions and technical information often disappear into long message histories that are difficult to search later.

Beever AI developed the platform to organise those conversations into a structured system for AI assistants. The software connects with Telegram, Discord, Mattermost, Microsoft Teams and Slack, then converts conversations into linked records of people, projects, files and decisions.

The collaboration combines Votee AI’s enterprise infrastructure work with Beever AI’s research around AI memory systems. The companies are releasing two versions of the product. The open-source edition is aimed at individual developers, researchers and creators. The enterprise edition is designed for banks, government agencies and larger organisations with stricter security requirements.

The release also reflects a broader shift happening across the AI industry. Companies are increasingly looking at how AI systems store and retrieve long-term knowledge, rather than relying solely on large context windows or search-based retrieval.

Earlier this year, OpenAI founding member and former director of AI at Tesla  Andrej Karpathy discussed the growing need for what he described as “LLM Knowledge Bases.” He argued that AI systems need structured and evolving memory rather than depending only on context windows and vector search.

Beever Atlas approaches that problem through workplace communication. Instead of focusing mainly on uploaded files, the system is designed around conversations that happen daily across team chat platforms. It can also process images, PDFs, voice notes and video files within the same searchable system.

The companies say the software is designed to work directly with AI assistants and coding tools such as Cursor, AWS Kiro and Qwen Code. Integrations for OpenClaw and Hermes Agent are expected later in 2026.

Pak-Sun Ting, Co-Founder and CEO of Votee AI  said: "Hong Kong has always been known for property and finance. Beever Atlas is proof that world-class AI infrastructure can emerge from an HK-headquartered company and be shared openly with the world. Every growing organization faces the same silent liability: conversational knowledge loss. Beever Atlas turns this perishable resource into a compounding organizational asset."

A large part of the enterprise version focuses on privacy and access control. The system mirrors permissions from Slack and Microsoft Teams so users can only retrieve information they are already authorised to access. Permission updates are reflected automatically when access changes inside company systems.

The enterprise edition also includes audit logs, encryption controls and data retention settings for organisations handling sensitive internal data. Companies can run the software entirely inside their own infrastructure using Docker and connect it to their preferred AI models through LiteLLM.

The companies argue that organising information is more useful than simply storing chat archives. Jacky Chan Co-Founder and CTO of Votee AI said: "The key technical decision was to treat agent memory as a knowledge engineering problem, not a retrieval problem. Structure beats similarity — a typed graph of who works on what is more useful to an AI than vector search over a Slack archive."

The software also includes protections against prompt injection attacks and systems designed to reduce hallucinated responses. According to the companies, the AI is designed to return “I don't know” with citations when confidence is low instead of generating unsupported answers.

As workplace communication becomes increasingly fragmented across chat platforms, companies are beginning to treat internal conversations as information that AI systems can organise, retrieve and build on. Beever Atlas reflects a broader push to turn everyday workplace communication into long-term organisational memory.